code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from math import factorial
def SCREAMING_SNAKE_CASE_ ( __A : int = 20 ) -> int:
"""simple docstring"""
a_ : str = 2 * n # middle entry of odd rows starting at row 3 is the solution for n = 1,
# 2, 3,...
a_ : Dict = n /... | 32 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 32 | 1 |
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class SCREAMING_SNAKE_CASE__ ( pl.LightningModule ):
def __init__( self : List[str] , SCREAMING_SNAKE_CASE__ ... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 32 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=lowercase__ ):
snake_case__ : int = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : str , *SCREAMING_SNAKE_CASE__ : Union[st... | 32 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __A : Lis... | 32 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUI... | 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 1 |
import random
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
a_ : Union[str, Any] = num - 1
a_ : List[Any] = 0
while s % 2 == 0:
a_ : Optional[int] = s // 2
... | 32 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 1 |
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .datac... | 32 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ : Any = {'UserAgent': UserAgent().random}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> dict:
... | 32 | 1 |
UpperCAmelCase_ : Optional[Any] = 6_5521
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> int:
"""simple docstring"""
a_ : Dict = 1
a_ : Dict = 0
for plain_chr in plain_text:
a_ : List[Any... | 32 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Tuple = ['''image_processor''', '''tokenizer''']
snake_case__ : Union[str, Any] ... | 32 | 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
UpperCAmelCase_ : List[str] = '\\n@misc{chen2021evaluating,\n tit... | 32 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 1 |
from __future__ import annotations
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE_ ( __A : Callable[[int | float], int | float] , __A : int | float , __A : int | float , __A : int = 1_00 , ) -> float:
"""simple docstrin... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> Union[str, Any]:
"""simple docstring"""
a_ : Di... | 32 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | 1 |
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatas... | 32 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'... | 32 | 1 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Union[str, Any] , __A : int , __A : Optional[int] ) -> Tuple:
"""simple docstri... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 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
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[int] = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingfa... | 32 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
UpperCAmelCase_ : Tuple = collections.namedtuple('_Dat... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
... | 32 | 1 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configur... | 32 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 32 | 1 |
import math
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> bool:
"""simple docstring"""
a_ : Dict = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__A )
def SCREAMING_SNAKE_... | 32 |
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5:... | 32 | 1 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCAmelCase_ : Tuple = 5_0000
UpperCAmelCase_ : str = 5000
UpperCAmelCase_ , UpperCAmelCase_ : List[Any] = os.path.split(__file_... | 32 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 | 1 |
# Imports
import numpy as np
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : List[str]=None , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMIN... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 | 1 |
import re
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> list:
"""simple docstring"""
return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]' , str_ )]
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> str:
"""... | 32 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 32 | 1 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> list[int]:
"""simple docstring"""
if num <= 0:
a_ : str = F"""{num}: Invalid input, please enter a positive integer."""
rais... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 32 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
UpperCAmelCase_ : Union[str, Any] = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE__ ( ... | 32 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 1 |
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase_ : Union[str, Any] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def SCREAMING_SNAKE_CASE_ ( ) -> Optional[Any]:
"""simple docstring"""
a_ : str =... | 32 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 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 ... | 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
UpperCAmelCase_ : Any = namedtuple('covid_data', 'cases deaths recovered')
def SCREAMING_SNAKE_CASE_ ( __A : str = "https://www.worldometers.info/coronavirus/" ) -> c... | 32 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 1 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( lowercase__ , unittest.... | 32 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ : Any = {'UserAgent': UserAgent().random}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> dict:
... | 32 | 1 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoToke... | 32 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Tuple = ['''image_processor''', '''tokenizer''']
snake_case__ : Union[str, Any] ... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( ) -> int:
"""simple docstring"""
return 1
def SCREAMING_SNAKE_CASE_ ( __A : int ) -> int:
"""simple docstring"""
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def ... | 32 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : int = 1_00_00_00 ) -> int:
"""simple docstring"""
a_ : Union[str, Any] = limit + 1
a_ : Optional[Any] = [0] * limit
for first_term in range(1 , __A ):
for n in range(__A , __... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import ... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
import datasets
from .evaluate import evaluate
UpperCAmelCase_ : Optional[Any] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={a... | 32 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'... | 32 | 1 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionImg2ImgPipeline` instead.'
