code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Dict = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTex... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Any = {
'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',... | 670 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}
class low... | 670 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 1 |
import operator as op
def snake_case (__lowercase ) -> Optional[Any]:
'''simple docstring'''
_snake_case : List[str] = []
_snake_case : Union[str, Any] = lambda __lowercase , __lowercase : int(x / y ) # noqa: E731 integer division operation
_snake_... | 670 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 1 |
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory... | 670 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 1 |
from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 1 |
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=None , lowercase_=None ):
_snake_case : Any = data
_snake_case : Dict = previous
_snake_case : str = next_node
def __str__( ... | 670 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 1 |
from collections import defaultdict
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ ):
_snake_case : Optional[Any] = total # total no of tasks (N)
# DP table will have a dimension of (2^M)*N
# initially all val... | 670 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class lowercase_ ( __snake_case ):
_lowerCamelCase = [... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def snake_case (__lowercase , __lowercase ) -> str | Literal[False]:
'''simple docstring'''
_snake_case : Dict = list(__lowercase )
_snake_case : str = list... | 670 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 1 |
from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 1 |
import operator
def snake_case (__lowercase , __lowercase = False , __lowercase = None ) -> list:
'''simple docstring'''
_snake_case : int = operator.lt if reverse else operator.gt
_snake_case : Optional[int] = solution or []
if not arr:
... | 670 | from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 670 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts ... | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowercase_ ( unittest.TestCase ):
@property
... | 670 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 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,
)
__SCREAMING_SNAKE_CASE : List[Any] = {
'configuration_clip': [
... | 670 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_l... | 670 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 1 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class lowercase_ ( __snake_ca... | 670 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 1 |
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 (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQu... | 670 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : str = {
'configuration_efficientformer': [
'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHI... | 670 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__SCREAMING_SNAKE_CASE : int = {'tokenization_herbert': ['HerbertTokenizer']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except Op... | 670 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowercase_ ( __snake_case , __snake_case ):
... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : ... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 1 |
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def snake_case (__lowercase , __lowercase , __lo... | 670 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 1 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__SCREAMING_SNAKE_CASE : Tuple = HUGGINGFACE_HUB_CACHE
__SCREAMING_SNAKE_CASE : Tuple = 'config.json'
__SCREAMING_SNAKE_CASE : str = 'diffusion_pytorch_model.bin'
__SCREAMING_SNAKE_C... | 670 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 1 |
import numpy
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ ):
_snake_case : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and... | 670 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 1 |
import numpy as np
class lowercase_ :
def __init__( self ):
_snake_case : int = (0, 0)
_snake_case : Optional[int] = None
_snake_case : str = 0
_snake_case : List[Any] = 0
_sn... | 670 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 1 |
def snake_case () -> List[str]:
'''simple docstring'''
_snake_case : Dict = 0
for i in range(1 , 1_001 ):
total += i**i
return str(__lowercase )[-10:]
if __name__ == "__main__":
print(solution()) | 670 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 1 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : Tuple = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(2_7))
print(perfect_cube(4)) | 670 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
'uc... | 670 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class lowercase_ ( __snake_case ):
def __init__( self , lowercase_ , lowerca... | 670 | from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 670 | 1 |
__SCREAMING_SNAKE_CASE : str = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def snake_case (__lowercase , __lowercase , __lowercase , __lowercase ) -> List[str]:
... | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : int = Rectangle(height=0.5 , width=0.5 )
_snake_case : Dict = Rectangle(height=0.46 , width=0.46 ).set_strok... | 670 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 1 |
import math
def snake_case (__lowercase , __lowercase ) -> float:
'''simple docstring'''
return math.pow(__lowercase , 2 ) - a
def snake_case (__lowercase ) -> float:
'''simple docstring'''
return 2 * x
def snake_case (__lowercase ) -> float:
... | 670 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 1 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase_ ( __snake_case ):
_lowerCamelCase = 'M-CLIP'
def __init__( self , lowercase_=1_024 , lowercase_=768 , **lowercase_ ):
_snake_case : Unio... | 670 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 670 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 1 |
import re
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : List[str] = re.compile(r"^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$" )
if match := re.search(__lowercase , __lowercase ):
return match.string == phone
return False
if __... | 670 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 1 |
