code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
a = {
'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'S... | 412 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a = logging.get_logger(__name__)
@add_end_docstring... | 412 | 1 |
'''simple docstring'''
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__snake_case = {
"""facebook/mask2former-swin-small-coco-instance""": (
"""https://huggingface.co/facebook/... | 603 | '''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 (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_d... | 603 | 1 |
"""simple docstring"""
import os
from typing import List, Optional, Union
from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging
a = logging.get_logger(__name__)
a = {'''vocab_file''': '''vocab.txt'''... | 7 |
'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 507 | 0 |
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('''repo_id''' , ['''canonical_dataset_name''', '''org-name/dataset-name'''] )
@pytest.mark.parametrize('''path''' , ['''filename.csv''', '''filename with blanks.csv'''] ... | 701 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_UpperCAmelCase : List[str] = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_A... | 288 | 0 |
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAttention,
TFBase... | 352 |
import requests
_lowerCamelCase : int = """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""
def __a ( __lowerCAmelCase ) -> None:
# fetching a list of articles in json format
SCREAMING_SNAKE_CASE : List[str] = requests.g... | 352 | 1 |
def _a ( lowerCamelCase__ ) -> list:
if bit_count < 0:
raise ValueError('The given input must be positive' )
# get the generated string sequence
lowerCamelCase_ : int = gray_code_sequence_string(__A )
#
# convert them to integers
for i in rang... | 708 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json''',
# See all GPTNeoX models at https:... | 144 | 0 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
SCREAMING_SNAKE_CASE : Dict = Mapping[str, np.ndarray]
SCREAMING_SNAKE_CASE : int = Mapping[str, Any] #... | 635 | import numpy as np
import datasets
SCREAMING_SNAKE_CASE : Optional[int] = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt ... | 635 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
Uppe... | 716 |
"""simple docstring"""
def __lowercase ( a : str , a : str ) -> str:
__snake_case : int =len(a )
__snake_case : int =len(a )
__snake_case : int =(
first_str_length if first_str_length > second_str_length else sec... | 497 | 0 |
UpperCamelCase__ = 'Input must be a string of 8 numbers plus letter'
UpperCamelCase__ = 'TRWAGMYFPDXBNJZSQVHLCKE'
def UpperCamelCase__ ( UpperCAmelCase_ ) -> bool:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ... | 322 |
from typing import Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_... | 322 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''huggingface/time-series-transformer-tourism-monthly''': (
'''... | 494 |
"""simple docstring"""
import sys
lowerCAmelCase_ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 494 | 1 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import ... | 19 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples ... | 67 | 0 |
'''simple docstring'''
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.u... | 609 |
'''simple docstring'''
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
UpperCAmelCase : List... | 609 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a__ : Any = {
"""configuration_convbert""": ["""CONVBERT_PRETRAINED_CONF... | 589 |
"""simple docstring"""
import numpy as np
def A__ ( __lowerCamelCase ):
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 589 | 1 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _UpperCamelCase ( ):
lowercase_ : str = ArgumentParser(
description=(
... | 438 | '''simple docstring'''
from __future__ import annotations
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
# preprocessing the first row
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
... | 438 | 1 |
import warnings
from functools import wraps
from typing import Callable
def __SCREAMING_SNAKE_CASE ( a__ : Callable ) -> Callable:
@wraps(a__ )
def _inner_fn(*a__ : Union[str, Any] ,**a__ : Any ):
warnings.warn(
(f"""'{fn.__name__}' is experimental and... | 17 |
from __future__ import annotations
import math
def _a ( lowerCAmelCase )-> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
... | 360 | 0 |
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {"vocab_file": "vocab.json"}
SCREAMING_SNAKE_CASE__ = {
"vocab_file":... | 718 |
def lowercase ( a ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE_ :set[int] = set()
return any(
node not in visited and depth_f... | 140 | 0 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE = [ord(letter) for let... | 94 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( snake_case : list )-> list:
'''simple docstring'''
UpperCAmelCase__ : List[str] = False
while is_sorted is False: # Until all the indices are traversed keep looping
