code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingS... | 167 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_... | 3 | 0 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxM... | 171 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
... | 3 | 0 |
import requests
lowerCAmelCase__: List[str] = '' # <-- Put your OpenWeatherMap appid here!
lowerCAmelCase__: Tuple = 'https://api.openweathermap.org/data/2.5/'
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE = "Chicago" , SCREAMING_SNAKE_CASE = APPID ) -> List[str]:
return requ... | 345 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
... | 3 | 0 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_... | 17 |
'''simple docstring'''
import numpy as np
def A_( A : str , A : Optional[Any] , A : Tuple , A : Optional[int] , A : str):
UpperCamelCase = int(np.ceil((x_end - xa) / h))
UpperCamelCase = np.zeros((n + 1,))
... | 3 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
SCR... | 523 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_)
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = field(default=... | 3 | 0 |
from typing import List
import numpy as np
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = {key: len(_A ) for key, value in gen_kwargs.items() if isinstance(_A , _A )}
if len(set(lists_lengths.values() ) ) > 1... | 376 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase : Union[str, Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase : List[str] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def A_( A : ... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
A__ : List[str] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class snake_case__ :
def __init__( self : ... | 286 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def A_( A : str):
if not sentence:
return ""
UpperCamelCase = dict(zip(A , A))
return lower_to_upper.get(sentence[0] , sentence[0]) + sentence[1:]
if __... | 3 | 0 |
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : str , __A : int ):
snake_case__ : Tuple = arr.split("," )
def _lowercase ( self : Tuple ):
snake_case__ : List[Any] = [int(self.array[0] )] *... | 297 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 3 | 0 |
"""simple docstring"""
import importlib.metadata
import warnings
from copy import deepcopy
from packaging import version
from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available
if is_bitsandbytes_available():
import bitsandbytes as bnb
import torch
impo... | 437 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensi... | 3 | 0 |
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _UpperCAmelCase ( Upp... | 611 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-la... | 3 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTex... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
... | 3 | 0 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, ra... | 167 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 | 0 |
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.uti... | 171 |
'''simple docstring'''
def A_( A : list[int]):
UpperCamelCase = []
if len(A) == 1:
return [nums.copy()]
for _ in range(len(A)):
UpperCamelCase = nums.pop(0)
UpperCamelCase = permute(A)
for perm ... | 3 | 0 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__: Dict = logging.get_logger(__name__)
lowerCAmelCase__: str = '▁'
lowerCAmel... | 345 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def A_( A : float , A : float , A : int):
UpperCamelCase = x
UpperCamelCase = y
for step in range(A): # noqa: B007
UpperCamelCase ... | 3 | 0 |
import qiskit
def __SCREAMING_SNAKE_CASE ( a__ : int = 2 ) -> Optional[int]:
__A : str = qubits
# Using Aer's simulator
__A : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Creating a Quantum Circuit acting on the q register
__A : Tuple ... | 17 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
tr... | 3 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def UpperCamelCase__ ( _lowercase : Any , _lowercase : float = 0.0 , _lowercase : float = 1.0 ) -> Dict:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __nam... | 523 |
'''simple docstring'''
lowerCAmelCase : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_( A : dict , A : str , A :... | 3 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_... | 376 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
def __init__( self )-> Dict:
'''simple docstring'''
UpperCamelCase = ... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
import bisect
def a_ ( _UpperCAmelCase : list[int] ,_UpperCAmelCase : int ,_UpperCAmelCase : int = 0 ,_UpperCAmelCase : int = -1 ) -> Union[str, Any]:
if hi < 0:
__snake_case : str ... | 286 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
... | 3 | 0 |
import os
def SCREAMING_SNAKE_CASE ( snake_case_ : Optional[Any] ):
snake_case__ : Optional[int] = len(grid[0] )
snake_case__ : List[str] = len(snake_case_ )
snake_case__ : str = 0
snake_case__ : List[str] = 0
snake_case__ : Optional[int] = ... | 297 |
'''simple docstring'''
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, torc... | 3 | 0 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class A_(snake_case_ ):
"""simple docstring"""
d... | 437 |
'''simple docstring'''
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 ..mo... | 3 | 0 |
