code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Autoenc... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
"""simple docstring"""
import sys
SCREAMING_SNAKE_CASE_ : List[Any] = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295... | 335 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" ... | 335 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_ut... | 335 |
"""simple docstring"""
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, ImageClassifierOutputWithNoAttenti... | 335 | 1 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config',... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 1 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokeniz... | 335 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 | 1 |
"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_ou... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: List[Any] ):
"""simple docstring"""
A__ = val
A__ = None
A__ = No... | 335 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author =... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ : Any = {
'... | 335 |
"""simple docstring"""
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_devi... | 335 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .va... | 335 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
... | 335 | 1 |
"""simple docstring"""
# 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... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
"""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
SCREAMING_SNAKE_CASE_ : Union[str, Any] = False
class a ( ... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
"""simple docstring"""
def UpperCamelCase ( self: List[Any] , UpperCamelCase: Any ):
... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
t... | 335 | 1 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class a ( _lowerCamelCase ):
"""simple docstring"""
@require_torch
def ... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
"""simple docstring"""
class a : # Public class to implement a graph
"""simple docstring"""
def __init__( self: List[str] , UpperCamelCase: int , UpperCamelCase: int , UpperCamelCase: list[list[bool]] ):
"""simple docstri... | 335 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch... | 335 | 1 |
"""simple docstring"""
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
... | 335 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of ini... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : str = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
... | 335 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor... | 335 | 1 |
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class a ( _lowerCamelCase, _lowerCamelCase ):
"""simple docstring"""
@register_to_config
def __init__( ... | 335 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / ""... | 335 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : List[Any] = {
'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/u... | 335 |
"""simple docstring"""
# 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... | 335 | 1 |
"""simple docstring"""
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 GradientStat... | 335 |
"""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 ImageProcessingSavingT... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
A__ = str(bin(UpperCAmelCase_ ) )
binary_numbe... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 | 1 |
"""simple docstring"""
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextCo... | 335 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple doc... | 335 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
"""simple docstring"""
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHT... | 335 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" ... | 335 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
SCREAMING_SNAKE_CASE_ : List[str] = {
'google/tapas-base-finetuned-sqa': (
'https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'
),
'google/tapas-base-finetuned-... | 335 |
"""simple docstring"""
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, ImageClassifierOutputWithNoAttenti... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE_ : str = 8.9_8_8E9 # units = N * m^s * C^-2
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float , UpperCAmelCase_ : float , Upp... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _snake_case ( UpperCAmelCase_ : list[int] , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ):
A__ = [0] * no_of_processes
A__ = ... | 335 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
t... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[Any] = loggin... | 335 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author =... | 335 | 1 |
"""simple docstring"""
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 a ( ... | 335 |
"""simple docstring"""
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_devi... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
class a :
"""simple docstring"""
def __init__( self: List[str] , UpperCamelCase: int ):
"""simple docstring"""
A__ = size
# a... | 335 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 200 ):
A__ = [1, 2, 5, 10, 20, 50, 100, 200]
A__ = [0] * (pence + 1)
A__ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(UpperCAme... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversa... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
t... | 335 | 1 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _snake_case ( ):
A__ = ArgumentParser(
description=(
"""P... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE_ : Optional[int] = Mapping[str, np.ndarray]
SCREAMING_SNAKE... | 335 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
SCREAMING_SNAKE_CASE_ : Optional[int] = '2020.9.26'
SCREAMING_SNAKE_CASE_ : Optional[int] = 'xcodz-dot, cclaus, dhruvmanila'
def _snake_case ( UpperCAmelCase_ : float ,... | 335 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of ini... | 335 | 1 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class a ( unittest.TestCase ):
... | 335 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor... | 335 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 0 # The first color of the flag.
SCREAMING_SNAKE_CASE_ : int = 1 # The second color of the flag.
SCREAMING_SNAKE_CASE_ : Dict = 2 # The third color of the flag.
