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 ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy... | 193 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import V... | 384 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def __a ( __lowerCAmelCase ) -> List[str]:
SCREAMING_SNAKE_CASE : str = [
'encoder.version',
'decoder.version',
... | 352 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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_configu... | 185 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: List[Any] = logging.get_logger(__name__)
__a: str = {
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class SCREAMING_... | 108 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 485 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
... | 647 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 511 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
import math
import unittest
def UpperCamelCase__( UpperCamelCase__ : Tuple )->bool:
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 ... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
import logging
from transformers import PretrainedConfig
UpperCamelCase = logging.getLogger(__name__)
UpperCamelCase = {
'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json',
}
c... | 520 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
def a(lowercase__ ):
'''simple docstring'''
snake_case_ = [0] * len(lowercase__ )
snake_case_ = []
snake_case_ = [1] * len(lowercase__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowercase__ ) ):
if indegree[i] == ... | 187 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 0 |
def UpperCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ) -> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(snake_case__ , n - 1 , snake_case__ ) * a) % mod
else:
... | 193 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def _UpperCAmelCase ( _lowerCamelCase : List[str] , _lowerCamelCase : List[str] , _lowerCamelCase : Tuple , _lowerCamelCase : ... | 384 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_... | 352 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 0 |
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = [
(1000, '''M'''),
(900, '''CM'''),
(500, '''D'''),
(400, '''CD'''),
(100, '''C'''),
(90, '''XC'''),
(50, '''L'''),
(40, '''XL'''),
(10, '''X'''),
(9, '''IX'''),
(5, '''V'''),
(4, '''IV'''... | 185 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split,... | 108 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_to... | 485 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 0 |
import unittest
from knapsack import knapsack as k
class lowerCamelCase__ ( unittest.TestCase):
'''simple docstring'''
def lowerCAmelCase__ (self ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Any = 0
lowerCAmelCase__ : str = [0]... | 647 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowerCAmelCase : str = logging.get_logger(_... | 511 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 0 |
from __future__ import annotations
import math
def UpperCamelCase__( UpperCamelCase__ : int )->bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
... | 190 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCamelCase = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
UpperCamelCase = ... | 520 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 0 |
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=10))
| 187 | 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,
ConditionalDetrForSegmen... | 604 | 0 |
from scipy.stats import pearsonr
import datasets
_lowerCAmelCase : List[str] = "\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption tha... | 193 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
import math
import os
import sys
def _UpperCAmelCase ( _lowerCamelCase : List[str] ) -> str:
_lowerCAmelCase : Union[str, Any] = """"""
try:
with open(_lowerCamelCase , """rb""" ) as binary_file:
_lowerCAmelCase : Any = ... | 384 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_util... | 352 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
UpperCamelCase_ : List[Any] = logging.get_logger(__name__)
class _a ( _A ):
def __init__( self ,*_SCREAMING_SNAKE_CASE ,**_SCREAMI... | 185 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_utils import requi... | 108 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
def _lowerCamelCase ( __A : Union[str, Any] = 200 ) -> int:
_UpperCAmelCase : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
_UpperCAmelCase : Any = [0] * (pence + 1)
_UpperCAmelCase : Optional[Any] = 1 # base case... | 485 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
def lowerCAmelCase__ ( lowerCamelCase_ : Dict ,lowerCamelCase_ : Union[str, Any]):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(lowerCamelCase_ ,int(b / 2)) * actual_power(lowerCamelCase_ ,int(b / 2))
... | 647 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 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_inputs
if is_to... | 511 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__: Tuple = {
'configuration_trajectory_transformer': [
'TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'TrajectoryTransformerConfig',
... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
UpperCamelCase = '\\n@misc{chen2021evaluating,\n title={Evaluating Large L... | 520 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
from __future__ import annotations
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance < 0:
raise ValueError('Resistance can... | 187 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 0 |
import requests
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> None:
"""simple docstring"""
lowerCAmelCase__ = {'Content-Type': 'application/json'}
lowerCAmelCase__ = requests.post(snake_case__ , json={'text': message_body} , headers=snake_c... | 193 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( ) -> Tuple:
_lowerCAmelCase : List[Any] = 0
for i in range(1 , 10_01 ):
total += i**i
return str(_lowerCamelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 384 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 0 |
import numpy as np
def __a ( __lowerCAmelCase ) -> np.array:
return 1 / (1 + np.exp(-vector ))
def __a ( __lowerCAmelCase ) -> np.array:
return vector * sigmoid(1.702 * vector )
if __name__ == "__main__":
import doctest
doctest.testm... | 352 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 0 |
'''simple docstring'''
def __a ( _UpperCamelCase: Tuple ) -> int:
"""simple docstring"""
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError("Input value must be an \'int\' type" )
_snake_case = 0
while numbe... | 185 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 0 |
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 108 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class A_ ( _A ):
'''simple docstring'''
_SCREAMI... | 485 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case : Optional[Any] ={
'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaC... | 647 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 0 |
def A_ ( a ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[Any] = [], []
while len(a ) > 1:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Optional[int] = min(a ), max(a )
start.appe... | 511 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 0 |
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class SCREAMING_SNAKE_CASE__ :
def __init__( self,__lowerCamelCase ):
A__ = data
A__ = [0X6745_2301, 0Xefcd_ab89, 0X98ba_dcfe, 0X1032_5476, 0Xc3d2_e1f0]... | 190 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 0 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'vocab_file': 'vocab.json',
'tokenizer_config_fil... | 520 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 0 |
import math
def a(lowercase__ = 100 ):
'''simple docstring'''
snake_case_ = sum(i * i for i in range(1 , n + 1 ) )
snake_case_ = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main... | 187 | 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,
ConditionalDetrForSegmen... | 604 | 0 |
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
_lowerCAmelCase : Dict = {"vocab_file": "vocab.txt"}
_... | 193 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase_ = {
"""configuration_clip"... | 384 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def __a ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = "x" , __lowerCAmelCase = 10**-10 , __lowerCAmelCase = 1 , ) -> complex:
SCREAMING_SNAKE_CASE : ... | 352 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import... | 185 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__a: Union[str, Any] = '''.'''