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_torchaudio
@require_sent... | 670 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 1 |
from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 1 |
def snake_case (__lowercase ) -> list:
'''simple docstring'''
_snake_case : Dict = len(__lowercase )
for _ in range(__lowercase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_snake_ca... | 670 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 1 |
import cva
import numpy as np
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ ):
if k in (0.04, 0.06):
_snake_case : List[Any] = k
_snake_case : int = window_size
else:
... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokeniza... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger... | 670 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 1 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/con... | 670 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 1 |
from ...processing_utils import ProcessorMixin
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'feature_extractor']
_lowerCamelCase = 'TvltImageProcessor'
_lowerCamelCase = 'TvltFeatureExtractor'
def __init__( self , lowerc... | 670 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 1 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutputWithP... | 670 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 1 |
import string
import numpy
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return b if a == 0 else greatest_common_divisor(b % a , __lowercase )
class lowercase_ :
_lowerCamelCase = string.ascii_uppercase + string.digits
# ... | 670 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : ... | 670 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 1 |
__SCREAMING_SNAKE_CASE : Tuple = [sum(int(c, 1_0) ** 2 for c in i.__str__()) for i in range(1_0_0_0_0_0)]
def snake_case (__lowercase ) -> int:
'''simple docstring'''
_snake_case : List[str] = 0
while number:
# Increased Speed Slightly by checking ... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__SCREAMING_SNAKE_CASE : Tuple = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__SCREAMING_SNAKE_CASE : Optional[int] = typing.Union[np.floataa, int, floa... | 670 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 1 |
def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : List[str] = F"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowercase )
if number < 1:
... | 670 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 1 |
from __future__ import annotations
from typing import Any
class lowercase_ ( __snake_case ):
pass
class lowercase_ :
def __init__( self , lowercase_ ):
_snake_case : Any = data
_snake_case : Node | None = None... | 670 | from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 670 | 1 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
_snake_case : List[str] = 0
for ch in input_str:
_snake_case : int = ord(__lowercase )
_snake_case : Tuple = pow(2 , __lowercase )
# If we alre... | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowe... | 670 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 1 |
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, enable_p... | 670 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 1 |
import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def snake_case (__lowercase , __lowercase , __lowercase = "x" , __lowercase = 10**-10 , __lowercase = 1 , ) -> complex:
'''simple docstring'''
_snake_case : List[str] = ... | 670 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 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,
PILImageResampling... | 670 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicabl... | 670 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__SCREAMING_SNAKE_CASE : int = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generatio... | 670 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 1 |
import inspect
import os
import sys
import unittest
import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu
class lowercase_ ( unittest.TestCase ):
def UpperCamelCase ( self ):
_snake_case : Dict = inspect.getfile(acceler... | 670 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 1 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def snake_case (__lowercase = 8 ) -> str:
'''simple docstring'''
_snake_case : Dict = ascii_letters + digits + punctuation
return "".join(secrets.... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ... | 670 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 1 |
def snake_case (__lowercase ) -> list:
'''simple docstring'''
_snake_case : Dict = [0] * len(__lowercase )
for i in range(1 , len(__lowercase ) ):
# use last results for better performance - dynamic programming
_snake_case : Tuple = ... | 670 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Optional[int] = {
'configuration_roberta_prelayernorm': [
'ROBERTA_PRELAYERNORM_PRETR... | 670 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 1 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimi... | 670 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 1 |
def snake_case (__lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> int:
'''simple docstring'''
if index == number_of_items:
return 0
_snake_case : int = 0
_snake_case : int = 0
_snake_case : A... | 670 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.... | 670 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 1 |
import math
import unittest
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
assert isinstance(__lowercase , __lowercase ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
ret... | 670 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
import torch
from transformers import AutoModel
class lowercase_ ( torch.nn.Module ):
def __init__( self , lowercase_="sayef/fsner-bert-base-uncased" ):
super(lowercase_ , self ).__init__()
_snake_case : List[Any] = AutoModel.fr... | 670 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 670 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class lowercase_ ( tf.keras.layers.Layer ):
def __init__( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_=1 , lowercase_=False , **lowercase_ ):
super... | 670 | from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 670 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils i... | 670 | import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTeste... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Tuple = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Config',
