code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import inspect
import unittest
from transformers import DecisionTransformerConfig, 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
fro... | 706 | """simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : List[str] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 707 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : int = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTe... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ) -> Any: # noqa: E741
_lowerCAmelCase : Optional[int] = len(_snake_case )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Tuple = [0] * n
_lowerCAmelCase : List[str] ... | 708 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 663 | 0 |
"""simple docstring"""
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_u... | 709 | """simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_Upp... | 663 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
_a : Optional[Any] = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
'layers_per_block': 2,
'num_cla... | 710 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 0 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVec... | 711 | """simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> Any:
_lowerCAmelCase : str = len(__snake_case )
for i in range(length - 1 ):
_lowerCAmelCase : Dict = i
for k in range(i + 1 ,__snake_case ):
if collection[k] < collect... | 712 | """simple docstring"""
_a : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a :... | 663 | 0 |
"""simple docstring"""
import json
import sys
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ,_lowerCamelCase : List[str] ) -> Union[str, Any]:
with open(lowerCamelCase_ ,encoding="""utf-8""" ) as f:
_lowerCAmelCase : Union[str, Any] = json.load(lowerCamelCase_ ... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
class __A :
def __init__( self , a__ , a__ ):
_lowerCAmelCase : Optional[int] = name
_lowerCAmelCase : Tuple = val
def __str__( self ):
return F"{self.__class__.__name__}({self.name}, {self.val})"... | 714 | """simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 663 | 0 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_a : Dict = logging.get_logger(__name__)
_a : Dict = {
'vocab_file': 'vocab.json',
'me... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 663 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : List[str] = {
'microsoft/xprophetnet-large-wiki100-cased': (
'https://huggingf... | 716 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 663 | 0 |
"""simple docstring"""
from typing import Any
import numpy as np
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : np.ndarray ) -> bool:
return np.array_equal(snake_case__ ,matrix.conjugate().T )
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : np.ndarray ,_lowerCamelCase : ... | 717 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 663 | 0 |
"""simple docstring"""
import re
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict ) -> bool:
_lowerCAmelCase : List[str] = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(_lowerCamelCase ,_lowerCa... | 718 | """simple docstring"""
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Any ) -> List[Any]:
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Optional[int] = (
f"curl -H \"Accept: applic... | 663 | 0 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerC... | 719 | """simple docstring"""
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 OptionalDepen... | 663 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):... | 720 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : List[str] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,_lowerCamelCase ):... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : list[list[float]] ) -> Optional[int]:
_lowerCAmelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_lowerCamelCase ):
if len(_lowerCamelCase ) < i + 1:
data_lists.appe... | 721 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 663 | 0 |
"""simple docstring"""
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List,... | 700 | """simple docstring"""
from PIL import Image
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ,_lowerCamelCase : int ) -> Image:
_lowerCAmelCase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int ) -> int:
return int(128 + facto... | 663 | 0 |
"""simple docstring"""
class __A :
def __init__( self , a__ ):
_lowerCAmelCase : Union[str, Any] = n
_lowerCAmelCase : List[str] = [None] * self.n
_lowerCAmelCase : Optional[int] = 0 # index of the first element
_lowerCAmelCas... | 701 | """simple docstring"""
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A :
def __init__( self ):
_lowerCAmelCase : Union[str, Any] = [
[],
[],
[],
... | 663 | 0 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class __A ( metaclass=lowerCamelCase__ ):
_UpperCamelCase : int = ["transformers", "torch", "note_seq"]
def __init__( self , *a__ , **a__ ):
requires_backends(self , [... | 702 | """simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 663 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_toke... | 703 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPM... | 663 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
_a : Dict = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/c... | 704 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.util... | 663 | 0 |
"""simple docstring"""
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 SCREAMING_SNAKE_CASE ... | 705 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A ( SCREAMING_... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_a : Optional[int] = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARC... | 706 | """simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 663 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_o... | 707 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : int = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTe... | 663 | 0 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : str = "cpu" ,_lowerCamelCase : Union[str, None] = None ) -> None:
