code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import Dict, List, Optional, Tuple, 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,
)... | 305 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[Any] = logging.get_logger(__name__)
A : int = {
"microsoft/trocr-base-handwritten": (
"https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A : Optional[Any] = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto accou... | 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 1 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pyte... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
from collections.abc import Callable
import numpy as np
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CAS... | 305 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 1 |
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures... | 305 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 1 |
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Return True if there is node that has not iterated.
SCREAMING_SNAKE_CASE_ = [False] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
que... | 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 1 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | # 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 applicabl... | 305 | 1 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCamelCase (TensorF... | 305 | from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = "x" , __UpperCamelCase = 1_0**-1_0 , __UpperCamelCase = 1 , ):
SCREAMING_SNAKE_CASE_ = symbols(__Upp... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Initialise PyTorch model
SCREAMING_SNA... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A : Any = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : Optional[int] , *__magic... | 305 | 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
A : str = logging.get_logger(__name__)
A : O... | 305 | 1 |
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_common import ConfigTester
from ...test_modeli... | 305 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 1 |
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
Pixa... | 305 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 1 |
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 1 |
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
if is... | 305 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 1 |
def a__ ( __UpperCamelCase ):
assert column_title.isupper()
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase ) - 1
SCREAMING_SNAKE_CASE_ = 0
while index >= 0:
SCREAMING_SNAKE_CASE_ ... | 305 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
A : Union[str, Any] = datasets.utils.logging... | 305 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBert... | 305 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 1 |
from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 1 |
from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 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
A : Tuple = logging.get_logger(__name__)
A : List[str] = {
"go... | 305 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 1 |
A : Dict = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = True
... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 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 I... | 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A : str = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : List[str] = logging.get_logger(__name__)
A : Union[str, Any] = {
"facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/conf... | 305 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 305 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[Any] = logging.get_logger(__name__)
A : Union[str, Any] = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "... | 305 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 1 |
from math import factorial
A : dict[str, int] = {str(digit): factorial(digit) for digit in range(10)}
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("Parameter number must be int" )
if numbe... | 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 1 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .... | 305 | # 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 applicabl... | 305 | 1 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = set({"(", "[", "{"} )
SCREAMING_SNAKE_CASE_ = set({")", "]", "}"} )
SCREAMING_SNAKE_CASE_ = {"{": "}", "[": "]", "(": ")"}
... | 305 | from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 1 |
from typing import Dict, Optional
import numpy as np
import datasets
A : Any = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) or multi-class se... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
A : Dict = logging.get_logger(__name__)
A : int = {"vocab_file": "vocab.json", "merges_f... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import ar... | 305 | 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
A : str = logging.get_logger(__name__)
A : O... | 305 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSol... | 305 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Un... | 305 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 1 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0 for i in range(r + 1 )]
# nc0 = 1
SCREAMING_SNAKE_CASE_ = 1
for i in range(1 , n + 1 ):
# to compute current row from previous row.
... | 305 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 1 |
def a__ ( __UpperCamelCase ):
if not isinstance(__UpperCamelCase , __UpperCamelCase ):
raise TypeError("Input value must be an 'int' type" )
SCREAMING_SNAKE_CASE_ = 0
while number:
position += 1
number >>= 1
return po... | 305 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Optional[int] = logging.get_logger(__name__)
A : Optional[Any] = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class... | 305 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 1 |
from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 1 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
A : List[str] = logging.... | 305 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 1 |
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
A : Optional[int] = logging.getLo... | 305 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have ... | 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCamelCase (SCREAMING_SNAKE_CASE__ , ... | 305 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 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
from ..auto import CONFIG_MAPPING
A : str = logging.get_logger(__name__)
A : O... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_m... | 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 1 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
return int((input_a, input_a).count(1 ) != 0 )
def a__ ( ):
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or_gate(1 , 0 ) == 1
assert or_gate(1 , 1 ) == 1... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 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 applicabl... | 305 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set... | 305 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from transformers.testing_... | 305 | # 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 applicabl... | 305 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = s.rsplit(__UpperCamelCase , __Upper... | 305 | from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import fl... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_t... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 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
A : str = logging.get_logger(__name__)
A : O... | 305 | 1 |
from math import isqrt
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __UpperCamelCase , __UpperCamelCase )... | 305 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 305 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils i... | 305 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from .... | 305 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 1 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_tra... | 305 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 1 |
from collections.abc import Callable
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Callable | None = None ) -> None:
# Stores actual heap items.
SCREAMING_SNAKE_CASE_ = []
# Stores inde... | 305 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 1 |
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
loggi... | 305 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 1 |
from __future__ import annotations
A : str = 1.6_021E-19 # units = C
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("You cannot supply more or le... | 305 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 1 |
import argparse
from collections import defaultdict
import yaml
A : Tuple = "docs/source/en/_toctree.yml"
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = defaultdict(__UpperCamelCase )
for doc in model_doc:
counts[doc["local"]] +=... | 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowerCamelCase (tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( ... | 305 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 1 |
from __future__ import annotations
import pandas as pd
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
SCREAMING_SNAKE_CASE_ = [0] * no_of_processes
# Copy the burst time in... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def a__ ( __UpperCamelCase ):
# vision encoder
if "img_encoder.pos_embed" in name:
SCREAMING_SNAKE_CASE_ = name.replace("img... | 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A : Any = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDep... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
... | 305 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 1 |
import math
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = int(math.floor(math.sqrt(__UpperCamelCase ) ) )
SCREAMING_SNAKE_CASE_ = 0
while arr... | 305 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 1 |
import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 1 |
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
BarkCoarseConfig,
Bar... | 305 | # 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 applicabl... | 305 | 1 |
def a__ ( __UpperCamelCase , __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [1]
for i in range(2 , __UpperCamelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
SCREAMING_SNAKE_CASE_ ... | 305 | from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 1 |
# using dfs for finding eulerian path traversal
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=None ):
SCREAMING_SNAKE_CASE_ = (path or []) + [u]
for v in graph[u]:
if visited_edge[u][v] is False:
... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : Dict = {
"huggingface/informer-tourism-monthly": (
"https://huggingface.co/huggingf... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
A : str = datasets.utils.logging.get_logger(__name__)
@dataclass
class lowerCamelCase (datasets.... | 305 | 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
A : str = logging.get_logger(__name__)
A : O... | 305 | 1 |
import math
A : Any = 10
A : List[Any] = 7
A : int = BALLS_PER_COLOUR * NUM_COLOURS
def a__ ( __UpperCamelCase = 2_0 ):
SCREAMING_SNAKE_CASE_ = math.comb(__UpperCamelCase , __UpperCamelCase )
SCREAMIN... | 305 | import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 305 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
# Initialise PyTorch model
SCREAMING_SNAKE_CA... | 305 | from __future__ import annotations
import numpy as np
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = np.shape(__UpperCamelCase )
if rows != columns:
SCREAMING_SNAKE_CASE_ = (
"'table' has to... | 305 | 1 |
def a__ ( __UpperCamelCase ):
if n_term == "":
return []
SCREAMING_SNAKE_CASE_ = []
for temp in range(int(__UpperCamelCase ) ):
series.append(F'''1/{temp + 1}''' if series else "1" )
return series
if __name__ == "__main__":
A ... | 305 | from math import pi, sqrt, tan
def a__ ( __UpperCamelCase ):
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
... | 305 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCamelCase :
"""simple docstring"""
lowerCamelCase__ = 42
lowerCamelCase__ = None
lowerCamelCase__ = None
A : Any = namedtup... | 305 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 1 |
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_... | 305 | import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils i... | 305 | 1 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
C... | 305 | from __future__ import annotations
A : Dict = "#"
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict ) -> None:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : List[Any] , __magic_... | 305 | 1 |
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = (DDPMParallelScheduler,)
