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 |
|---|---|---|---|---|
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
... | 604 | 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 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageR... | 604 | 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 | 1 |
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 | 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 | 1 |
from math import loga
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(UpperCAmelCase, UpperCAmelCase ):
rai... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> Optional[Any]:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(UpperCAmelCase, n - 1, UpperCAmelCase ) * a) % mod
... | 604 | 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 | 1 |
A_ = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def __UpperCAmelCase ( UpperCAmelCase )-> bytes:
"""simple docstring"""
if not isinstance(UpperCAmelCase, UpperCAmelCase ):
lowercase = f'a bytes-li... | 604 | 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 | 1 |
A_ = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def __UpperCAmelCase ( UpperCAmelCase )-> int... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A_ = logging.getL... | 604 | 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 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A_ = TypeVar("T")
class __lowercase ( Generic[T] ):
def __init__( ... | 604 | 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 | 1 |
import re
import string
import numpy as np
import datasets
A_ = "\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n"
A_ = "\nArgs:\n predictions: List of predicted texts.\n referen... | 604 | 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 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowercase ( _A ):
lowercase = 'M-CLIP'
def __init__( self : Optional[int] , __lowerCamelCase : Optional[Any]=10_24 , __lowerCamelCase ... | 604 | 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 | 1 |
import re
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = re.compile(
R'''^(?:0|94|\+94|0{2}94)''' R'''7(0|1|2|4|5|6|7|8)''' R'''(-| |)''' R'''\d{7}$''' )
return bool(re.search(UpperCAme... | 604 | 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 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
A_ = logging.get_logger(__name__)
class __lowercase ( _A ):
lowercase = 'upernet'
def __init__( s... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if not is_torch_available():
raise Optio... | 604 | 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 | 1 |
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
A_ = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}
try:
if not is_tokenizers_availab... | 604 | 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 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __lowercase ( _A ):
... | 604 | 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 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> np.array:
"""simple docstring"""
lowercase = f'{sampling_rate}'
... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A_ = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A_ = _LazyModule(__name__... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
A_ = logging.get_logger(__name__)
A_ = {"voca... | 604 | 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 | 1 |
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_channel_dimen... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if not numbers:
return 0
if not isinstance(UpperCAmelCase, (list, tuple) ) or not all(
isinstance(UpperCAmelCase, UpperCAmelCase ) for number in numbers ... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int(input_a == input_a == 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
print('''Trut... | 604 | 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 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
... | 604 | 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 | 1 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.... | 604 | 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 | 1 |
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 | 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 | 1 |
from sklearn.metrics import matthews_corrcoef
import datasets
A_ = "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positiv... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ = {
"configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
"feature_ext... | 604 | 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 | 1 |
import math
import unittest
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
assert isinstance(UpperCAmelCase, UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < numbe... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the... | 604 | 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 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( UpperCAmelCase )-> list[int]:
"""simple docstring"""
lowercase = [True] * limit
lowercase = False
lowercase = False
lowercase = True
for i... | 604 | 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 | 1 |
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, Distri... | 604 | 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 | 1 |
import warnings
from typing import Any, Dict, 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
fro... | 604 | 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 | 1 |
import argparse
import os
import re
A_ = "src/diffusers"
# Pattern that looks at the indentation in a line.
A_ = re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
A_ = re.compile(R"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` a... | 604 | 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 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,... | 604 | 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 | 1 |
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 | 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 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_det... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
A_ = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNot... | 604 | 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 | 1 |
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,
ImageIn... | 604 | 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 | 1 |
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.data import Dat... | 604 | 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 | 1 |
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 __U... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
A_ = "Alexander Joslin"
import operator as op
from .stack import Stack
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
lowercase = {'''*''': op.mul, '''/''': op.truediv, '''+''': op.add, '''-''': op.sub}
... | 604 | 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 | 1 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class __lowercase ( _A ):
def __init__( self : Optional... | 604 | 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 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 604 | 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 | 1 |
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 | 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 | 1 |
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
A_ = "\\n@misc{chen2021evaluating,\n title={Evaluating Large Language Models Trai... | 604 | 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 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> dict[str, float]:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only on... | 604 | 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 | 1 |
from math import isqrt
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
return all(number % divisor != 0 for divisor in range(2, isqrt(UpperCAmelCase ) + 1 ) )
def __UpperCAmelCase ( ... | 604 | 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 | 1 |
import unittest
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_ima... | 604 | 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 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
... | 604 | 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 | 1 |
from __future__ import annotations
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase = None, UpperCAmelCase = None, UpperCAmelCase = False, )-> tuple[int, float, str]:
"""simple docstring"""
lowercase = cipher_alphabet or [chr(UpperCAmelCa... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase, UpperCAmelCase ):
raise TypeError('''Input value must be an \'int\' type''' )
