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 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tens... | 210 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
from math import sqrt
def _lowercase ( UpperCamelCase_ ) -> bool:
'''simple docstring'''
assert isinstance(UpperCamelCase_ , UpperCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
SCREAMING_SNAKE_CASE__ = True
... | 472 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def snake_case__ ( _lowerCamelCase, _lowerCamelCase, _lowerCamelCase ) ->list[int]:
"""simple docstring"""
__lowercase : int = [0] * no_of_processes
__lowercase : Optio... | 575 |
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 _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Dict = logging.get_logger(__name__)
_lowerCAmelCase : int = {
'''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
# ... | 454 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
"""simple docstring"""
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 ( __lowercase ... | 178 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
fro... | 501 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataL... | 632 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
def UpperCAmelCase_ ( __lowerCAmelCase ) -> "list[int]":
if upper_limit < 0:
raise ValueError('''Limit for the Catalan sequence must be ≥ 0''' )
__lowercase : List[Any] = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__lowercase : ... | 509 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
"""simple docstring"""
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
lowercase_ = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from ... | 470 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
'''simple docstring'''
from itertools import product
def UpperCamelCase ( _lowerCamelCase : int , _lowerCamelCase : int ):
A__ = sides_number
A__ = max_face_number * dice_number
A__ = [0] * (max_total + 1)
A__ = 1
A__ ... | 440 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
UpperCamelCase = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
UpperCamelCase = requests.get(... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 0 |
'''simple docstring'''
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_lowercase : Dict = logging.get_logger(__name__)
def lowerCamelCase__ ( A : List[str] ):
'''simple ... | 210 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__snake_case = Mapping[str, np.ndarray]
__snake_case = Mapping[str, Any] # Is a nested dict.
__snake_case ... | 472 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 0 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__A : Optional[Any] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst... | 575 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
def __snake_case ( _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : int ) -> int:
def count_of_possible_combinations(_lowerCAmelCase : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
... | 454 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
__snake_case = get_tests_dir... | 178 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 0 |
'''simple docstring'''
import os
def __snake_case ():
"""simple docstring"""
lowerCamelCase_ : int = os.path.join(os.path.dirname(__UpperCAmelCase ) , '''num.txt''' )
with open(__UpperCAmelCase ) as file_hand:
return str(sum(int(__UpperCAmelCase ) for ... | 501 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Ac... | 632 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 0 |
from __future__ import annotations
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
__lowercase : List[Any] = get_failure_array(__lowerCAmelCase )
# 2) Step through text searching for pattern
__lowercase , __lowerc... | 509 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, lo... | 470 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 0 |
'''simple docstring'''
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tes... | 440 |
# Copyright 2023 The HuggingFace Inc. 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 ap... | 699 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMix... | 61 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( A : int ):
'''simple docstring'''
UpperCAmelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))
| 210 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
from math import isqrt
def _lowercase ( UpperCamelCase_ ) -> bool:
'''simple docstring'''
return all(number % divisor != 0 for divisor in range(2 , isqrt(UpperCamelCase_ ) + 1 ) )
def _lowercase ( UpperCamelCase_ = 10**6 ) -> ... | 472 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
"""simple docstring"""
import math
import os
import sys
def snake_case__ ( _lowerCamelCase ) ->str:
"""simple docstring"""
__lowercase : Any = ""
try:
with open(_lowerCamelCase, "rb" ) as binary_file:
__lowercase : Tuple = ... | 575 |
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 _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
# Copyright 2023 The HuggingFace Inc. 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 b... | 454 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __lowerCAmelCase ( ) -> None:
"""simple docstring"""
print("Making key files..." )
make_key_f... | 178 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
'''simple docstring'''
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
__lowerCamelCase : Dict = HfApi()
__lowerCamelCase : str = {}
# fmt: off
__lowerCamelCase : List[str] = torch.tensor([
-0.7_5_1_5,... | 501 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def lowerCAmelCase__ ( UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Union[str, Any] , UpperCamelCase_ : Dict , UpperCamelCase_ : Dict )-> Dict:
... | 632 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegm... | 509 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
"""simple docstring"""
def A_ ( lowercase ) -> list:
"""simple docstring"""
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
UpperCAmelCase_ : Union[str, Any] = gray_... | 470 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
'''simple docstring'''
import operator as op
__lowerCAmelCase : List[str] ="scaler.pt"
__lowerCAmelCase : int ="pytorch_model"
__lowerCAmelCase : Tuple ="random_states"
__lowerCAmelCase : Dict ="optimizer"
__lowerCAmelCase : Tu... | 440 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
# Copyright 2021 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 req... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 0 |
'''simple docstring'''
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def lowerCamelCase__ ( ):
'''simple docstring'''