if __name__ == "__main__":
__a: Tuple = os.path.join(REPO_PATH, '''utils/documentation_tests.... | 108 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
def _lowerCamelCase ( __A : Dict , __A : str ) -> bool:
_UpperCAmelCase : str = len(__A ) + 1
_UpperCAmelCase : str = len(__A ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i o... | 485 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__snake_case : Optional[int] =[
'Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phon... | 647 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 0 |
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] = {
'vocab_file': 'vocab.txt'... | 511 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def UpperCamelCase__( UpperCamelCase__ : Any )->Union[str, Any]:
A__ = FileLock(str(tmpdir / '''foo.lock''' ) )
A__ = FileLock(str(tmpdir / '''foo.lock''' ... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase_ ( _lowercase , _lowercase ) ... | 520 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
import unittest
from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
A = get_tests_dir('fixtures/spiece.model')
@require_sentencepi... | 187 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 0 |
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enab... | 193 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Union[str, Any] ) -> Any: # noqa: E741
_lowerCAmelCase : List[str] = len(_lowerCamelCase )
_lowerCAmelCase : Any = 0
_lowerCAmelCase : List[Any] = [0] * n
_lowerCAmelC... | 384 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 0 |
from typing import Dict, Iterable, 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... | 352 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.util... | 185 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 0 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.... | 108 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
def _lowerCamelCase ( __A : str , __A : Optional[int] ) -> Tuple:
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def _lowerCamelCase ( __A : Dict , __A : Tuple=0 ) -> Optional[Any]:
return sorted(__A ... | 485 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tenso... | 647 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFXLMRobertaModel
@r... | 511 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 0 |
def UpperCamelCase__( UpperCamelCase__ : Dict = 10_00 )->int:
A__ , A__ = 1, 1
A__ = []
for i in range(1 , n + 1 ):
A__ = prev_numerator + 2 * prev_denominator
A__ = prev_numerator + prev_denominator
... | 190 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 0 |
def lowerCamelCase_ ( _lowercase = 50 ) -> int:
__A : int = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
... | 520 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 0 |
def a(lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError('The length of profit and weight must be same.' )
if max_weight <= 0:
raise ValueError('max_weight must greater than zero.' )
i... | 187 | 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,
ConditionalDetrForSegmen... | 604 | 0 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
_lowerCAmelCase : int = "Usage of script: script_name <size_of_canvas:int>"
_lowerCAmelCase : List[str] = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def Upper... | 193 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_pro... | 384 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import log... | 352 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
UpperCamelCase_ : Union[str, Any] = TypeVar('''T''')
class _a ( Ge... | 185 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Distribu... | 108 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class A_ ( _A , _A ):
'''simple docstring'''
@register_to_config
def ... | 485 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCAmelCase__ ( lowerCamelCase_ : int ,lowerCamelCase_ : Dict ,lowerCamelCase_ : Optional[Any] ,lowerCamelCase_ : Optional[Any]=5):
'''simple docstring... | 647 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase : Optional[Any] = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecode... | 511 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
a__: int = WebClient(token=os.environ['CI_SLACK_BOT_TOKEN'])
def UpperCamelCase__( UpperCa... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, ... | 520 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_conf... | 187 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 0 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
_lowerCAmelCase : List[str] = tran... | 193 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : List[str] ) -> int:
_lowerCAmelCase : int = hex_num.strip()
if not hex_num:
raise ValueError("""No value was passed to the function""" )
_lowerCAmelCase : Optional[int] = hex_num[0] ... | 384 | from __future__ import annotations
import math
A_ = "2020.9.26"
A_ = "xcodz-dot, cclaus, dhruvmanila"
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> tuple[float, float]:
"""simple docs... | 604 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
"""facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.jso... | 352 | from __future__ import annotations
import os
from collections.abc import Mapping
A_ = tuple[int, int]
class __lowercase :
def __init__( self : Dict , __lowerCamelCase : set[int] , __lowerCamelCase : Mapping[EdgeT, int] ) -> None:
... | 604 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transforme... | 185 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 0 |