def _UpperCAmelCase ( UpperCamelCase: int ):
"""simple docstring"""
__lowerCAmelCase = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4))
| 611 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, requi... | 3 | 0 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
A... | 270 |
'''simple docstring'''
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 VQDif... | 3 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase_ ( snake_case_ ):
'''simple docstring'''
a__ : Any = ["""image_processor""", """tokenizer"""]
a__ : Optional[int] = "... | 167 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_... | 3 | 0 |
A__ : List[Any] = range(2, 20 + 1)
A__ : Union[str, Any] = [10**k for k in range(ks[-1] + 1)]
A__ : dict[int, dict[int, list[list[int]]]] = {}
def UpperCamelCase( __UpperCamelCase : Any ,__UpperCamelCase : Dict ,__UpperCamelCase : str ,_... | 171 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
... | 3 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__: Tuple = {
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
'Blip2Vision... | 345 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
... | 3 | 0 |
import math
import qiskit
def __SCREAMING_SNAKE_CASE ( a__ : int = 1 ,a__ : int = 1 ,a__ : int = 1 ) -> Any:
if (
isinstance(a__ ,a__ )
or isinstance(a__ ,a__ )
or isinstance(a__ ,a__ )
):
raise TypeError("""inputs must be intege... | 17 |
'''simple docstring'''
import numpy as np
def A_( A : str , A : Optional[Any] , A : Tuple , A : Optional[int] , A : str):
UpperCamelCase = int(np.ceil((x_end - xa) / h))
UpperCamelCase = np.zeros((n + 1,))
... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import TypedDict
class a ( snake_case_ ):
"""simple docstring"""
__lowerCAmelCase = 4_2
__lowerCAmelCase = 4_2
def UpperCamelCase__ ( _lowercase : str ) -> ... | 523 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_)
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = field(default=... | 3 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase__ : Any = {
'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}... | 376 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase : Union[str, Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase : List[str] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def A_( A : ... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
class snake_case__ :
def __init__( self : Optional[int] , __a : List[Any] ) -> List[str]:
'''simple docstring'''
__snake_case : Tuple = ... | 286 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def A_( A : str):
if not sentence:
return ""
UpperCamelCase = dict(zip(A , A))
return lower_to_upper.get(sentence[0] , sentence[0]) + sentence[1:]
if __... | 3 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self : List[Any] ):
snake_case__ : Optional[Any] = ""
snake_case__ : Union[str, Any] = ""
... | 297 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 3 | 0 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class A_(snake_case_ , unittest.Te... | 437 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensi... | 3 | 0 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
... | 611 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-la... | 3 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
A_ = 'us-east-1' # defaults region
@dataclass
class lowercase_ :
A_ = 4_2
A_ = "arn:aws:iam::558105141721:role/sagemaker_execution_role"
A_ = {
... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
... | 3 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
__UpperCamelCase :Union[str, Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(SCREAMING_S... | 167 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 | 0 |
def UpperCamelCase( __UpperCamelCase : list[int] ):
lowerCAmelCase_ : List[str] = []
if len(__UpperCamelCase ) == 1:
return [nums.copy()]
for _ in range(len(__UpperCamelCase ) ):
lowerCAmelCase_ : Optional[Any] = nums.pop(0 )
lo... | 171 |
'''simple docstring'''
def A_( A : list[int]):
UpperCamelCase = []
if len(A) == 1:
return [nums.copy()]
for _ in range(len(A)):
UpperCamelCase = nums.pop(0)
UpperCamelCase = permute(A)
for perm ... | 3 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
stooge(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Optional[Any]:
if i >= h:
... | 345 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def A_( A : float , A : float , A : int):
UpperCamelCase = x
UpperCamelCase = y
for step in range(A): # noqa: B007
UpperCamelCase ... | 3 | 0 |
def __SCREAMING_SNAKE_CASE ( a__ : str ) -> int:
__A : Optional[Any] = [0] * len(a__ )
for i in range(1 ,len(a__ ) ):
# use last results for better performance - dynamic programming
__A : Dict = prefix_result[i - 1]
while j > 0 and input_st... | 17 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
tr... | 3 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_C... | 523 |
'''simple docstring'''
lowerCAmelCase : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_( A : dict , A : str , A :... | 3 | 0 |
from typing import Any
def lowerCamelCase__ ( _A ):
'''simple docstring'''
if not input_list:
return []
snake_case_ = [input_list.count(_A ) for value in input_list]
snake_case_ = max(_A ) # Gets the maximum count in the in... | 376 |
'''simple docstring'''