_lowerCAmelCase : int = torch.load(_lowerCam... | 708 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 663 | 0 |
"""simple docstring"""
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 ... | 709 | """simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_Upp... | 663 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_a : List[Any] = datasets.ut... | 710 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int] ) -> Dict:
_lowerCAmelCase : str = len(_lowerCamelCase )
while cur > 1:
# Find the maximum number in arr
_lowerCAmelCase : Any = arr.index(max(arr[0:cur] ) )
# Reverse from 0... | 711 | """simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 663 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
_a : Union[str, Any] = logging.get_l... | 712 | """simple docstring"""
_a : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a :... | 663 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGE... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
... | 714 | """simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 663 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
_a : List[Any] = list[tuple[int, int]]
_a : str = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0,... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 663 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeC... | 716 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : List[Any] ) -> Optional[Any]:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_... | 717 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 663 | 0 |
"""simple docstring"""
# Logistic Regression from scratch
# In[62]:
# In[63]:
# importing all the required libraries
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ) -> Union[str, Any]:
return... | 718 | """simple docstring"""
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Any ) -> List[Any]:
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Optional[int] = (
f"curl -H \"Accept: applic... | 663 | 0 |
"""simple docstring"""
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCase : List[Any] = [
'''safety_checker/pytorch_model.bin''',
'''... | 719 | """simple docstring"""
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 OptionalDepen... | 663 | 0 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
_a : Optional[Any] = namedtuple('covid_data', 'cases deaths recovered')
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] = "https://www.worldomete... | 720 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : List[str] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,_lowerCamelCase ):... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ) -> list[int]:
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
_lowerCAmelCase : Optional[int] = [True] * (num + 1)
_lowerCAmelCase : Union[str, Any] = 2
... | 721 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_a : Optional[Any] = {
'configuration_whisper': ['WHISPER_PRETRAINE... | 700 | """simple docstring"""
from PIL import Image
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ,_lowerCamelCase : int ) -> Image:
_lowerCAmelCase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int ) -> int:
return int(128 + facto... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
_a : Optional[Any] = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 701 | """simple docstring"""
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A :
def __init__( self ):
_lowerCAmelCase : Union[str, Any] = [
[],
[],
[],
... | 663 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_... | 702 | """simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class __A :
def __init__( self , a__ = 6 ):
_lowerCAmelCase : Node | None = None
_lowerCAmelCase : Node | None = None
self.create_linked_list(a__ )
... | 703 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPM... | 663 | 0 |
"""simple docstring"""
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 OptionalDepen... | 704 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.util... | 663 | 0 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 705 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A ( SCREAMING_... | 663 | 0 |
"""simple docstring"""
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Any ,_lowerCamelCase : List[Any] ,_lowerCamelCase : List[str]=1... | 706 | """simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 663 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class __A ( SCREAMING_SNAKE_CASE_ ):
@require_torch
def __A ( self ):
... | 707 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : int = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTe... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase = 42
_UpperCamelCase = None
_UpperCamelCase = None
... | 708 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 663 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ,_lowerCamelCase : int = 10 ) -> int:
_lowerCAmelCase : defaultdict = defaultdict(_lowerCamelCase )
for outer_width in range(3 ... | 709 | """simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_Upp... | 663 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokeniza... | 710 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ,_lowerCamelCase : int ,_lowerCamelCase : int ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
_lowerCAmelCase : Dict = _modexpt(_lowerCamelCase ,exponent // 2 ,_lowerCamelCase ... | 711 | """simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 663 | 0 |
"""simple docstring"""
import warnings
from typing import Dict, List, Optional, Tuple
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelC... | 712 | """simple docstring"""
_a : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a :... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
_a : Dict = 8.9_88e9 # units = N * m^s * C^-2
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : float ,_lowerCamelCase : float ,_lowerCamelCase : float ,_lowerCamelCase : float ) -> dict[str, float]:
_lowerCAmelCase... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
import json
import os
import unittest