UpperCAmelCase__ ... | 438 | 0 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeli... | 334 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
__A : Optional[Any] = logging.getLogger(__name__)
if __name__ == "__main__":
__A : int... | 334 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ():
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
__UpperCamelCase : Union[str, Any] = generate_large_matrix()
__UpperCamelCase : Tuple = (
[[4, 3, 2, -1], [3, 2, 1, -1... | 4 |
import sys
def __magic_name__ ( __lowerCAmelCase : str ) -> Union[str, Any]:
__lowerCamelCase = len(__lowerCAmelCase )
__lowerCamelCase = [[0 for x in range(__lowerCAmelCase )] for x in range(__lowerCAmelCase )]
__lowerCamelCase = ... | 298 | 0 |
"""simple docstring"""
a : Union[str, Any] = 8.3_14_45_98
def _UpperCamelCase ( _A , _A ) -> float:
"""simple docstring"""
if temperature < 0:
raise Exception("""Temperature cannot be less than 0 K""" )
if molar_mass <= 0:
raise Exception("... | 19 |
"""simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patc... | 19 | 1 |
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
def __lowerCamelCase (UpperCAmelCase__ ... | 403 | import math
from typing import Callable, List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_dif... | 403 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class UpperCAmelCase_ :
"""simple docstring"""
lowerCamelCase : Optional[Union[str, Path]] = None
lowerCamelCase : bool = False
lowerCamelCase : ... | 382 |
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusi... | 382 | 1 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, Attn... | 370 | '''simple docstring'''
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils impo... | 370 | 1 |
'''simple docstring'''
def a__ ( lowerCAmelCase__ ) -> bool:
UpperCAmelCase__ : Union[str, Any] = 0
for ch in input_str:
UpperCAmelCase__ : Optional[Any] = ord(lowerCAmelCase__ )
UpperCAmelCase__ : List[str] = pow(2... | 312 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/config.json''',
'... | 312 | 1 |
'''simple docstring'''
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def A__ ( A_ ) -> Optional[int]:
_lowercase = int(A_ )
_lowercase , ... | 497 |
'''simple docstring'''
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import versio... | 497 | 1 |
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import Conf... | 207 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basi... | 207 | 1 |
import argparse
import os
import re
import packaging.version
_lowerCamelCase = 'examples/'
_lowerCamelCase = {
'examples': (re.compile(r'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(r'^__version__\s+=\s+"([^"... | 144 |
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 import (
AutoencoderKL,
... | 144 | 1 |
def _UpperCamelCase ( UpperCamelCase_ : str ) -> list:
"""simple docstring"""
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(UpperCamelCase_ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctes... | 717 |
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
... | 365 | 0 |
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class a ( unittest.TestCase ):
'''simple docstring'''
... | 144 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
... | 144 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_... | 605 | '''simple docstring'''
import math
from numpy import inf
from scipy.integrate import quad
def snake_case__ ( _A: float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
return quad(_A , 0 , _A , args=(_A... | 605 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_a... | 56 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_... | 239 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from typing import Any
class lowerCAmelCase__ :
def __init__( self : str ) -> None:
"""simple docstring"""
lowerCamelCase_ : list[Any] = []
lowerCamelCas... | 702 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils i... | 418 | 0 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
fr... | 33 |
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import D... | 33 | 1 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
... | 714 |
'''simple docstring'''
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from data... | 656 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0 ):
"""simple docstring"""
lowercase__ = length or len(SCREAMING_SNAKE_CASE )
lowercase__ = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowercase__ , lowercase__ = l... | 43 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
fro... | 531 | 0 |
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 transformers import AutoTo... | 46 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self... | 46 | 1 |
def a_ ( lowerCAmelCase_ : float, lowerCAmelCase_ : float ):
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if __name__ == "_... | 53 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import... | 53 | 1 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase__ )
class _lowercase ( UpperCAmelCase__ ):
_UpperCAmelCase = field(default='''language-modeling''', ... | 32 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InstructBlipConfig''',
'''... | 32 | 1 |