with offline(OfflineSimul... | 210 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 0 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__snake_ca... | 472 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils impo... | 575 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
import datasets
_lowerCAmelCase : Optional[Any] = '''\\n@InProceedings{conneau2018xnli,\n author = \"Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n a... | 454 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class _lowerCAmelCase :
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
if len(UpperCamelCase__ )... | 178 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 0 |
'''simple docstring'''
from math import isqrt
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase_ : Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __Upper... | 501 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
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... | 632 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def UpperCAmelCase_ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase=5 ) -> List[Any]:
# Adapted from https://github.com/pytorch/fairseq/blob/master... | 509 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "Fu... | 470 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, pre... | 440 |
# Copyright 2023 The HuggingFace Inc. 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 ap... | 699 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}... | 61 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ... | 210 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
from typing import List
import datasets
from datasets.tasks import AudioClassification
from ..folder_based_builder import folder_based_builder
__snake_case = datasets.utils.logging.get_logger(__name__)
class lowercase__ ( folder_based_builder.FolderBasedBuilderConfig ):
A__ : bool ... | 472 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def snake_case__ ... | 575 |
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 _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_availabl... | 454 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
"""simple docstring"""
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, requir... | 178 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr impor... | 501 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
_lowercase = logging.get_logger(__name__)
_lowercase = ... | 632 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__lowerCAmelCase : int = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang... | 509 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = ... | 470 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__lowerCAmelCase : Any =logging.get_logger(__name__)
__lowerCAmelCase : str ={
"Visual-Attention-Network/van-base": (
"https://huggingface.co/Visual-Atten... | 440 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
def _A ( lowerCAmelCase_ : str , lowerCAmelCase_ : str ):
"""simple docstring"""
if not (isinstance(lowerCAmelCase_ , lowerCAmelCase_ ) and isinstance(lowerCAmelCase_ , lowerCAmelCase_ )):
raise ValueError("longest_common_substring() takes tw... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__( metaclass=__lowercase ):
__magic_name__ : Any = ['''torch''', '''scipy''']
def __init__( self : int , *lowerCAmelCase : str , **lowerCAmelCase... | 210 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Dist... | 472 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__A : Dict = {
'configuration_blip': [
'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 575 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : List[str] = {
'''configuration_distilbert''': [
'''... | 454 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : int ) -> Optional[int]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowercase , int(b / 2 ) ) * actual_power(lowercase , ... | 178 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 0 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block,... | 501 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
_lowercase = logging.get_logger(__name__)
_lowercase = R"\n Args:\n input_ids (`torch.LongTensor` of shape `(batch_size, sequ... | 632 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 0 |
from __future__ import annotations
from random import random
class __lowerCAmelCase :
"""simple docstring"""
def __init__( self : List[Any] , _snake_case : int | None = None ):
__lowercase : List[str] = v... | 509 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 0 |
"""simple docstring"""
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Tuple )-> str:
"""simple docstring"""
UpperCAmelCase_ : Optional[int] = {}
def a ( self : List[Any] )-> Any:
"""simple do... | 470 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testi... | 440 |
# Copyright 2023 The HuggingFace Inc. 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 ap... | 699 | 0 |
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if length <= 0 or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(lowerCAmelCase_ )]
if... | 61 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 0 |
'''simple docstring'''
def lowerCamelCase__ ( A : str , A : list[str] ):
'''simple docstring'''
UpperCAmelCase = ''''''
for word_or_phrase in separated:
if not isinstance(A , A ):
raise Exception('''join() accepts only strings to... | 210 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to... | 472 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def snake_case__ ... | 575 |
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 _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
# Copyright 2022 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 app... | 454 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def __lowerCAmelCase ( lowercase : Callable[[int | float], int | float] , lowercase : int | float , lowercase : int | float , lowercase : int = 100 , ) ... | 178 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
'''simple docstring'''
from timeit import timeit
def __snake_case (__UpperCAmelCase ):
"""simple docstring"""
if number < 0:
raise ValueError('''the value of input must not be negative''' )
lowerCamelCase_ : Optional[int] = 0
while number:
number &= number - 1
... | 501 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
from collections.abc import Sequence
from queue import Queue
class _UpperCAmelCase :
def __init__( self , a__ , a__ , a__ , a__=None , a__=None):
A__ = start
A__ = end
A__ = val
A__ = (star... | 632 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowerCAmelCase ( __... | 509 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
