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 |
|---|---|---|---|---|
from math import factorial
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if successes > trials:
raise ValueError("""successes must be lower or equal to trials""" )
if trials < 0 or successes < 0:
raise ValueError("""the function is defined for non-negative integers""" ... | 287 |
# Copyright 2021 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 app... | 287 | 1 |
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""hustvl/yolos-small""": ... | 287 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
... | 287 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase ={
"""configuration_whisper""": ["""WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """... | 287 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(default=... | 287 | 1 |
import argparse
import os
import re
_lowerCamelCase ="""src/diffusers"""
# Pattern that looks at the indentation in a line.
_lowerCamelCase =re.compile(R"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowerCamelCase =re.compile(R"""^\s*\"([^\"]+)\":""")
# Pattern tha... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class A__ ( yaml.SafeLoader):
def UpperCamelCase__ ( self , __magic_name__ ):
lowerCamelCase : List[Any] = [self.constructed_objects[key_node] for key... | 287 |
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = set(range(3, lowerCamelCase, 2 ) )
primes.add(2 )
for p in range(3, lowerCamelCase, 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p, lowerCamelCase, lowerCamelCase ... | 287 | 1 |
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_flax_available():
from transformers.mod... | 287 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 1 |
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 ...tokeniza... | 287 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 287 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
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__ :
def __init__( self , __magic_name__ , __magic_name__=sys.maxsize ):
... | 287 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 1 |
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
f... | 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
from typing import TYPE_CHECKING
from ....utils import _LazyModule
_lowerCamelCase ={"""tokenization_tapex""": ["""TapexTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_tapex import TapexTokenizer
else:
import sys
_lowerCamelCase =_LazyModule(__name__, globals()["""__file__""... | 287 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 | 1 |
import warnings
from .generation import TFGenerationMixin
class A__ ( __SCREAMING_SNAKE_CASE):
# warning at import time
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v... | 287 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
lo... | 287 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 1 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class A__ ( unittest.TestCase):
@property
de... | 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 287 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 287 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_lowerCamelCase ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_... | 287 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 287 | 1 |
from __future__ import annotations
from collections.abc import Callable
_lowerCamelCase =list[list[float | int]]
def _a ( lowerCamelCase, lowerCamelCase ):
lowerCamelCase : int = len(lowerCamelCase )
lowerCamelCase : Matrix = [[0 for _ in range(size + 1 ... | 287 |
from scipy.stats import pearsonr
import datasets
_lowerCamelCase ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset i... | 287 | 1 |
import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A__ ( __SCREAMING_SNAKE_CASE , unittest.TestCase):
_UpperCAmelCase : Any = DownBlockaD # noqa F4... | 287 |
import copy
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 ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCame... | 287 | 1 |
import os
def _a ( ):
with open(os.path.dirname(lowerCamelCase ) + """/p022_names.txt""" ) as file:
lowerCamelCase : str = str(file.readlines()[0] )
lowerCamelCase : str = names.replace("""\"""", """""" ).split(""",""" )
names.sort()
lowe... | 287 |
import json
import sys
def _a ( lowerCamelCase, lowerCamelCase ):
with open(lowerCamelCase, encoding="""utf-8""" ) as f:
lowerCamelCase : List[Any] = json.load(lowerCamelCase )
lowerCamelCase : Optional[Any] = ["""<details>""", """<summary>Show updated b... | 287 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataC... | 287 |
def _a ( lowerCamelCase ):
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 287 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase, lowerCamelCase ):
# Initialise PyTorch mo... | 287 |
# Copyright 2021 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 app... | 287 | 1 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
_lowerCamelCase ={
"""tiny.en""": """https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32acce... | 287 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 1 |
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_lowerCamelCase =logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""")
@requir... | 287 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(default=... | 287 | 1 |
# 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 required by applicab... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set... | 287 |
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = set(range(3, lowerCamelCase, 2 ) )
primes.add(2 )
for p in range(3, lowerCamelCase, 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p, lowerCamelCase, lowerCamelCase ... | 287 | 1 |