"""simple docstring"""
import os
import sys
__UpperCAmelCase = os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
A... | 65 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"facebook/s2t-small-librispeech-asr": (
"https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config... | 155 | 0 |
'''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_text import VOCAB... | 331 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
Ada... | 331 | 1 |
'''simple docstring'''
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch("""socket.socket""" )
@patch("""builtins.open""" )
def _snake_case ( A_ : Union[str, Any] , A_ : Optional[int] ):
"""simple docstring"""
... | 577 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
... | 577 | 1 |
import inspect
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
import torch.utils.checkpoint
from ...models import UNetaDModel, VQModel
from ...schedulers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteSche... | 688 |
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class... | 688 | 1 |
"""simple docstring"""
import math
import unittest
def snake_case_ ( A_ : int ):
'''simple docstring'''
assert isinstance(A_, A_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 ... | 83 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTester... | 110 | 0 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase_ = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2... | 706 | #
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-g... | 476 | 0 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def lowerCAmelCase_ ( ) -> Optional[int]:
'''simple docstring'''
raise RuntimeError... | 486 | def lowerCAmelCase_ ( __A, __A ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase__ = [1]
for i in range(2, __A ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
... | 486 | 1 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_c... | 366 | """simple docstring"""
import os
from argparse import ArgumentParser
from typing import List
import torch.utils.data
from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node
__A = 4
__A = 3
class _snake_case ( a__ ):
pass
def lowercase_ ... | 366 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unle... | 420 | """simple docstring"""
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
... | 420 | 1 |
'''simple docstring'''
_lowerCamelCase = 9.8_0665
def _SCREAMING_SNAKE_CASE ( snake_case_ , snake_case_ , snake_case_ = g ):
if fluid_density <= 0:
raise ValueError("""Impossible fluid density""" )
if volume < 0:
raise ValueError("""Impossible Object volume""" ... | 572 |
'''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 ImageProcessingSavingTestMixin, ... | 572 | 1 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@re... | 237 |
"""simple docstring"""
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class snake_case_ ( a_ ):
def __init__( self , a_="" , a_="train" ):
assert os.path.isdir(a_ )
a_ : List[Any] = []
... | 237 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
_A = logging.get_logger(__name__)
_A = {
'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}
... | 701 | '''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENS... | 438 | 0 |
from __future__ import annotations
def lowercase__ ( A_: list[int] , A_: list[int] , A_: list[int] , A_: list[list[str]] , A_: int , ) -> None:
"""simple docstring"""
__UpperCAmelCase =len(A_ )
... | 68 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase__ ( unittest.TestCase ):
@pr... | 612 | 0 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import... | 271 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transform... | 271 | 1 |
"""simple docstring"""
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowercase ( __snake_case : Optional[Any] ):
return input_array.reshape((input_arr... | 231 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__A =True
except (ImportError, ModuleNotFoundError):
__A =False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def _UpperCamelC... | 407 | 0 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_... | 716 |
"""simple docstring"""
from __future__ import annotations
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A=None ) -> Tuple:
lowerCAmelCase_ :Optional[int] = data
lowerCAmelCase_ :List[Any] = None
... | 256 | 0 |
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common imp... | 208 |
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast... | 693 | 0 |
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_availabl... | 421 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : List[... | 421 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = (UnCLIPScheduler,)
def lowerCamelCase__ (self : Union[str, Any] , **_Upper... | 15 | import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__UpperCamelCase : List[Any] = (
'This metric will be removed from the li... | 248 | 0 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase : List[str] = logging.get_logger(__name__... | 707 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