SCREAMING_SNAKE_CASE_ : Tuple ... | 335 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / ""... | 335 | 1 |
"""simple docstring"""
import os
SCREAMING_SNAKE_CASE_ : Tuple = {'I': 1, 'V': 5, 'X': 1_0, 'L': 5_0, 'C': 1_0_0, 'D': 5_0_0, 'M': 1_0_0_0}
def _snake_case ( UpperCAmelCase_ : str ):
A__ = 0
A__ = 0
while index < len(Up... | 335 |
"""simple docstring"""
# 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... | 335 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class a ( metaclass=_lowerCamelCase ):
"""simple docstring"""
UpperCAmelCase = ["torch"]
def __init__( self: Optional[int] , *UpperCamelCase: Optional[Any] , **... | 335 |
"""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 ImageProcessingSavingT... | 335 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 | 1 |
"""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 ImageProcessingSavingT... | 335 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple doc... | 335 | 1 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: ... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE_ : Tuple = logging.get_logger(__name__)
def _snake_case ... | 335 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" ... | 335 | 1 |
"""simple docstring"""
from PIL import Image
def _snake_case ( UpperCAmelCase_ : Image , UpperCAmelCase_ : float ):
def brightness(UpperCAmelCase_ : int ) -> float:
return 128 + level + (c - 128)
if not -2_55.0 <= level <= 2_55.0:
... | 335 |
"""simple docstring"""
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, ImageClassifierOutputWithNoAttenti... | 335 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class a ( _lowerCamelCa... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : str ... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( UpperCAmelCase_ : ... | 335 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_fl... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_cop... | 335 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author =... | 335 | 1 |
"""simple docstring"""
import enum
import shutil
import sys
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ : str = shutil.get_terminal_size()
SCREAMING_SNAKE_CASE_ : Dict = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class a ( enum.Enum ):
... | 335 |
"""simple docstring"""
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_devi... | 335 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( UpperCAmelCase_ : List[Any] , Upper... | 335 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 6008_5147_5143 ):
try:
A__ = int(UpperCAmelCase_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
"""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 ImageProcessingSavingT... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class a ( _lowerCamelCase ):
... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
t... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Dict = {'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}
try:
if not is_torch_av... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Tuple = ... | 335 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch... | 335 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ : Union[str, Any] = logging.get_logger(__name__)
SCREAMIN... | 335 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of ini... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int ):
A__ = [1]
A__ , A__ , A__ = 0, 0, 0
A__ = ugly_nums[ia] * 2
A__ = ugly_nums[ia] * 3
A__ = ugly_nums[ia] * 5
for _ in range(1 , ... | 335 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor... | 335 | 1 |
"""simple docstring"""
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
... | 335 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / ""... | 335 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_commo... | 335 |
"""simple docstring"""
# 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... | 335 | 1 |
"""simple docstring"""
import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union
from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings
SCREAMING_S... | 335 |
"""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 ImageProcessingSavingT... | 335 | 1 |
"""simple docstring"""
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def _snake_case ( UpperCAmelCase_ : Dict ):
if not is_accelerate_available():
return method
A__ ... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 | 1 |
"""simple docstring"""
import math
import sys
def _snake_case ( UpperCAmelCase_ : int ):
if number != int(UpperCAmelCase_ ):
raise ValueError("""the value of input must be a natural number""" )
if number < 0:
raise ValueError("""the value of inp... | 335 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple doc... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
A__ = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return tota... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
import queue
class a :
"""simple docstring"""
def __init__( self: Optional[Any] , UpperCamelCase: str ):
"""simple docstring"""
A__ = data
... | 335 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" ... | 335 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Optional[int] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Tuple = {
'asapp/sew-d-tiny-1... | 335 |
"""simple docstring"""
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, ImageClassifierOutputWithNoAttenti... | 335 | 1 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': ... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
SCREAMING_SNAKE_CASE_ : Any = 'https://www.indeed.co.in/jobs?q=mobile+app+development&l='
def _snake_case ( UpperCAmelC... | 335 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 | 1 |
"""simple docstring"""
# Copyright (c) 2021-, NVIDIA CORPORATION. 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... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
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 (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
... | 335 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author =... | 335 | 1 |
"""simple docstring"""
from math import sqrt
def _snake_case ( UpperCAmelCase_ : int ):
assert isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
A__ = True
# 0 and ... | 335 |
"""simple docstring"""
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_devi... | 335 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from transformers import TensorFlowBenchmark, Tensor... | 335 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
... | 335 | 1 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
SCREAMING_SNAKE_CASE_ : List[str] = 3
def _snake_case ( UpperCAmelCase_ : int ):
print("""Generating primitive root of... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
"""simple docstring"""