_lowerCamelCase =[
"""DownloadConfig""",
"""DownloadManager""",
"""DownloadMode""",
"""StreamingDownloadManager""",
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 287 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 1 |
# 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 required by applicab... | 287 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 287 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""microsoft/trocr-base-handwritten""": (
"""https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"""
),
# Se... | 287 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
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
from ...test_configuration_common imp... | 287 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase ={
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""... | 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
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 ImageProcessingSavi... | 287 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 287 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if len(lowerCamelCase ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
left >= len(lowerCamelCase )
or left < -len(lowerCamelCase )
o... | 287 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 1 |
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class A__ :
def __init__( self , __magic_name__ = None ):
if components is None:
lowerCamelCase : Optional[Any] = []
lowerC... | 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
lowerCamelCase : int = list(range(len(lowerCamelCase ) ) )
lowerCamelCase : Optional[int] = [v / w for v, w in zip(lowerCamelCase, lowerCamelCase )]
i... | 287 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 287 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_av... | 287 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 287 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 |
from scipy.stats import pearsonr
import datasets
_lowerCamelCase ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset i... | 287 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase ... | 287 |
import copy
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 ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCame... | 287 | 1 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 |
import json
import sys
def _a ( lowerCamelCase, lowerCamelCase ):
with open(lowerCamelCase, encoding="""utf-8""" ) as f:
lowerCamelCase : List[Any] = json.load(lowerCamelCase )
lowerCamelCase : Optional[Any] = ["""<details>""", """<summary>Show updated b... | 287 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 |
def _a ( lowerCamelCase ):
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 287 | 1 |
import os
import pytest
from attr import dataclass
_lowerCamelCase ="""us-east-1""" # defaults region
@dataclass
class A__ :
_UpperCAmelCase : str
_UpperCAmelCase : Any = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
_UpperCAmelCase :... | 287 |
# Copyright 2021 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 app... | 287 | 1 |
import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path
import pytest
import transformers
from transformers import (
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoTokenizer,
BertConfig,
BertTokenizer,
BertTokenizerFast,
CTR... | 287 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
def _a ( lowerCamelCase, lowerCamelCase ):
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
lowerCamelCase : Dict = str(bin(lowerCamelCase ) )[2:] # remove the leading "0b"
lowerCamelCase : List[Any] = str(bin(lowe... | 287 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 1 |
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import con... | 287 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(default=... | 287 | 1 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class A__ ( unittest.TestCase):
def UpperCamelCase__ ( self ):
lowerCamelCase : Dict = [... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTeste... | 287 |
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = set(range(3, lowerCamelCase, 2 ) )
primes.add(2 )
for p in range(3, lowerCamelCase, 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p, lowerCamelCase, lowerCamelCase ... | 287 | 1 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils im... | 287 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 1 |
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_copies.py
_lowerCamelCase ="""src/diffus... | 287 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 287 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ... | 287 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_lowerCamelCase ={
"""configuration_owlvit""": [
"""OWLVIT_PRETR... | 287 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 1 |
from collections import defaultdict
from math import gcd
def _a ( lowerCamelCase = 150_0000 ):
lowerCamelCase : defaultdict = defaultdict(lowerCamelCase )
lowerCamelCase : str = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % ... | 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 | 1 |
from __future__ import annotations
from math import gcd
def _a ( lowerCamelCase, lowerCamelCase = 2, lowerCamelCase = 1, lowerCamelCase = 3, ):
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# ... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
_lowerCamelCase =8.3144598
def _a ( lowerCamelCase, lowerCamelCase ):
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("""Molar mass cannot be less than or equal to 0 kg/mol""" )
else:
return (3 * UNI... | 287 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 | 1 |
from sklearn.metrics import fa_score
import datasets
_lowerCamelCase ="""