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class SCREAMING_SNAKE_CASE__ :
def __init__( self )-> Dict:
'''simple docstring'''
UpperCamelCase = ... | 3 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : int ) -> Dict:
__snake_case , __snake_case : Optional[int] = [], []
while len(_UpperCAmelCase ) > 1:
__snake_case , __snake_case : Dict = min(_UpperCAmelCase ... | 286 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
'microsoft/unispeech-sat-base-100h-libri-ft': (
... | 3 | 0 |
import numpy as np
__lowerCamelCase : Union[str, Any] = [
['a', 'b', 'c', 'd', 'e'],
['f', 'g', 'h', 'i', 'k'],
['l', 'm', 'n', 'o', 'p'],
['q', 'r', 's', 't', 'u'],
['v', 'w', 'x', 'y', 'z'],
]
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __... | 297 |
'''simple docstring'''
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, torc... | 3 | 0 |
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bi... | 437 |
'''simple docstring'''
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 ..mo... | 3 | 0 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from tra... | 611 |
'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass
import torch
from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, requi... | 3 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( ) -> Dict:
for n in range(1 , 100_0000 ):
yield n * (n + 1) // 2
def UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ) -> Any:
snake_case__ : Union[str, Any] = 1
snake_case__ ... | 270 |
'''simple docstring'''
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 VQDif... | 3 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__lowercase = logging.get_lo... | 167 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Union[str, Any] = {
'configuration_git': ['GIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GitConfig', 'GitVisionConfig'],
'processing_... | 3 | 0 |
A__ : Any = [
'DownloadConfig',
'DownloadManager',
'DownloadMode',
'StreamingDownloadManager',
]
from .download_config import DownloadConfig
from .download_manager import DownloadManager, DownloadMode
from .streaming_download_manager import StreamingDownloadManager
| 171 |
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union
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
if is_torch_available():
... | 3 | 0 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__: Any = logging.get_logger(__name__)
lowerCAmelCase__: Union[s... | 345 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('Googling.....')
lowerCAmelCase : List[Any] = 'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
... | 3 | 0 |
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import... | 17 |
'''simple docstring'''
import numpy as np
def A_( A : str , A : Optional[Any] , A : Tuple , A : Optional[int] , A : str):
UpperCamelCase = int(np.ceil((x_end - xa) / h))
UpperCamelCase = np.zeros((n + 1,))
... | 3 | 0 |
'''simple docstring'''
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.da... | 523 |
'''simple docstring'''
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=snake_case_)
class SCREAMING_SNAKE_CASE__ ( snake_case_):
lowerCAmelCase_ = field(default=... | 3 | 0 |
from cva import destroyAllWindows, imread, imshow, waitKey
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ , snake_case_ = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(_A ):... | 376 |
'''simple docstring'''
from __future__ import annotations
lowerCAmelCase : Union[str, Any] = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase : List[str] = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def A_( A : ... | 3 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ : Optional[int] = logging.get_logger(__name__)
A__ : int = {
'google/realm-cc-news-pretrained-embedder': (
'https://huggingface.co/google/realm-cc-new... | 286 |
'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase
def A_( A : str):
if not sentence:
return ""
UpperCamelCase = dict(zip(A , A))
return lower_to_upper.get(sentence[0] , sentence[0]) + sentence[1:]
if __... | 3 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@req... | 297 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtte... | 3 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
a_ = get_tests_dir("""fixt... | 437 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensi... | 3 | 0 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
def _UpperCAmelCase ( UpperCamelCase: str=None , UpperCamelCase: Union[st... | 611 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-la... | 3 | 0 |
'''simple docstring'''
import os
import sys
import unittest
A_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object,... | 270 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
... | 3 | 0 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__lowercase = logging.get_logger(__name__)
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SC... | 167 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 3 | 0 |
from math import factorial
def UpperCamelCase( __UpperCamelCase : int = 20 ):
lowerCAmelCase_ : List[str] = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
lowerCAmelCase_ : List[Any] = n // 2
return int(facto... | 171 |
'''simple docstring'''
def A_( A : list[int]):
UpperCamelCase = []
if len(A) == 1:
return [nums.copy()]
for _ in range(len(A)):
UpperCamelCase = nums.pop(0)