)
| 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 | 1 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tok... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 1 |
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] ) -> Dict:
"""simple docstring"""
a_ : Dict = [
'e... | 32 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[int] = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingfa... | 32 | 1 |
from ... import PretrainedConfig
UpperCAmelCase_ : Optional[Any] = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : str = NEZHA_PRETRAINED... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : int = 3 , __A : int = 7 , __A : int = 1_00_00_00 ) -> int:
"""simple docstring"""
a_ : Tuple = 0
a_ : Any = 1
for current_denominator in range(1 , limit + 1 ):
a_ ... | 32 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 32 | 1 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=lowercase__ ):
snake_case__ : List[str] = ['''torch''', '''transformers''', '''onnx''']
def __init__( self : List[Any] , *SCREAMING_SNAKE_CASE__ : ... | 32 |
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5:... | 32 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():... | 32 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 | 1 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
UpperCAm... | 32 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 32 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from .... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCAmelCase_ : Union[str, Any] = {
'configuration_x_clip': [
'XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'XCLIPConfig',
'XCLIPTextConfig',
... | 32 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
... | 32 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 1 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/m... | 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 1 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCAmelCase_ : List[str] = 'scheduler_config.json'
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
... | 32 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 1 |
from __future__ import annotations
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , SCREAMING_SNAKE_CASE__ : int ) -> None:
a_ : int = num_of_nodes
a_ : list[list[int]] = ... | 32 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ : Any = {'UserAgent': UserAgent().random}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> dict:
... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
UpperCAmelCase_ : Tuple = {'tokenization_bertweet': ['BertweetTokenizer']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
UpperCAmelCase_ : int = ... | 32 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Tuple = ['''image_processor''', '''tokenizer''']
snake_case__ : Union[str, Any] ... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 1 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Any ... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKA... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
s... | 32 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class ... | 32 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'... | 32 | 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 SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Union... | 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 | 1 |
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
Ju... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 1 |
import random
def SCREAMING_SNAKE_CASE_ ( __A : List[Any] , __A : Optional[int] , __A : List[str] ) -> Optional[Any]:
"""simple docstring"""
a_ : List[str] = a[left_index]
a_ : int = left_index + 1
for j in ... | 32 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[int] = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingfa... | 32 | 1 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[Any] = {
'nielsr/canine-s': 2048,
}
# Unicode ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
... | 32 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor, B... | 32 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 32 | 1 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def SCREAMING_SNAKE_CASE_ ( __A : List[str] ) -> Optional[... | 32 |
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5:... | 32 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_en... | 32 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE__ :
@property
def SCREAMING_SNAKE_CASE ... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 | 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
@... | 32 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 32 | 1 |
import json
import os
import tempfile
from transformers.testing_utils import check_json_file_has_correct_format
class SCREAMING_SNAKE_CASE__ :
snake_case__ : int = None
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Tuple:
... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 32 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'... | 32 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
... | 32 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 1 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case_... | 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer... | 32 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
a_ : Optional[Any] = str(bin(__A ) )[2:] # remove the le... | 32 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ : Any = {'UserAgent': UserAgent().random}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> dict:
... | 32 | 1 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
UpperCAmelCase_ : Optional[int] = datasets.load_iris()
UpperCAmelCase_ : int = np.array(data['data'])
