code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = get_tests_dir('''fixtures/test_sentencepiece_with_bytefallba... | 9 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 | 1 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def _a ( self : str ):
"""simple docstring"""
A__ = get_ac... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
SCREAMING_SNAKE_CASE__ = '''src/diffusers'''
# Matches is_xxx_available()
SCREAMING_SNAKE_CASE__ = re.compile(r'''is\_([a-z_]... | 9 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 9 | 1 |
import numpy as np
import datasets
SCREAMING_SNAKE_CASE__ = '''
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. ... | 9 |
from __future__ import annotations
from typing import Any
def A ( __UpperCamelCase ) -> int:
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = []
for token in postfix_notation:
if token in operations:
A__ , A__ = stack.p... | 9 | 1 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( __UpperCamelCase ) -> Op... | 9 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def A ( ) -> Union[str, Any]:
A__ = HfArgumentParser(__UpperCamelCase )
A__ = parser.parse_args_into_dataclasses()[0]
A__ = TensorFlowBenchmark(args=__UpperCamelCase )
... | 9 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A ( __UpperCamelCase , __UpperCamelCase=7 ) -> Optional[int]:
A__ = None
if token is not None:
A__ = {'Accept': 'application/vnd.github+json',... | 9 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
SCREAMING_SNAKE_CASE__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 1 |
import math
def A ( __UpperCamelCase , __UpperCamelCase ) -> float:
if initial_intensity < 0:
raise ValueError('The value of intensity cannot be negative' )
# handling of negative values of initial intensity
if angle < 0 or angle > 360:
raise ValueError('I... | 9 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 | 1 |
# flake8: noqa
# Lint as: python3
SCREAMING_SNAKE_CASE__ = [
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progre... | 9 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 | 1 |
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = 0
A__ = len(__UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collec... | 9 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__ = Lock()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ... | 9 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *_snake_cas... | 9 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
SCREAMING_SNAKE_CASE__ = logging.getLogger(__name__)
class __lowerCAmelCase :
"""simple docstring"""
def __init__( s... | 9 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 9 | 1 |
def A ( __UpperCamelCase = 4_000_000 ) -> int:
A__ = [0, 1]
A__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
A__ = 0
for j in range(len(__UpperCamelCase ) - 1 ):
if fib[... | 9 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 9 | 1 |
def A ( __UpperCamelCase ) -> tuple[int, int]:
try:
A__ = float(__UpperCamelCase )
except ValueError:
raise ValueError('Please enter a valid number' )
A__ = decimal - int(__UpperCamelCase )
if fractional_part == 0:
return int(__UpperCam... | 9 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 9 | 1 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput... | 9 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 9 | 1 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __lowerCAmelCase ( UpperCAmelCase_ , unittest.TestCase ):
... | 9 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = '''docs/source/en/_toctree.yml'''
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = defaultdict(__UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
A__ ... | 9 | 1 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''kakaobrain/align-base''': '''... | 9 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 9 | 1 |
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def A ( __UpperCamelCase ) -> Any:
for param in module.parameters():
A__ = False
def A ( ) -> str:
A__ = 'cuda' if torch.cuda.is_available() else 'cpu'
if torch.backends.mps.i... | 9 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( __UpperCamelCase ) -> Op... | 9 | 1 |
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.... | 9 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 9 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , _snak... | 9 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 | 1 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreT... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 9 | 1 |
from collections import defaultdict
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
A__ = first_str.lower().strip()
A__ = second_str.lower().strip()
# Remove whitespace
A__ = first_str.replace(' ' , '' )
A__ = second_str.replac... | 9 |
from __future__ import annotations
from typing import Any
def A ( __UpperCamelCase ) -> int:
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = []
for token in postfix_notation:
if token in operations:
A__ , A__ = stack.p... | 9 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''SCUT-DLVCLab/lilt-roberta-en-base''': (
'''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 9 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:... | 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 1 |
import math
def A ( __UpperCamelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number ... | 9 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 | 1 |
from __future__ import annotations
def A ( __UpperCamelCase , __UpperCamelCase ) -> list[str]:
if partitions <= 0:
raise ValueError('partitions must be a positive number!' )
if partitions > number_of_bytes:
raise ValueError('partitions can not > number_of_bytes!'... | 9 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 | 1 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(__UpperCamelCase ) - ngram_size + 1 )]
if __name__ == "__main__":
from doctest import testmod
testmod()
| 9 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 1 |
import warnings
from .generation import TFGenerationMixin
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
warnings.warn(
"Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will "
"be removed in Transformers v5... | 9 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 | 1 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCAmelCase ( UpperCAmelCase_ ):
""... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = 0