import heapq
import sys
import numpy as np
__a: Optional[int] = tuple[int, int]
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Dict ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase = []
_... | 108 | import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __UpperCAmelCase ( )-> List[Any]:
"""simple docstring"""
... | 604 | 0 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class A_ ( _A ):
'''simple docstring'''
@staticmethod
@abstractmethod
def snake_case__ ( _A) -> Optional[int]:
"""simple docstring"""
raise NotImplemente... | 485 | import gc
import threading
import time
import psutil
import torch
class __lowercase :
def __init__( self : List[str] ) -> List[str]:
'''simple docstring'''
lowercase = psutil.Process()
lowercase = False
def... | 604 | 0 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def lowerCAmelCase__ ( ... | 647 | import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDPMSchedul... | 604 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early... | 511 | import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"to... | 604 | 0 |
from math import ceil
def UpperCamelCase__( UpperCamelCase__ : Any , UpperCamelCase__ : Dict )->Dict:
A__ = list(range(0 , UpperCamelCase__ ) )
A__ = [item for sublist in list(device_map.values() ) for item in sublist]
# Dupl... | 190 | import numpy as np
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def __UpperCAmelCase ( UpperCAmelCase )-> np.array:
"""simple do... | 604 | 0 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCamelCase = logging.get_logger(__name__)
class _a ( _A ):
'''simple docstring'''
def __init__( self , *__UpperCAmelCase , **__UpperCAmelCase ... | 520 | import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...tes... | 604 | 0 |
import warnings
from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor
A = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _A ):
"""simple docstring"""
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simp... | 187 | 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,
ConditionalDetrForSegmen... | 604 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ ( snake_case__ , snake_case__ ) -> str:
"""simple docstring"""
lowerCAmelCase__ = BeautifulSoup(requests.get(snake_case__ , params=snake_case__ ).content , 'html.parser' )
lowerCA... | 193 | import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load... | 604 | 0 |
'''simple docstring'''
def _UpperCAmelCase ( _lowerCamelCase : Tuple ) -> float:
return 10 - x * x
def _UpperCAmelCase ( _lowerCamelCase : Optional[Any] , _lowerCamelCase : str ) -> float:
if equation(_lowerCamelCase ) * equation(_lowerCam... | 384 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if not is_torch_available():
... | 604 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowercase ( _A):
'''simple docstring'''
def __init__( self : Union[str, Any] , snake_case : Dict , snake_case : List[Any] ):
... | 352 | import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = "▁"
A_ = {"vocab_file": "vocab.txt", ... | 604 | 0 |
'''simple docstring'''
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def __a ... | 185 | from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_te... | 604 | 0 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case , __snake_case = None , __snake_case = None , __snake_case = False , ) -> tuple[int, float, str]:
_UpperCAmelCase = cipher_alphabet or [chr(__snake_case ) for i in range... | 108 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 0 |
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require_torch... | 485 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 604 | 0 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def lowerCAmelCase__ ( lowerCamelCase_ : Dict ,lowerCamelCase_ : Dict):
'''simple docstring'''
return math.sqrt(sum(pow(a - b ,2) for a, b in zip(lowerCamelC... | 647 | 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 __lowercase ( _A , unittest.TestCase ... | 604 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase : Union[str, Any] = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowerCAmelCase : in... | 511 | import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> List[Any]:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name... | 604 | 0 |
a__: Dict = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'
def UpperCamelCase__( UpperCamelCase__ : Optional[int] )->bytes:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
A__ = f"a bytes-like o... | 190 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __lowercase ( unittest.TestCase ):
... | 604 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def lowerCamelCase_ ( ) -> tuple[list[int], int]:
__A : List[Any] = [randint(-1_000 , 1_000 ) for i in range(10 )]
__A : ... | 520 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> str:
"""simple docstring"""
return "\n".join(
f'{number} * {i} = {number * i}' for i in range(1, number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_ta... | 604 | 0 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 187 | def __UpperCAmelCase ( UpperCAmelCase = 50 )-> int:
"""simple docstring"""
lowercase = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start i... | 604 | 0 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __snake_case ( _A , unittest... | 193 | import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A_ ... | 604 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.