UpperCAmelCase_ : Optional[i... | 32 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Tuple = ['''image_processor''', '''tokenizer''']
snake_case__ : Union[str, Any] ... | 32 | 1 |
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCAmelCase_ : Optional[int] = '<<<<<<< This should probably be modified because it mentions: '
Upp... | 32 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 1 |
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__A ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('doctes... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # noqa: F401 # Here to have ... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface... | 32 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 | 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
... | 32 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase_ : str = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
'... | 32 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ ... | 32 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTester... | 32 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_t... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : int = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_... | 32 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ : Optional[int] = {
'facebook/mask2former-swin-small-coco-instance': (
'https://huggingfa... | 32 | 1 |
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : Any ) -> Optional[int]:
"""simple docstring"""
a_ : Any ... | 32 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[str, Any] = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( ) -> list[list[int]]:
"""simple docstring"""
return [list(range(10_00 - i , -10_00 - i , -1 ) ) for i in range(10_00 )]
UpperCAmelCase_ : Dict = generate_large_matrix()
UpperCAmelCase_ : List[Any] = ... | 32 |
# Copyright 2023 The HuggingFace Inc. 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 re... | 32 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def __init__( self : Optional[int] , ... | 32 |
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5:... | 32 | 1 |
from manim import *
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
a_ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
a_ : List[Any] = R... | 32 |
import math
import flax.linen as nn
import jax.numpy as jnp
def SCREAMING_SNAKE_CASE_ ( __A : jnp.ndarray , __A : int , __A : float = 1 , __A : float = 1 , __A : float = 1.0e4 , __A : bool = False , __A : float = 1.0 , ) -> jnp.ndarray:
... | 32 | 1 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMa... | 32 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
UpperCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase_ ... | 32 | 1 |
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> bool:
"""simple docstring"""
return credit_card_number.startswith(('34', '35', '37', '4', '5', '6') )
def SCREAMING_SNAKE_CASE_ ( __A : str ) -> bool:
"""simple docstring"""
... | 32 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@requ... | 32 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql... | 32 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 32 | 1 |
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def SCREAMING_SNAKE_CASE_ ( *__A : str , __A : Optional[Union[Dict, Any]] = None , __A : Tuple=True , __A : int=2 ) -> Optional[Any]:
"""si... | 32 |
def SCREAMING_SNAKE_CASE_ ( __A : list[int] , __A : str ) -> list[int]:
"""simple docstring"""
a_ : Any = int(__A )
# Initialize Result
a_ : Tuple = []
# Traverse through all denomination
for denomination ... | 32 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
... | 32 |
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE__ ( nn.Module ):
snake_case__ : int
snake_case__ : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
... | 32 | 1 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_availabl... | 32 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
from .te... | 32 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
UpperCAmelCase_ : Optional[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : str ... | 32 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCAmelCase_ : List[str] = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_... | 32 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
UpperCAmelCase_ : Any = {'UserAgent': UserAgent().random}
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] ) -> dict:
... | 32 | 1 |
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Any ) -> Dict:
a_ : Any = data
a_ : str = None
class SCREAMING_SNAKE_CASE__ :
def __init__( ... | 32 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Tuple = ['''image_processor''', '''tokenizer''']
snake_case__ : Union[str, Any] ... | 32 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Any = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
... | 32 |
from __future__ import annotations
UpperCAmelCase_ : Tuple = []
def SCREAMING_SNAKE_CASE_ ( __A : list[list[int]] , __A : int , __A : int ) -> bool:
"""simple docstring"""
for i in range(len(__A ) ):
if boa... | 32 | 1 |
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
fro... | 32 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE_ ( ) -> Any:
"""simple docstring"""
a_ : Optional[Any] = HfArgumentParser(__A )
a_ : Optional[int] = parser.par... | 32 | 1 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
UpperCAmelCase_ :... | 32 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_... | 32 | 1 |
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
UpperCAmelCase_ : Optional[int] = 4
UpperCAmelCase_ : Dict = 3
class ... | 32 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import... | 32 | 1 |
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