A__ = len(__UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collec... | 9 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.... | 9 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *_snake_cas... | 9 | 1 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __lowerCAmelCase :
"""simple docstring"""
def _a ( self : Dict , _snake_case : List[str] ):
... | 9 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 9 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_r... | 9 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 9 | 1 |
from __future__ import annotations
import math
def A ( __UpperCamelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
r... | 9 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 9 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType... | 9 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 9 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 9 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = '''docs/source/en/_toctree.yml'''
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = defaultdict(__UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
A__ ... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
'''tokenization_luke''': ['''LukeTokenizer'''],
}
try:
if... | 9 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 9 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 9 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( __UpperCamelCase ) -> Op... | 9 | 1 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = get_tests_dir('''fixtur... | 9 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 9 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Reg... | 9 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 | 1 |
from collections import defaultdict
def A ( __UpperCamelCase ) -> int:
A__ = 1
A__ = True
for v in tree[start]:
if v not in visited:
ret += dfs(__UpperCamelCase )
if ret % 2 == 0:
cuts.append(__UpperCamelCase )
return ret
def A (... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, requi... | 9 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''DeiTOnnxC... | 9 |
from __future__ import annotations
from typing import Any
def A ( __UpperCamelCase ) -> int:
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = []
for token in postfix_notation:
if token in operations:
A__ , A__ = stack.p... | 9 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils impo... | 9 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> Tuple:
A__ = s.rsplit(__UpperCamelCase , __UpperCamelCase ... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = ''''''
SCREAMING_SNAKE_CASE__ = 1 # (0 is vertical, 1 is horizontal)
def A ( ) -> None:
... | 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 1 |
import pprint
import requests
SCREAMING_SNAKE_CASE__ = '''https://zenquotes.io/api'''
def A ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/today' ).json()
def A ( ) -> list:
return requests.get(API_ENDPOINT_URL + '/random' ).json()
if __name__ == "... | 9 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 | 1 |
def A ( ) -> int:
return [
a * b * (1_000 - a - b)
for a in range(1 , 999 )
for b in range(__UpperCamelCase , 999 )
if (a * a + b * b == (1_000 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(f'{solution() = }')
| 9 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
cla... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 1 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
SCREAMING_SNAKE_CASE__ = ['''small''', '''medium''', '''large''']
SCREAMING_SNAKE_CASE__ = '''lm_head.decoder.weight'''
SCREAMING_SNAKE_CASE__ = '''lm_head.weight'''
def A ( __UpperCamelCase , ... | 9 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase_ )
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : str ... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = 0
A__ = len(__UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collec... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE__ = {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}
tr... | 9 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *_snake_cas... | 9 | 1 |
from manim import *
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A__ = Rectangle(height=0.5 , width=0.5 )
A__ = Rectangle(hei... | 9 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 9 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
... | 9 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 9 | 1 |
from __future__ import annotations
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ) -> tuple[str, float]:
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif stress <... | 9 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 9 | 1 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 9 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BertTokenizer, BlipImageP... | 9 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = '''docs/source/en/_toctree.yml'''
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = defaultdict(__UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
A__ ... | 9 | 1 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE__ = importlib.util.find_spec('''s3fs''') is not None
if _has_safs:
from .safilesystem impo... | 9 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 9 | 1 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'''vocab_file''': '''vocab.txt'''}... | 9 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( __UpperCamelCase ) -> Op... | 9 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
SCREAMING_SNAKE_CASE__ = '''
@inproceedings{xu-etal-2016-optimizing,
title = {Optimizing Statistical Machine Translation for Text Simplification},
authors={Xu, ... | 9 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 9 | 1 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
A__ : Optional[Any] = "Speech2TextFeatureExtractor"
A__ : Optional[Any] = "S... | 9 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 | 1 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 9 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 9 | 1 |
from math import factorial, radians
def A ( __UpperCamelCase , __UpperCamelCase = 18 , __UpperCamelCase = 10 ) -> float:
A__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
A__ = radians(__UpperCamelCase ... | 9 |
from __future__ import annotations
from typing import Any
def A ( __UpperCamelCase ) -> int:
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = []
for token in postfix_notation:
if token in operations:
A__ , A__ = stack.p... | 9 | 1 |