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
_lowerCamelCase ="""
Args:
predictions (`list` of `int`): Predic... | 287 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 1 |
from cva import destroyAllWindows, imread, imshow, waitKey
def _a ( lowerCamelCase ):
# getting number of pixels in the image
lowerCamelCase , lowerCamelCase : Union[str, Any] = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(lower... | 287 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 1 |
class A__ :
def __init__( self ):
lowerCamelCase : Optional[int] = """"""
lowerCamelCase : Dict = """"""
lowerCamelCase : Optional[int] = []
def UpperCamelCase__ ( self , __magic_name__ , __magic_name__ ... | 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 | 1 |
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
_lowerCamelCase =parse(importlib.metadata.version("""torch"""))
def _a ( lowerCamelCase, lowerCamelCase, lowerCamelCase ):
if operation not in... | 287 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 287 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_lowerCamelCase =TypeVar("""T""")
class A__ ( Generic[T]):
def __init__( self , __magic_... | 287 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 287 | 1 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():... | 287 |
from scipy.stats import pearsonr
import datasets
_lowerCamelCase ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset i... | 287 | 1 |
_lowerCamelCase ={
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"""],
}
def _a ( lowerCamelCase, lowerCam... | 287 |
import copy
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 ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCame... | 287 | 1 |
def _a ( lowerCamelCase = 10, lowerCamelCase = 1000, lowerCamelCase = True ):
assert (
isinstance(lowerCamelCase, lowerCamelCase )
and isinstance(lowerCamelCase, lowerCamelCase )
and isinstance(lowerCamelCase, lowerCamelCase )
), "Invalid type of value(s) specified... | 287 |
import json
import sys
def _a ( lowerCamelCase, lowerCamelCase ):
with open(lowerCamelCase, encoding="""utf-8""" ) as f:
lowerCamelCase : List[Any] = json.load(lowerCamelCase )
lowerCamelCase : Optional[Any] = ["""<details>""", """<summary>Show updated b... | 287 | 1 |
def _a ( lowerCamelCase, lowerCamelCase = 0 ):
lowerCamelCase : Tuple = length or len(lowerCamelCase )
lowerCamelCase : List[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowerCamelCase , lowerCamelCase : Any = ... | 287 |
def _a ( lowerCamelCase ):
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 287 | 1 |
from __future__ import annotations
def _a ( lowerCamelCase, lowerCamelCase ):
lowerCamelCase : Optional[Any] = sorted(numsa + numsa )
lowerCamelCase , lowerCamelCase : List[Any] = divmod(len(lowerCamelCase ), 2 )
if mod == 1:
return all_number... | 287 |
# Copyright 2021 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 app... | 287 | 1 |
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
_lowerCamelCase =logging.get_logger(__name__)
def _a ( lower... | 287 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={"""vocab_file... | 287 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 1 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(default=... | 287 | 1 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxMode... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Dict = """ClapFeatureExtractor"""
_UpperCAmelCase : List[str] = ("""RobertaTokenizer""", """RobertaToke... | 287 |
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = set(range(3, lowerCamelCase, 2 ) )
primes.add(2 )
for p in range(3, lowerCamelCase, 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p, lowerCamelCase, lowerCamelCase ... | 287 | 1 |
import re
from filelock import FileLock
try:
import nltk
_lowerCamelCase =True
except (ImportError, ModuleNotFoundError):
_lowerCamelCase =False
if NLTK_AVAILABLE:
with FileLock(""".lock""") as lock:
nltk.download("""punkt""", quiet=True)
def _a ( ... | 287 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 1 |
def _a ( lowerCamelCase ):
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError("""The given input must be positive""" )
# get the generated string sequence
lowerCamelCase : List[Any] = gray_code_sequence_string(lowerCamelCase )
#
#... | 287 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 287 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class A__ ( __SCREAMING... | 287 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
from __future__ import annotations
import requests
_lowerCamelCase =set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edit... | 287 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... | 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
import mpmath # for roots of unity
import numpy as np
class A__ :
def __init__( self , __magic_name__=None , __magic_name__=None ):
# Input as list
lowerCamelCase : str = list(poly_a or [0] )[:]
lowerCamelCase : Union[str, Any] = l... | 287 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 | 1 |
from math import factorial
def _a ( lowerCamelCase = 100 ):
return sum(map(lowerCamelCase, str(factorial(lowerCamelCase ) ) ) )
if __name__ == "__main__":
print(solution(int(input("""Enter the Number: """).strip())))
| 287 |
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
_lowerCamelCase =logging.get_logger(__name__)
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , *__magic_name__ , **__magic_name__ ):
warnings.warn(
... | 287 | 1 |
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _a ( lowerCamelCase ):
return DownloadCommand(args.model, args.cache_dir, args.force, args.trust_remote_code )
class A__ ( __SCREAMING_SNAKE_CASE):
@staticmethod