def __A ( self : Dict , **__ma... | 305 | from collections import deque
class lowerCamelCase :
"""simple docstring"""
def __init__( self : str , __magic_name__ : str , __magic_name__ : int , __magic_name__ : int ) -> None:
SCREAMING_SNAKE_CASE_ = process_name ... | 305 | 1 |
import math
import qiskit
def a__ ( __UpperCamelCase = 1 , __UpperCamelCase = 1 , __UpperCamelCase = 1 ):
if (
isinstance(__UpperCamelCase , __UpperCamelCase )
or isinstance(__UpperCamelCase , __UpperCamelCase )
or isinstance(__UpperCamelC... | 305 | import torch
def a__ ( ):
if torch.cuda.is_available():
SCREAMING_SNAKE_CASE_ = torch.cuda.device_count()
else:
SCREAMING_SNAKE_CASE_ = 0
print(F'''Successfully ran on {num_gpus} GPUs''' )
if __name__ == "__main__":
main... | 305 | 1 |
import requests
from bsa import BeautifulSoup
def a__ ( __UpperCamelCase = "AAPL" ):
SCREAMING_SNAKE_CASE_ = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
SCREAMING_SNAKE_CASE_ = BeautifulSoup(requests.get(__UpperCamelCase ).text , "h... | 305 | from collections.abc import Generator
from math import sin
def a__ ( __UpperCamelCase ):
if len(__UpperCamelCase ) != 3_2:
raise ValueError("Input must be of length 32" )
SCREAMING_SNAKE_CASE_ = b""
for i in [3, 2, 1, 0]:
little_endian += s... | 305 | 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_big_bird import... | 305 | import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, BlipImageProces... | 305 | 1 |
def a__ ( ):
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 2
while i * i <= n:
SCREAMING_SNAKE_CASE_ ... | 305 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A : List[Any] = {"configuration_dpt": ["DPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPTConfig"]}
try:
if n... | 305 | 1 |
import baseaa
import io
import json
import os
from copy import deepcopy
from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Any , __magic_name__ : Optional[int] ) ->... | 305 | from __future__ import annotations
import collections
import pprint
from pathlib import Path
def a__ ( __UpperCamelCase ):
return "".join(sorted(__UpperCamelCase ) )
def a__ ( __UpperCamelCase ):
return word_by_signature[signature(__UpperCamelCase )]
A : st... | 305 | 1 |
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [0] * len(__UpperCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [1] * len(__UpperCamelCase )
for values in graph.values():
for i in values:
... | 305 | import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : int = logging.get_logger(__name__)
A : str = {
"kakaobrain/align-base": "https://hug... | 305 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""... | 305 | import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def a__ ( __UpperCamelCase ):
return x + 2
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : ... | 305 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
A : List[Any] = logging.get_logger(__name... | 305 | import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def a__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
S... | 305 | 1 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def a__ ( __UpperCamelCase ):
if "model" in orig_key:
SCREAMING_SNAKE_CASE_ = orig_key.replace("model." , "" )
if "norm1" in orig_key:
SCREAMING_SNAKE_CASE_ ... | 305 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A : List[str] = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
... | 305 | 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 applicabl... | 305 | import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import... | 305 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConf... | 305 | # 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 applicabl... | 305 | 1 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def a__ ( __UpperCamelCase ):
SCREAMING_SNAKE_CASE_ = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"m... | 305 | from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
lowerCamelCase__ = field(defa... | 305 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A : Optional[Any] = logging.get_logger(__name__)
A : int = {
"ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}
class ... | 305 | from ....utils import logging
A : List[str] = logging.get_logger(__name__)
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : List[str] , __magic_name__ : Optional[Any] , __magic_name__ : Any=None ... | 305 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A : Optional[int] = {
"configuration_blenderbot_small": [
"BLENDERBOT_SMALL... | 305 | import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
""... | 305 | 1 |
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