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'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class SCREAMING_SNAKE_CASE__ ... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase=2_8_1_2_3 ) -> Tuple:
__a : int = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
sum_divs... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_scor... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm i... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''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, slo... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
import pytest
__SCREAMING_SNAKE_CASE : Any = '__dummy_dataset1__'
__SCREAMING_SNAKE_CASE : List[Any] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/mai... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase = 1_0 , lowercase = 1_0_0_0 , lowercase = True ) -> int:
assert (
isinstance(lowercase , lowercase )
and isinstance(lowercase , lowercase )
and isinstance(lowercase , lowercase ... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _snake_case ( lowercase ) -> List[Any]:
# A local function to see if a dot lands in the circle.
def is_in_circle(lowercase... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def _snake_case ( lowercase , lowercase ) -> Dict:
__a : Dict = k_... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
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 .toke... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
from functools import reduce
__SCREAMING_SNAKE_CASE : Tuple = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from tra... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def _snake_case ( lowercase ) -> List[Any]:
return DownloadCommand(args.model , args.cache_dir , args.force , args.trust_remote_code )
class SCREAMING_SNAK... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
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 AutoTok... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCH... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> int:
assert column_title.isupper()
__a : List[str] = 0
__a : List[str] = len(lowercase ) - 1
__a : List[Any] = 0
while index >= 0:
__a : U... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase = 1_0**9 ) -> int:
__a : Dict = 1
__a : Any = 2
__a : Optional[Any] = 0
__a : Tuple = 0
__a : Tuple = 0
while perimeter <=... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if ... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
from math import factorial, radians
def _snake_case ( lowercase , lowercase = 1_8 , lowercase = 1_0 ) -> float:
__a : Union[str, Any] = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
# Converting from degree... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTP... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = shutil.get_terminal_size()
__SCREAMING_SNAKE_CASE : List[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class SCREAMING_... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import re
def _snake_case ( lowercase ) -> str:
if len(re.findall("""[ATCG]""" , lowercase ) ) != len(lowercase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if ... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_u... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
import argparse
import json
from tqdm import tqdm
def _snake_case ( ) -> Dict:
__a : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--src_path""" , type=lowercase , default... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
import os
from pathlib import Path
def _snake_case ( ) -> str:
from torch.utils.cpp_extension import load
__a : Union[str, Any] = Path(lowercase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
__a ... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cac... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase = 5_0 ) -> int:
__a : Union[str, Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( lowercase ) -> bool:
__a : Any = len(lowercase )
# We need to create solution object to save path.
__a : Union[str, Any] = [[0 for _ in range(lowercase )] for ... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''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 _snake_case ( lowercase , lowercas... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exp... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
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_float32 (there's also the fix_lavis branch)
# also note: to convert Vicu... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__SCREAMING_SNAKE_CASE : int = Lock()
def _snake_case ( lowercase , lowercase , lowercase , lowercase , ... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
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
... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
from typing import Dict, Optional
import numpy as np
import datasets
__SCREAMING_SNAKE_CASE : List[str] = '\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentat... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import random
from .binary_exp_mod import bin_exp_mod
def _snake_case ( lowercase , lowercase=1_0_0_0 ) -> List[str]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__a : List... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE__ ( metaclass=__UpperCamelCase ):
lowercase__ = ["transformers", "torch", "note_seq"]
def __init__( self , *__UpperCamelCase , **__UpperCamelCas... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_tor... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
from timeit import timeit
def _snake_case ( lowercase ) -> int:
if number < 0:
raise ValueError("""the value of input must not be negative""" )
__a : Union[str, Any] = 0
while number:
number &= number - 1
... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
from typing import Any
class SCREAMING_SNAKE_CASE__ :
def __init__( self , __UpperCamelCase ):
'''simple docstring'''
__a : List[Any] = data
__a : str = None
def __repr__( self ... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
def _snake_case ( lowercase , lowercase = None , lowercase = None ) -> None:
if start is None:
__a : Dict = 0
if end is None:
__a : List[str] = len(lowe... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
__SCREAMING_SNAKE_CASE : str = 3
def _snake_case ( lowercase ) -> int:
print("""Generating primitive root of p""" )
... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers i... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_tex... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> None:
__a : Optional[Any] = generate_pascal_triangle(lowercase )
for row_idx in range(lowercase ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=""... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE ... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase = 4_0_0_0_0_0_0 ) -> int:
__a : int = [0, 1]
__a : int = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : Any = {'configuration_encoder_decoder': ['Enco... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUEST... | 697 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase , lowercase ) -> Optional[Any]:
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(lowercase , n - 1 , lowercase ) * a) % mod
else:
__a ... | 697 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 1 |
'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...t... | 697 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase , lowercase=False ) -> int:
if isinstance(lowercase , lowercase ) and isinstance(lowercase , lowercase ):
__a : int = len(set_a.intersection(lowercase ) )
if a... | 697 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 1 |
'''simple docstring'''
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def ... | 697 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase ) -> list:
if any(not isinstance(lowercase , lowercase ) or x < 0 for x in sequence ):
raise TypeError("""Sequence must be list of non-negative integers""" )
for _ in range(len(lowercase ) ):
for i, (r... | 697 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 1 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__SCREAMING_SNAKE_CAS... | 697 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : L... | 697 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 1 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__SCREAMING_SNAKE_CASE : List[Any] = ... | 697 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 1 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, Sw... | 697 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTes... | 697 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 1 |
'''simple docstring'''
def _snake_case ( lowercase , lowercase ) -> str:
if not isinstance(lowercase , lowercase ):
raise ValueError("""iterations must be defined as integers""" )
if not isinstance(lowercase , lowercase ) or not number >= 1:
... | 697 |
'''simple docstring'''
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffuse... | 697 | 1 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MA... | 697 |
'''simple docstring'''
import numpy as np
from PIL import Image
def _snake_case ( lowercase , lowercase , lowercase ) -> np.ndarray:
__a : Any = np.array(lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError("""The input array i... | 697 | 1 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__SCREAMING_SNAKE_CASE : Optional[int] = object()
# For specifying empty leaf d... | 697 |
'''simple docstring'''
import qiskit
def _snake_case ( lowercase , lowercase ) -> qiskit.result.counts.Counts:
__a : Any = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__a : str ... | 697 | 1 |
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transfo... | 697 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFea... | 697 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 |
'''simple docstring'''
import warnings
from functools import wraps
from typing import Callable
def _snake_case ( lowercase ) -> Callable:
@wraps(lowercase )
def _inner_fn(*lowercase , **lowercase ):
warnings.warn(
(F"""'{fn.__name__}' is exp... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...tes... | 697 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Ba... | 697 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __... | 697 |
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = 9.80_665
def _snake_case ( lowercase , lowercase , lowercase = g ) -> float:
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise V... | 697 | 1 |
'''simple docstring'''
from __future__ import annotations
import matplotlib.pyplot as plt # type: ignore
import numpy
# initial triangle of Koch snowflake
__SCREAMING_SNAKE_CASE : Tuple = numpy.array([0, 0])
__SCREAMING_SNAKE_CASE : Any = numpy.array([0.5, 0.8_... | 697 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingS... | 697 | 1 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
cl... | 697 |
'''simple docstring'''
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate impor... | 697 | 1 |
'''simple docstring'''
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _snake_case ( lowercase , lowercase = True , lowercase = math.inf , lowercase = -math.inf , lowercase = math.inf , lowercase = -math.inf , ... | 700 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE... | 697 | 0 |
'''simple docstring'''
# flake8: noqa
# Lint as: python3
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
'VerificationMode',
'Version',
'disable_progress_bar',
'enable_progress_bar',
'is_progress_bar_enabled',
'experimental',
]
from .info_utils import Ve... | 701 |
'''simple docstring'''
def _snake_case ( lowercase ) -> bool:
if not isinstance(lowercase , lowercase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__a : str = str(lowercase )
__a : Any = """""".j... | 697 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDi... | 702 |
'''simple docstring'''
import argparse
import torch
from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _snake_case ( lowercase , lowercase , lowerca... | 697 | 0 |
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_avail... | 703 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline... | 697 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _snake_case ( lowercase ) -> None:
__a : List[Any] = analyze_text(_lowercase )
__a : Any = lis... | 704 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[str] = {
... | 697 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = 42
class SCREAMING_SNAKE_CASE__ :
def __i... | 705 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class SCREAMING_SNAKE_CASE__ ( __UpperCamelCase ):
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
'''simple docst... | 697 | 0 |
'''simple docstring'''
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class SCREAMING_SNAKE_CASE__ ( __lowerCamelCase ):
def __lowerCamelCase ( self , __UpperCamelCase ):
'''simple docstrin... | 706 |
'''simple docstring'''
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class ... | 697 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"facebook/s2t-small-librispeech-asr": (
... | 707 |
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_d... | 697 | 0 |
'''simple docstring'''
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
__SCREAMING_SNAKE_CASE : List[Any] = object()
# For specifying empty leaf dict ... | 708 |
'''simple docstring'''
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowerCamelCase ( self ):
'''simple ... | 697 | 0 |
'''simple docstring'''
def _snake_case ( lowercase ) -> int:
__a : Any = []
for data in source_data:
for i, el in enumerate(__lowerCAmelCase ):
if len(__lowerCAmelCase ) < i + 1:
data_lists.append([] )
... | 709 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__SCREAMING_SNAKE_CASE : List[str] = (
'4S 3H 2C 7S 5H',
'9D 8H 2C 6S 7H',
'2D 6D 9D TH 7D',
'TC 8C 2S JH 6C',
... | 697 | 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 SCREAMING_SNAKE_CASE__ :
lowercase__ = 42
lowercase__ = Non... | 710 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARC... | 697 | 0 |
'''simple docstring'''
import math
def _snake_case ( lowercase ) -> Any:
__a : Optional[int] = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_lowerCAmelCase )
def _snake_case ( lowercase = 1 / 1_2_3_4_5 ... | 711 |
'''simple docstring'''
from __future__ import annotations
import bisect
def _snake_case ( lowercase , lowercase , lowercase = 0 , lowercase = -1 ) -> int:
if hi < 0:
__a : Union[str, Any] = len(lowercase )
while lo < hi:
... | 697 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__SCREAMING_SNAKE_CASE : Optional[Any] = [
"EAGER",
"AOT_EAGER",
"INDUCTOR",... | 712 |
'''simple docstring'''
from itertools import product
def _snake_case ( lowercase , lowercase ) -> list[int]:
__a : Optional[int] = sides_number
__a : Union[str, Any] = max_face_number * dice_number
__a : Optional[Any] ... | 697 | 0 |
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