lowercase = 0
while number:
positio... | 604 | 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 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
'''files''', [
['''full:README.md''', '''dataset_infos.json'''],
['''empty:README.md''', '''dataset_in... | 604 | 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 | 1 |
from math import ceil
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> Dict:
"""simple docstring"""
lowercase = list(range(0, UpperCAmelCase ) )
lowercase = [item for sublist in list(device_map.values() ... | 604 | 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 | 1 |
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 __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase ... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase = 1000 )-> int:
"""simple docstring"""
lowercase ,lowercase = 1, 1
lowercase = []
for i in range(1, n + 1 ):
lowercase = prev_numerator + 2 * prev_denominator
lowercase... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase ) + 1
lowercase = len(UpperCAmelCase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix str... | 604 | 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 | 1 |
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 transformers.utils.import_utils i... | 604 | 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 | 1 |
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 604 | 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 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
A_ = logging.get_logger(__name__)
class __lowercase ( _A ):
def __init__( self : List[str] , *__lowerCamelCase : List[str] , *... | 604 | 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 | 1 |
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 VOCAB_FILES_NAM... | 604 | 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 | 1 |
import math
import os
import sys
def __UpperCAmelCase ( UpperCAmelCase )-> str:
"""simple docstring"""
lowercase = ''''''
try:
with open(UpperCAmelCase, '''rb''' ) as binary_file:
lowercase = binary_fil... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
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 | 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 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def __UpperCAmelCase ( )-> tuple[list[int], int]:
"""simple docstring"""
lowercase = [randint(-1000, 1000 ) for ... | 604 | 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 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transforme... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> float:
"""simple docstring"""
if edge <= 0 or not isinstance(UpperCAmelCase, UpperCAmelCase ):
raise ValueError('''Length must be a positive.''' )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2))... | 604 | 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 | 1 |
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 | 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 | 1 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A_ = False
class __lowercase ( unittest.TestCase ):
... | 604 | 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 | 1 |
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_avai... | 604 | 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 | 1 |
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_CHE... | 604 | 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 | 1 |
from __future__ import annotations
from typing import Any
def __UpperCAmelCase ( UpperCAmelCase )-> int:
"""simple docstring"""
if not postfix_notation:
return 0
lowercase = {'''+''', '''-''', '''*''', '''/'''}
lowercase ... | 604 | 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 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
A_ = logging.get_logger(__name__)
... | 604 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
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 | 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 | 1 |
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 | 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 | 1 |
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
A_ = transforms.Compose(
... | 604 | 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 | 1 |
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 | 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 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 604 | 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 | 1 |
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 Decoder, DecoderOutput, E... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
return credit_card_number.startswith(('''34''', '''35''', '''37''', '''4''', '''5''', '''6''') )
def __UpperCAmelCase ( UpperCAmelCase )-> bool:... | 604 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A_ = {
"configuration_clip": [
"CLIP_PRETRAINED_CO... | 604 | 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 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
"vocab_file": "vocab.json",
"tokenizer_config_file": "tokenizer_config.json",
"mer... | 604 | 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 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_t... | 604 | 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 | 1 |
import math
def __UpperCAmelCase ( UpperCAmelCase )-> bool:
"""simple docstring"""
assert isinstance(UpperCAmelCase, UpperCAmelCase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> float:
"""simple docstring"""
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
... | 604 | A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 1 |
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 __lowercase ( _A , _A ):
@register_to_config
def __init__( self ... | 604 | 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 | 1 |
def __UpperCAmelCase ( UpperCAmelCase )-> str:
"""simple docstring"""
return " ".join(
''''''.join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
... | 604 | 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 | 1 |
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
... | 604 | 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 | 1 |
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
A_ = "%20".join(argv[1:]) if len(argv) > 1 else quote(str(input("Search: ")))
print("Googling....... | 604 | 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 | 1 |
import torch
from diffusers import StableDiffusionPipeline
A_ = "path-to-your-trained-model"
A_ = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
A_ = "A photo of sks dog in a bucket"
A_ = pipe(prompt, num_inference_steps=50, guidance_scal... | 604 | 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 | 1 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils impo... | 604 | 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 | 1 |
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 | 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 | 1 |
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 | 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 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"configuration_deberta": ["DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaConfig", "DebertaOnnxConfig... | 604 | 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 | 1 |
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 | def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> int:
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def __UpperCAmelCase ( )-> None:
"""simple docstring"""
... | 604 | 1 |
A_ = 256
# Modulus to hash a string
A_ = 1000003
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase )-> bool:
"""simple docstring"""
lowercase = len(UpperCAmelCase )
lowercase = len(UpperCAmelCase ... | 604 | 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 | 1 |
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 __UpperCAme... | 604 | 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 | 1 |
from __future__ import annotations
A_ = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
A_ = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def __UpperCAmelCase ( UpperCAmelCase )-> list[float]:
"""simple docstring"""
... | 604 | 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 | 1 |
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_ = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def __UpperCAmelCase ( UpperCAmelCase... | 604 | 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 | 1 |
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_utils im... | 604 | 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 | 1 |
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