UpperCamelCase = permute(A)
for perm ... | 3 | 0 |
from string import ascii_lowercase, ascii_uppercase
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Optional[int]:
if not sentence:
return ""
SCREAMING_SNAKE_CASE_ : Optional[int] = dict(zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) )
return lower_to_upper.get(sentence[... | 345 |
'''simple docstring'''
import colorsys
from PIL import Image # type: ignore
def A_( A : float , A : float , A : int):
UpperCamelCase = x
UpperCamelCase = y
for step in range(A): # noqa: B007
UpperCamelCase ... | 3 | 0 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCa... | 17 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
tr... | 3 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ = TypeVar('T')
class a ( Generic[T] ):
"""simple docstring"""
def __init__( self , snake_case_ ):
'''simple docstring'''
__Up... | 523 |
'''simple docstring'''
lowerCAmelCase : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_( A : dict , A : str , A :... | 3 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, Tens... | 4 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decod... | 4 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageR... | 4 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstri... | 4 | 1 |
"""simple docstring"""
import inspect
import os
import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed
import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
fr... | 4 |
"""simple docstring"""
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 _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase ... | 4 | 1 |
"""simple docstring"""
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 _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase ... | 4 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ... | 4 | 1 |
"""simple docstring"""
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational impor... | 4 |
"""simple docstring"""
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
__UpperCamelCase : List[Any] = logg... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Tuple = {
'''configuration_rag''': ['''RagConfig'''],
'''retrieval_rag''': ['''RagRetriever'''],
'''... | 4 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layo... | 4 | 1 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', ... | 4 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero... | 4 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[Any] ):
lowerCAmelCase = args.pruning_method
lowerCAmelCase = args.thresho... | 4 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-k... | 4 | 1 |
"""simple docstring"""
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class a ( a__ , a__ ):
... | 4 |
"""simple docstring"""
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 TFModelTest... | 4 | 1 |
"""simple docstring"""
import math
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : int ):
lowerCAmelCase = len(_UpperCAmelCase )
lowerCAmelCase = int(math.floor(math.sqrt(_UpperCAmelCase ) ) )
lowerCAmelCase = 0
while arr[min(_UpperC... | 4 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_v... | 4 | 1 |
"""simple docstring"""
import operator as op
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : List[str] ):
lowerCAmelCase = []
lowerCAmelCase = lambda _UpperCAmelCase , _UpperCAmelCase : int(x / y ) # noqa: E731 integer division operation
lowerCAmelCase = {
'^': op... | 4 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import re... | 4 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
if is_flax_available():
import jax.numpy as jnp
from transformers import FlaxXLMRobertaModel
... | 4 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 4 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-k... | 4 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 4 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all fil... | 4 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', ... | 4 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate... | 4 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : ... | 4 | 1 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ... | 4 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipelin... | 4 | 1 |
"""simple docstring"""
__UpperCamelCase : str = {
0: '''0''',
1: '''1''',
2: '''2''',
3: '''3''',
4: '''4''',
5: '''5''',
6: '''6''',
7: '''7''',
8: '''8''',
9: '''9''',
10: '''a''',
11: '''b''',
12: '''c''',
13: '''d''',
14: '''e''... | 4 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 4 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_chann... | 4 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F... | 4 | 1 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
if len(_UpperCAmelCase ) < 2:
raise ValueError('Monogons and Digons are not polygons in the Euclidean space' )
if any(i <= 0 for i in nums ):
raise ValueError('All values... | 4 |
"""simple docstring"""
from __future__ import annotations
import requests
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
lowerCAmelCase = F'https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'
return requests.get(_UpperCAmelCase ).json()
def _SCREAMING_SNAKE_C... | 4 | 1 |
"""simple docstring"""
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _SCREAMING_SNAKE_CASE ():
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