from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Tokenizer... | 714 | """simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 663 | 0 |
"""simple docstring"""
from collections import defaultdict
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : str ) -> bool:
_lowerCAmelCase : List[Any] = first_str.lower().strip()
_lowerCAmelCase : Union[str, Any] = second_str.lower().strip()
#... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 663 | 0 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 716 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : list ,_lowerCamelCase : list ,_lowerCamelCase : int ) -> int:
if len(_lowerCamelCase ) != len(_lowerCamelCase ):
raise ValueError("""The length of profit and weight must be same.""" )
if max_weight <= 0:
ra... | 717 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 663 | 0 |
"""simple docstring"""
_a : dict[str, float] = {
"joule": 1.0,
"kilojoule": 1_000,
"megajoule": 1_000_000,
"gigajoule": 1_000_000_000,
"wattsecond": 1.0,
"watthour": 3_600,
"kilowatthour": 3_600_000,
"newtonmeter": 1.0,
"calorie_nutr": 4_186.8,
"kilocalorie_nutr": ... | 718 | """simple docstring"""
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Any ) -> List[Any]:
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Optional[int] = (
f"curl -H \"Accept: applic... | 663 | 0 |
"""simple docstring"""
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
... | 719 | """simple docstring"""
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 OptionalDepen... | 663 | 0 |
"""simple docstring"""
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
... | 720 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : List[str] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,_lowerCamelCase ):... | 663 | 0 |
"""simple docstring"""
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.t... | 721 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 663 | 0 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_a : int = Lock()
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : str ,_lowerCamelCase : List[str... | 700 | """simple docstring"""
from PIL import Image
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ,_lowerCamelCase : int ) -> Image:
_lowerCAmelCase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int ) -> int:
return int(128 + facto... | 663 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 701 | """simple docstring"""
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A :
def __init__( self ):
_lowerCAmelCase : Union[str, Any] = [
[],
[],
[],
... | 663 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
s... | 702 | """simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 663 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Union[str, Any] = logging.get_logger(__name__)
_a : int = {
'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
# See all PEGASUS ... | 703 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPM... | 663 | 0 |
"""simple docstring"""
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 It... | 704 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.util... | 663 | 0 |
"""simple docstring"""
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class __A :
def __A ( self , a__ ):
raise NotImplementedError()
def __A ( self ):
... | 705 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A ( SCREAMING_... | 663 | 0 |
"""simple docstring"""
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from t... | 706 | """simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 663 | 0 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
_a : str = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth. For binary (two classes) ... | 707 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : int = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTe... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Union[str, Any] ) -> List[Any]:
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
i... | 708 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 663 | 0 |
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
_a : Union[str, Any] = ''
_a : Optional[Any] = ''
_a : List[str] = ''
_a : Optional[Any] = 1 # (0 is vertical, 1 is horizontal)
def SCREAMING_SNA... | 709 | """simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_Upp... | 663 | 0 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def SCREAMING_SNAKE_CASE ( _lowerCamelCase ... | 710 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils i... | 711 | """simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 663 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Optional[Any] = ["image_processor", "tokenizer"]
_UpperCamelCase : Any = "AutoIma... | 712 | """simple docstring"""
_a : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a :... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
_a : Union[str, Any] = tuple[int, int]
class __A :
def __init__( self , a__ , a__ ):
_lowerCAmelCase : set[int] = vertices
_lowerCAmelCa... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 714 | """simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : str ,_lowerCamelCase : str ) -> float:
def get_matched_characters(_lowerCamelCase : str ,_lowerCamelCase : str ) -> str:
_lowerCAmelCase : Any = []
_lowerCAmelCase : List[Any] = min(len(_s... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 663 | 0 |
"""simple docstring"""
import os
import re
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 logging
_a : Optional[int] = logging.get_logger(__name__)
_a ... | 716 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 663 | 0 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_a : Optional[Any] = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_token... | 717 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 663 | 0 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_bac... | 718 | """simple docstring"""
import argparse
import json
import subprocess
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ,_lowerCamelCase : Any ) -> List[Any]:
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Optional[int] = (