import heapq as hq
import math
from collections.abc import Iterator
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : Tuple ):
"""simple docstring"""
A__ = str(id_ )
... | 9 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
# See all CANINE models at https://... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
def A ( __UpperCamelCase ) -> float:
return 10 - x * x
def A ( __UpperCamelCase , __UpperCamelCase ) -> float:
# Bolzano theory in order to find if there is a root between a and b
if equation(__UpperCamelCase ) * equation(__UpperCamelCase ) >= 0:... | 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 1 |
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : str , _snake_case : List[Any... | 9 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 | 1 |
from __future__ import annotations
def A ( __UpperCamelCase , __UpperCamelCase ) -> set[str]:
A__ , A__ = set(__UpperCamelCase ), [start]
while stack:
A__ = stack.pop()
explored.add(__UpperCamelCase )
# Differences from BFS:
# 1)... | 9 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 | 1 |
from math import ceil
def A ( __UpperCamelCase = 1_001 ) -> int:
A__ = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
A__ = 2 * i + 1
A__ = 2 * i
A__ = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__mai... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logg... | 9 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE__ = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
... | 9 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
A__ = 0
A__ = len(__UpperCamelCase ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collec... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_... | 9 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *_snake_cas... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_gpt_bigcode''': ['''GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTBigCodeConfig'''],
}
try:
if not is_torch_a... | 9 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
SCREAMING_SNAKE_CASE__ = np.linspace(start=0, stop=7_5, num=7_5, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
... | 9 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/ma... | 9 |
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 9 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
SCREAMING_SNAKE_CASE__ = {
'''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}
try:
if ... | 9 |
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Opti... | 9 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[str] , _snake_case : Optional[int] , _snake_case : ... | 9 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 9 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResamp... | 9 |
import argparse
from collections import defaultdict
import yaml
SCREAMING_SNAKE_CASE__ = '''docs/source/en/_toctree.yml'''
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = defaultdict(__UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] += 1
A__ ... | 9 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {'''v... | 9 |
import inspect
import unittest
from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_comm... | 9 | 1 |
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamel... | 9 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def A ( __UpperCamelCase ) -> Op... | 9 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
'''CarlCochet/trajectory-transformer-halfcheetah-medium-v2''': (
'''https://huggingface.co/CarlCochet/trajectory-transforme... | 9 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ..... | 9 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[str] , ... | 9 |
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
A__ = OmegaConf.load(__UpperCamelCase )
A__ = torch.load(__Uppe... | 9 | 1 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A ( __UpperCamelCase ) -> Optional[Any]:
A__ = [
'encoder.version',
'decoder.version',
'model.encoder.version',
'model.decoder.vers... | 9 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def A ( __UpperCamelCase ... | 9 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 9 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE__ = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE__ = [ord(letter) for letter in string.ascii_lowercase]
SC... | 9 |
from __future__ import annotations
from typing import Any
def A ( __UpperCamelCase ) -> int:
if not postfix_notation:
return 0
A__ = {'+', '-', '*', '/'}
A__ = []
for token in postfix_notation:
if token in operations:
A__ , A__ = stack.p... | 9 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...t... | 9 |
from __future__ import annotations
def A ( __UpperCamelCase = 4 ) -> list[list[int]]:
A__ = abs(__UpperCamelCase ) or 4
return [[1 + x + y * row_size for x in range(__UpperCamelCase )] for y in range(__UpperCamelCase )]
def A ( __UpperCamelCase ) ... | 9 | 1 |
import pickle
import numpy as np
from matplotlib import pyplot as plt
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : int , _snake_case : Optional[Any] , _snake_case : Tuple , _snake_case : int ... | 9 |
from __future__ import annotations
from fractions import Fraction
def A ( __UpperCamelCase , __UpperCamelCase ) -> bool:
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def A ( __UpperCamelCase ) -> list[str]:... | 9 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.model... | 9 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by appli... | 9 | 1 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map_nested,
t... | 9 |
SCREAMING_SNAKE_CASE__ = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
SCREAMING_SNAKE_CASE__ = ... | 9 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
@staticmethod
@abstractmethod
def _a ( _snake_case : ArgumentParser ):
"""simple docstring"""
... | 9 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pi... | 9 | 1 |
import os
import string
import sys
SCREAMING_SNAKE_CASE__ = 1 << 8
SCREAMING_SNAKE_CASE__ = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
'''l... | 9 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 1 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaFo... | 9 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResamplin... | 9 | 1 |
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : Dict , *... | 9 |
SCREAMING_SNAKE_CASE__ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def A ( __UpperCamelCase , ... | 9 | 1 |
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