... | 670 | import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import I... | 670 | 1 |
def snake_case (__lowercase ) -> int:
'''simple docstring'''
_snake_case : Optional[int] = len(__lowercase )
_snake_case : int = sum(__lowercase )
_snake_case : List[str] = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for... | 670 | from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__) # pylint: disable=invalid-name
def snake_case (... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {
'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
if... | 670 | import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = loggin... | 670 | 1 |
from __future__ import annotations
import math
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers,... | 670 | import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __snake_case ):
_lowerCamelCase = ['image_processor', 'tokenizer']
_lowerCamelCase = 'CLIPImageProcessor'
_lowerCamelCase = ('XLM... | 670 | 1 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[Any] = loggin... | 670 | from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVa... | 670 | import functools
import gc
import inspect
import torch
from .imports import is_npu_available, is_xpu_available
def snake_case (*__lowercase ) -> Dict:
'''simple docstring'''
if not isinstance(__lowercase , __lowercase ):
_snake_case : Dict = list(__lowercase )... | 670 | 1 |
import requests
def snake_case (__lowercase , __lowercase ) -> None:
'''simple docstring'''
_snake_case : Dict = {"Content-Type": "application/json"}
_snake_case : Optional[int] = requests.post(__lowercase , json={"text": message_body} , headers... | 670 | __SCREAMING_SNAKE_CASE : Union[str, Any] = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA',
'o'... | 670 | 1 |
def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import rep... | 670 | 1 |
from graphs.minimum_spanning_tree_kruskal import kruskal
def snake_case () -> int:
'''simple docstring'''
_snake_case : Optional[Any] = 9
_snake_case : Optional[int] = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
from typing import Any
class lowercase_ :
def __init__( self , lowercase_ ):
_snake_case : Any = data
_snake_case : List[str] = None
class lowercase_ :
def __init__( self ):
_snake_case : Lis... | 670 | import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
_lower... | 670 | 1 |
import numpy as np
def snake_case (__lowercase , __lowercase , __lowercase = 1e-12 , __lowercase = 100 , ) -> tuple[float, np.ndarray]:
'''simple docstring'''
assert np.shape(__lowercase )[0] == np.shape(__lowercase )[1]
# Ensure proper dimensionality.
asser... | 670 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/c... | 670 | 1 |
def snake_case (__lowercase , __lowercase ) -> int:
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def snake_case () -> None:
'''simple docstring'''
assert or_gate(0 , 0 ) == 0
assert or_gate(0 , 1 ) == 1
assert or... | 670 | from cva import destroyAllWindows, imread, imshow, waitKey
def snake_case (__lowercase ) -> Tuple:
'''simple docstring'''
_snake_case ,_snake_case : int = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(__lowercase ):
... | 670 | 1 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__SCREAMING_SNAKE_CASE : List[str] = 3
def snake_case (__lowercase ) -> int:
'''simple docstring'''
print("Generating primitive root of p" )
while True:
... | 670 | import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__SCREAMING_SNAKE_CASE : List[str] = Mapping[str, np.ndarray]
__SCREAMING_SNAKE_CASE : List[Any] = Mapping[st... | 670 | 1 |
def snake_case (__lowercase = 50 ) -> int:
'''simple docstring'''
_snake_case : Dict = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length... | 670 | from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
... | 670 | 1 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from trans... | 670 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
if not grid or not grid[0]:
raise TypeError("The grid does not contain the appropriate information" )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][cell_n - 1]
_snake... | 670 | 1 |
from __future__ import annotations
def snake_case (__lowercase , __lowercase , __lowercase ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
... | 670 | import random
def snake_case (__lowercase , __lowercase ) -> tuple:
'''simple docstring'''
_snake_case ,_snake_case ,_snake_case : List[Any] = [], [], []
for element in data:
if element < pivot:
less.append(__lowercase )
... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : str = {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartC... | 670 | from math import pow, sqrt
def snake_case (*__lowercase ) -> bool:
'''simple docstring'''
_snake_case : str = len(__lowercase ) > 0 and all(value > 0.0 for value in values )
return result
def snake_case (__lowercase , __lowercase ) -> float | ValueError:
... | 670 | 1 |
def snake_case (__lowercase , __lowercase , __lowercase = 0 , __lowercase = 0 ) -> int:
'''simple docstring'''
_snake_case : Tuple = right or len(__lowercase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return ... | 670 | import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class lowercase_ ( __snake_case ):
def __init__( self , *lowercase_ , **lowercase_ ):... | 670 | 1 |
from __future__ import annotations
from decimal import Decimal
from numpy import array
def snake_case (__lowercase ) -> list[list[float]]:
'''simple docstring'''
_snake_case : Tuple = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implem... | 670 | from __future__ import annotations
from typing import TypedDict
class lowercase_ ( __snake_case ):
_lowerCamelCase = 42
_lowerCamelCase = 42
def snake_case (__lowercase ) -> list[str]:
'''simple docstring'''
if not isinstance(__lowercase , __... | 670 | 1 |
def snake_case (__lowercase ) -> bool:
'''simple docstring'''
return str(__lowercase ) == str(__lowercase )[::-1]
def snake_case (__lowercase ) -> int:
'''simple docstring'''
return int(__lowercase ) + int(str(__lowercase )[::-1] )
def snake_case (__lowercase =... | 670 | # NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
from ..controlnet.pipeline_controlnet import ... | 670 | 1 |
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