f"curl -H \"Accept: applic... | 663 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : int = ["image_processor", "tokenizer"]
_UpperCamelCase : Dict = "Chine... | 719 | """simple docstring"""
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 OptionalDepen... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : List[str] = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['Ta... | 720 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1000000 ) -> int:
_lowerCAmelCase : List[str] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,_lowerCamelCase ):... | 663 | 0 |
"""simple docstring"""
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A :
def __init__( self ):
_lowerCAmelCase : Union[str, Any] = [
[],
[],
[],
... | 721 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 663 | 0 |
"""simple docstring"""
_a : Any = {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
_a : Any = ['a', 'b', 'c', 'd', 'e']
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Optional[Any] ,_lowerCamelCase : Dict ) -> int:
_l... | 700 | """simple docstring"""
from PIL import Image
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Image ,_lowerCamelCase : int ) -> Image:
_lowerCAmelCase : Any = (259 * (level + 255)) / (255 * (259 - level))
def contrast(_lowerCamelCase : int ) -> int:
return int(128 + facto... | 663 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 701 | """simple docstring"""
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A ( SCREAMING_SNAKE_CASE_ ):
pass
class __A :
def __init__( self ):
_lowerCAmelCase : Union[str, Any] = [
[],
[],
[],
... | 663 | 0 |
"""simple docstring"""
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Dict = "EncodecFeatureExtractor"
_UpperCamelCase : Optional[i... | 702 | """simple docstring"""
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_si... | 663 | 0 |
"""simple docstring"""
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class __A ( nn.Module ):
def __init__( self , a__ = 16 , a__ = 88 , a__ = None , a__ = 1 , a__ = 0.0 , a_... | 703 | """simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPM... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class __A ( SCREAMING_SNAKE_CASE_ ):
_UpperCamelCase : Any = "S... | 704 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.util... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 705 | """simple docstring"""
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class __A ( SCREAMING_... | 663 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Tuple ,_lowerCamelCase : List[str] ,_lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Dict ) -> List[... | 706 | """simple docstring"""
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common imp... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : Tuple = {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}
try:
if not is_torch_availabl... | 707 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_a : int = {
'configuration_altclip': [
'ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'AltCLIPConfig',
'AltCLIPTe... | 663 | 0 |
"""simple docstring"""
import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeC... | 708 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : int ) -> bool:
return sum(i for i in range(1 ,number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('Program to check whether a number is a Perfect number or not...')
_a : int ... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : Tuple = {'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}
try:
if not is_torch_available():
raise OptionalDepe... | 709 | """simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __A :
_UpperCamelCase : int
_UpperCamelCase : Node | None = None
_Upp... | 663 | 0 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_a : List[Any] = logging.get_logger(__name__)
# TODO: upload to AWS
_a : Dict = {
'yjernite/retribert-base-uncased': (
'https://huggingface.co/yjernite/retribert-base-uncase... | 710 | """simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __A ( unittest.TestCase ):
def __A ( self ):
_lowerCAmelCa... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
_a : Optional[int] = list[list[float | int]]
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Matrix ,_lowerCamelCase : Matrix ) -> Matrix:
_lowerCAmelCase : int = len(_lowerCam... | 711 | """simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Union[str, Any] ,_lowerCamelCase : Tuple ) -> Dict:
_lowerCAmelCase : List[str] = int(_lowerCamelCase )
assert noofclusters < len(... | 663 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class __A ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
@register_to... | 712 | """simple docstring"""
_a : Optional[Any] = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'
_a :... | 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : List[str] = {
'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}
try:
if not is_torch_availab... | 713 | """simple docstring"""
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... | 663 | 0 |
"""simple docstring"""
import numpy
class __A :
def __init__( self , a__ , a__ ):
_lowerCAmelCase : int = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is th... | 714 | """simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
' StableDiffusionInpaintPipeline` instead.'
)
| 663 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 715 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAv... | 663 | 0 |
"""simple docstring"""
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_sp... | 716 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils imp... | 663 | 0 |
"""simple docstring"""
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : float ,_lowerCamelCase : float ,_lowerCamelCase : float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument m... | 717 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_a : Tuple = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ):
def __init__( self , *a__ , **a__ ):
warnin... | 663 | 0 |
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