import datasets
from .evaluate import evaluate
SCREAMING_SNAKE_CASE_ : Any = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and P... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
"""simple docstring"""
import os
from pathlib import Path
def _snake_case ( ):
from torch.utils.cpp_extension import load
A__ = Path(UpperCAmelCase_ ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
A__ = [
root... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
t... | 335 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class a ( unittest.TestCase ... | 335 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 | 1 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import time
from argparse import Namespace
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from torch.utils.data import DataLoader, TensorDataset
from transfor... | 335 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : float ):
if edge <= 0 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
raise ValueError("""Length must be a positive.""" )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
... | 335 |
"""simple docstring"""
import math
def _snake_case ( UpperCAmelCase_ : float , UpperCAmelCase_ : float ):
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of ini... | 335 | 1 |
"""simple docstring"""
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import... | 335 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor... | 335 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
... | 335 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( UpperCAmelCase_ : List[Any] ):
A__ = FileLock(str(tmpdir / """foo.lock""" ) )
A__ = FileLock(str(tmpdir / ""... | 335 | 1 |
"""simple docstring"""
# 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... | 335 |
"""simple docstring"""
# 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... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'configuration_resnet': ['RESN... | 335 |
"""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 ImageProcessingSavingT... | 335 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : Dict ): # noqa: E741
A__ = len(UpperCAmelCase_ )
A__ = 0
A__ = [0] * n
A__ = [False] * n
A__ = [False] * n
def dfs(UpperCAmelCase_ : ... | 335 | 1 |
"""simple docstring"""
from __future__ import annotations
SCREAMING_SNAKE_CASE_ : Optional[Any] = []
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
for i in range(len(Up... | 335 |
"""simple docstring"""
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class a ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase ( self: str ):
"""simple doc... | 335 | 1 |
"""simple docstring"""
import torch
from diffusers import DiffusionPipeline
class a ( _lowerCamelCase ):
"""simple docstring"""
def __init__( self: Union[str, Any] , UpperCamelCase: Optional[Any] , UpperCamelCase: Optional[Any] ):
... | 335 |
"""simple docstring"""
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_... | 335 | 1 |
"""simple docstring"""
import torch
from torch import nn
class a ( nn.Module ):
"""simple docstring"""
def __init__( self: Tuple , UpperCamelCase: Dict , UpperCamelCase: str , UpperCamelCase: int , UpperCamelCase: Dict , Upp... | 335 |
"""simple docstring"""
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
SCREAMING_SNAKE_CASE_ : int = [
'Prosecutor: "No videos were used in the crash investigation" ... | 335 | 1 |
"""simple docstring"""
import math
class a :
"""simple docstring"""
def __init__( self: List[Any] , UpperCamelCase: List[str]=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
A__ = n
A__... | 335 |
"""simple docstring"""
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, ImageClassifierOutputWithNoAttenti... | 335 | 1 |
"""simple docstring"""
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class a :
"""simple docstring"""
def __ini... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : list[list[int]] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : set ):
A__ , A__ = len(UpperCAmelCase_ ), len(grid[0] )
if (
min(Upper... | 335 | 1 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def _snake_case ( UpperCAmelCase_ : Any ):
A__ = SwinConfig(image_size=192 )
... | 335 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ : int = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
... | 335 | 1 |
"""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 a ( _lowerCame... | 335 |
"""simple docstring"""
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"""kwargs, expected""" , [
({"""num_shards""": 0, """max_num_jobs""": 1}, []),
({"""num_shards... | 335 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ : Any = {
'configuration_squeezebert': [
'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP... | 335 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
SCREAMING_SNAKE_CASE_ : str = parse(importlib.metadata.version('torch'))
def _snake_case ( Uppe... | 335 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import BertForMaskedLM
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ : str = argparse.ArgumentParser(
description=(
'Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM f... | 335 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
SCREAMING_SNAKE_CASE_ : Dict = '\\n@inproceedings{popovic-2015-chrf,\n title = "chr{F}: character n-gram {F}-score for automatic {MT} evaluation",\n author =... | 335 | 1 |
"""simple docstring"""
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _snake_case ( UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] ):
... | 335 |
"""simple docstring"""
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_devi... | 335 | 1 |
"""simple docstring"""
import os
def _snake_case ( UpperCAmelCase_ : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(UpperCAmelCase_ ) , UpperCAmelCase_ ) ) as in_file:
A__ = in_file.read()
A__ = [[int(UpperCAmel... | 335 |
"""simple docstring"""
class a :
"""simple docstring"""
def __init__( self: Dict ):
"""simple docstring"""
A__ = {}
def UpperCamelCase ( self: List[str] ):
"""simple docstring"""
... | 335 | 1 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
while b:
A__ , A__ = b, a % b
return a
def _snake_case ( UpperCAmelCase_ : int , UpperCAmelCase_ : int ):
... | 335 |
"""simple docstring"""
def _snake_case ( UpperCAmelCase_ : int = 10 ):
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or n < 0:
raise ValueError("""Invalid input""" )
A__ = 10**n
A__ = 2_8433 * (pow(2 , 783_0457 , ... | 335 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizer... | 335 |
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
SCREAMING_SNAKE_CASE_ : Optional[int] = tuple[float, float, float]
def _snake_case ( UpperCAmelCase_ : Pointad , UpperCAmelCase_ : Pointad ):
... | 335 | 1 |
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