"""simple docstring"""
def A_ ( lowercase = 1000 ) -> int:
"""simple docstring"""
UpperCAmelCase_ ,UpperCAmelCase_ : Optional[Any] = 1, 1
UpperCAmelCase_ : List[Any] = 2
while True:
UpperCAmelCase_ : U... | 470 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
'''simple docstring'''
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class UpperCAmelCase ( pl.LightningModule ):
def __init__( self :List[str] , lowercase_ :str )-> ... | 440 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __lowerCamelCase ( __lowercase , __lower... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_lowercase : int ... | 210 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 0 |
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__snake_case = logging.get_logger(__name__)
class lowercase__ ( __lowercase ):
def __init__( self : Optional[int] , *UpperCAmelCase_ : List[str] , **UpperCAmelCase... | 472 |
def __UpperCamelCase (lowerCAmelCase : list[int] ) -> int:
if not numbers:
return 0
if not isinstance(lowerCAmelCase, (list, tuple) ) or not all(
isinstance(lowerCAmelCase, lowerCAmelCase ) for number in numbers ):
raise ValueError('numbers m... | 699 | 0 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__A : List[str] = False
class lowerCAmelCase__ ( unittest.TestCase ... | 575 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 699 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCAmelCase : List[Any] = {
'''configuration_swiftformer''': [
'''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''SwiftFormerConf... | 454 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at https://huggin... | 699 | 0 |
"""simple docstring"""
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_singl... | 178 |
import sys
def __UpperCamelCase (lowerCAmelCase : Dict ) -> Dict:
A = len(lowerCAmelCase )
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
A = [[0 for x in range(lowerCAmelCase )] for x in range(lowerCAmelCase )]
for chai... | 699 | 0 |
'''simple docstring'''
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase__ ( __lowercase ... | 501 |
from math import isqrt
def __UpperCamelCase (lowerCAmelCase : int ) -> bool:
return all(number % divisor != 0 for divisor in range(2, isqrt(lowerCAmelCase ) + 1 ) )
def __UpperCamelCase (lowerCAmelCase : int = 10**6 ) -> int:
A = 0
A = 1
... | 699 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( UpperCamelCase_ : dict , UpperCamelCase_ : str )-> set[str]:
A__ , A__ = set(UpperCamelCase_ ), [start]
while stack:
A__ = stack.pop()
explored.add(Upper... | 632 |
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
_UpperCAmelCase = logging.get_logger(__name__)
class _UpperCAmelCase ( __lowercase ):
'''simple docstring'''
def __init__( self : List[str] , *UpperCame... | 699 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
impo... | 509 |
from typing import Any, Dict, Optional
import torch
import torch.nn.functional as F
from torch import nn
from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings
@maybe_allow_in_graph
class ... | 699 | 0 |
"""simple docstring"""
import sys
def A_ ( lowercase ) -> Dict:
"""simple docstring"""
UpperCAmelCase_ : Dict = len(lowercase )
UpperCAmelCase_ : Dict = [[0 for x in range(lowercase )] for x in range(lowercase )]
U... | 470 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "to... | 699 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCamelCase ( _lowerCamelCase : str , _lowerCamelCase : dict ):
A__ = BeautifulSoup(requests.get(_lowerCamelCase , params=_lowerCamelCase ).content , "html.parser" )
A__ = ... | 440 |
# Copyright 2023 The HuggingFace Inc. 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 ap... | 699 | 0 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __lowerCamelCase ( yaml.SafeLoader ):
"""simple docstring"""
def a ( self : Tuple , SCREAMING_SNAKE_CASE__ : Optional[int] ) -> int:... | 61 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
def __UpperCamelCase (lowerCAmelCase : List[str] ) -> Dict:
A = r'... | 699 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 210 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class _UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[Any] , UpperCamelCase__ : Collection[float] | None = None ):
if... | 699 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = """▁"""
__snake_case ... | 472 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.uti... | 699 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
cl... | 575 |
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 _UpperCAmelCase ( __lowercase ):
'''simple docstring... | 699 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_... | 454 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> str:
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 699 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configu... | 178 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class _UpperCAmelCase ( __lowercase , __lowercase ):
... | 699 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Union[st... | 501 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase = 0b10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_... | 699 | 0 |
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def lowerCAmelCase__ ( U... | 632 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
"post_extract_proj": "feature_projecti... | 699 | 0 |
import numpy as np
import qiskit
def UpperCAmelCase_ ( __lowerCAmelCase = 8 , __lowerCAmelCase = None ) -> str:
__lowercase : Union[str, Any] = np.random.default_rng(seed=__lowerCAmelCase )
# Roughly 25% of the qubits will contribute to the key... | 509 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"shi-labs/nat-mi... | 470 |
from __future__ import annotations
import math
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int, lowerCAmelCase : bool, lowerCAmelCase : list[int], lowerCAmelCase : float ) -> int:
if depth < 0:
raise ValueError('Depth can... | 699 | 0 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust... | 440 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceCl... | 699 | 0 |
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
UpperCam... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase = {"configuration_xglm": ["XGLM_PRETRAINED_C... | 699 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 210 |
def __UpperCamelCase (lowerCAmelCase : int, lowerCAmelCase : int ) -> Optional[int]:
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCAmelCase, int(b / 2 ) ) * actual_power(lowerCAmelCase, int(b / 2 ) )
else:
r... | 699 | 0 |
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