... | 4 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor
from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : ... | 4 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
... | 4 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
# See all MEGATRON_BERT models at https://huggingface.co/model... | 4 | 1 |
"""simple docstring"""
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 tran... | 4 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : str ):
return " ".join(input_str.split()[::-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 4 | 1 |
"""simple docstring"""
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = val
lowerCAmelCase = None
lowerCAmelCase = None
def UpperCamelCase__ ( self , _snake_case ):
"""... | 4 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decod... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : List[str] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Instru... | 4 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstri... | 4 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import BertConfig, 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():
... | 4 |
"""simple docstring"""
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 _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase ... | 4 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__UpperCamelCase : Union[str, Any] = generate_large_matrix()
__UpperCamelCase : Tuple = (
[[4, 3, 2, -1], [3, 2, 1, -1... | 4 |
"""simple docstring"""
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _SCREAMING_SNAKE_CASE ... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Transf... | 4 |
"""simple docstring"""
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
__UpperCamelCase : List[Any] = logg... | 4 | 1 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
RobertaT... | 4 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : Dict = {'''processing_layo... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : List[Any] = {
'''configuration_vision_encoder_decoder''': ['''Vi... | 4 |
"""simple docstring"""
from __future__ import annotations
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : list[float] ):
lowerCAmelCase = 0.00
lowerCAmelCase = 0
for resistor in resistors:
if resistor <= 0:
lowerCAmelCase = F'Resistor at index {index} has a negative or zero... | 4 | 1 |
"""simple docstring"""
class a : # Public class to implement a graph
def __init__( self , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
lowerCAmelCase = row
lowerCAmelCase = col
lowerCAmelCase = graph... | 4 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-k... | 4 | 1 |
"""simple docstring"""
import numpy as np
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray , _UpperCAmelCase : float = 1e-12 , _UpperCAmelCase : int = 100 , ):
assert np.shape(_UpperCAmelCase )[0] == np.shape(_UpperCAmel... | 4 |
"""simple docstring"""
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 TFModelTest... | 4 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : float , _UpperCAmelCase : float ):
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_UpperCAmelCase ) * abs(_UpperCAmelCase )
if __name__ == "__main__":
im... | 4 |
"""simple docstring"""
import argparse
import os
import re
import packaging.version
__UpperCamelCase : Union[str, Any] = '''examples/'''
__UpperCamelCase : str = {
'''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_v... | 4 | 1 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__UpperCamelCase : int = pytest.mark.integration
@pytest.mark.p... | 4 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import re... | 4 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__UpperCamelCase : List[Any] = {'''configuration_encoder_decoder''': ['''EncoderDecoder... | 4 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 4 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = {
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ous... | 4 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTe... | 4 | 1 |
"""simple docstring"""
from collections.abc import Sequence
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Sequence[float] , _UpperCAmelCase : float ):
return sum(c * (x**i) for i, c in enumerate(_UpperCAmelCase ) )
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : Sequence[flo... | 4 |
"""simple docstring"""
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', ... | 4 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-bas... | 4 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : ... | 4 | 1 |
"""simple docstring"""
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone imp... | 4 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipelin... | 4 | 1 |
"""simple docstring"""
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils i... | 4 |
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import OpenLlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common i... | 4 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - ... | 4 |
"""simple docstring"""
from typing import Any
class a :
def __init__( self , _snake_case ):
"""simple docstring"""
lowerCAmelCase = data
lowerCAmelCase = None
def __repr__( self ):
"""simple docstring"""
return F... | 4 | 1 |
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