def UpperCame... | 287 |
import warnings
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
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""nvidia/... | 287 | 1 |
import math
def _a ( lowerCamelCase ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i... | 287 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name_... | 287 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(def... | 287 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_E... | 287 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert impo... | 287 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 287 | 1 |
from __future__ import annotations
from random import choice
def _a ( lowerCamelCase ):
return choice(lowerCamelCase )
def _a ( lowerCamelCase, lowerCamelCase ):
lowerCamelCase : Any = random_pivot(lowerCamelCase )
# partition based on pivot
... | 287 |
from scipy.stats import pearsonr
import datasets
_lowerCamelCase ="""
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset i... | 287 | 1 |
import argparse
import re
import torch
from CLAP import create_model
from transformers import AutoFeatureExtractor, ClapConfig, ClapModel
_lowerCamelCase ={
"""text_branch""": """text_model""",
"""audio_branch""": """audio_model.audio_encoder""",
"""attn""": """attention.self""",
"""self.pro... | 287 |
import copy
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 ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCame... | 287 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class A__ ( __SCREAMING_SNAKE_CASE):
def __init__( self , __magic_name__ , __magic... | 287 |
import json
import sys
def _a ( lowerCamelCase, lowerCamelCase ):
with open(lowerCamelCase, encoding="""utf-8""" ) as f:
lowerCamelCase : List[Any] = json.load(lowerCamelCase )
lowerCamelCase : Optional[Any] = ["""<details>""", """<summary>Show updated b... | 287 | 1 |
import math
def _a ( lowerCamelCase ):
assert isinstance(lowerCamelCase, lowerCamelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even num... | 287 |
def _a ( lowerCamelCase ):
return " ".join(
"""""".join(word[::-1] ) if len(lowerCamelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words("""Hey wollef sroirraw"""))
| 287 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def _a ( lowerCamelCase ):
lowerCamelCase : List[str] = SwinC... | 287 |
# Copyright 2021 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 app... | 287 | 1 |
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTo... | 287 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""camembert-base""": """https://huggingface.co/camembert... | 287 | 1 |
import logging
import os
from .state import PartialState
class A__ ( logging.LoggerAdapter):
@staticmethod
def UpperCamelCase__ ( __magic_name__ ):
lowerCamelCase : str = PartialState()
return not main_process_only or (main_process_only and stat... | 287 |
_lowerCamelCase ={
"joule": 1.0,
"kilojoule": 1_0_0_0,
"megajoule": 1_0_0_0_0_0_0,
"gigajoule": 1_0_0_0_0_0_0_0_0_0,
"wattsecond": 1.0,
"watthour": 3_6_0_0,
"kilowatthour": 3_6_0_0_0_0_0,
"newtonmeter": 1.0,
"calorie_nutr": 4_1_8_6.8,
"kilocalorie_nutr": 4_1_8_6_8_0_0.0_0... | 287 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any ... | 287 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : str = field(default=... | 287 | 1 |
def _a ( lowerCamelCase ):
if p < 2:
raise ValueError("""p should not be less than 2!""" )
elif p == 2:
return True
lowerCamelCase : Any = 4
lowerCamelCase : List[str] = (1 << p) - 1
for _ in range(p - 2 ):
lowerCamelCase : List[Any] = ((s... | 287 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""BridgeTower/bridgetower-base""": """https://huggingface.co/BridgeTower/bridgetower-base/blob/main/con... | 287 | 1 |
import argparse
import struct
import unittest
class A__ :
def __init__( self , __magic_name__ ):
lowerCamelCase : List[str] = data
# Initialize hash values
lowerCamelCase : List[str] = [
0X6A09E667,
0XBB67AE85,
... | 287 |
def _a ( lowerCamelCase = 100_0000 ):
lowerCamelCase : Any = set(range(3, lowerCamelCase, 2 ) )
primes.add(2 )
for p in range(3, lowerCamelCase, 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p, lowerCamelCase, lowerCamelCase ... | 287 | 1 |
from collections import namedtuple
_lowerCamelCase =namedtuple("""from_to""", """from_ to""")
_lowerCamelCase ={
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_0_0_0),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00454, 264.172),
"""cubicyard""": from_to(0... | 287 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 287 |
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,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ... | 287 | 1 |
from __future__ import annotations
_lowerCamelCase =[True] * 1_0_0_0_0_0_1
_lowerCamelCase =2
while i * i <= 1_0_0_0_0_0_0:
if seive[i]:
for j in range(i * i, 1_0_0_0_0_0_1, i):
_lowerCamelCase =False
i += 1
def _a ( lowerCamelCase ... | 287 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_... | 287 | 1 |
import argparse
import os
# New Code #
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 Acce... | 287 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertMode... | 287 | 1 |
import copy
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 ..auto import CONFIG_MAPPING
_lowerCamelCase =logging.get_logger(__name__)
_lowerCame... | 287 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
... | 287 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import ... | 287 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
_lowerCamelCase =5_0_0_0_0_0
_lowerCamelCase , _lowerCamelCase =os.path.split(__file__)
_lowerCamelCase =os.path.join(RESULTS_BASEPATH, """results""", RESULTS_... | 287 | 1 |
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