... | 684 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class A_ ( UpperCAmelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Option... | 67 |
"""simple docstring"""
a : str = range(2, 20 + 1)
a : Optional[Any] = [10**k for k in range(ks[-1] + 1)]
a : dict[int, dict[int, list[list[int]]]] = {}
def lowercase__(A , A , A , A ) ->Any:
... | 218 | 0 |
from __future__ import annotations
def __UpperCamelCase ( lowerCAmelCase__ : int | str ):
__a : Any = str(lowerCAmelCase__ )
return n == n[::-1]
def __UpperCamelCase ( lowerCAmelCase__ : int = 1_0_0_0_0_0_0 ):
__a : A... | 326 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test... | 326 | 1 |
import inspect
import unittest
from transformers import ViTConfig
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
f... | 239 |
'''simple docstring'''
def __A ( a_ : list[list[float]] ):
lowerCAmelCase : list[list[float]] = []
for data in source_data:
for i, el in enumerate(a_ ):
if len(a_ ) < i + 1:
data_lists.append([] )
data_lists[i].append(float(a_ ... | 525 | 0 |
"""simple docstring"""
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=log... | 194 |
"""simple docstring"""
from typing import List
import jiwer
import jiwer.transforms as tr
from packaging import version
import datasets
from datasets.config import PY_VERSION
if PY_VERSION < version.parse('3.8'):
import importlib_metadata
else:
import importlib.metadata as impo... | 194 | 1 |
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def UpperCamelCase ( _a ) -> tuple:
'''simple d... | 257 |
from typing import Any
import numpy as np
def UpperCamelCase ( _a ) -> bool:
'''simple docstring'''
return np.array_equal(_a , matrix.conjugate().T )
def UpperCamelCase ( _a , _a ) -> Any:
'''simple docstring... | 257 | 1 |
'''simple docstring'''
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowercase : Optional[int] ... | 162 |
'''simple docstring'''
import numpy as np
from transformers import Pipeline
def A (__lowerCamelCase :Any ):
_lowerCAmelCase = np.max(__lowerCamelCase , axis=-1 , keepdims=__lowerCamelCase )
_lowerCAmelCase = np.exp(outputs - maxes )
return shifted_exp / shifted... | 162 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
"configuration_speech_to_text":... | 205 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
... | 205 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow... | 317 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.f... | 317 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
f... | 618 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ :Any = logging.get_logger(__name__)
lowerCAmelCase__ :Union[str, Any] = {
'''facebook/xmod-ba... | 618 | 1 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class l... | 66 |
"""simple docstring"""
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowerCA... | 66 | 1 |
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
__lowerCAmelCase : Optional[int] = object()
# For specifying empty leaf dict `{}`
__lowerCAmelCase : ... | 509 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowerCAmelCase = {'configuration_deit': ['DEIT_PRETRAINED_CON... | 466 | 0 |
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
snake_case : Optional[int]... | 339 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
snake_case : Union[str, Any] = logging.get_logger(__name__)
snake_case : List[Any] ... | 339 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Tuple = {
"""YituT... | 352 |
from datetime import datetime
import requests
def __a ( __lowerCAmelCase ) -> bytes:
SCREAMING_SNAKE_CASE : int = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
SCREAMING_SNAKE_CASE : Any = requests.get(base_url +... | 352 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowerCamelCase_ ( lowerCAmelCase: str , lowerCAmelCase: str , **lowerCAmelCase: Tuple )-> str:
_snake_case : List[str] = AutoConfig.from_pretrained(lowerCAmelCase , ... | 718 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
lowerCAmelCase_ = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
... | 669 | 0 |
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__a :Tuple = '\\n@misc{chen2021evaluating,\n title={Evaluating Large Langu... | 86 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE__ ( snake_case__ :int ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples ... | 67 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class lowerCamelCase_ ( snake_case_ ):
def __init__( self : Union[str, Any] , lowerCAmelCase__ : str , lowerCAmelCase__ ... | 464 |
'''simple docstring'''
from bisect import bisect
from itertools import accumulate
def UpperCAmelCase ( A : Tuple , A : Optional[Any] , A : Dict , A : Any ):
SCREAMING_SNAKE_CASE : List[str] = sorted(zip(A , A ) , key... | 464 | 1 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase = 1 , UpperCamelCase = 1000 ) -> int:
"""simple docstring"""
__UpperCAmelCase : int = 1
__UpperCAmelCase : Tuple = 0
for divide_by_number in range(U... | 77 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokeni... | 77 | 1 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCAmelCase_ ( __lowercase : int ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = [
"""encoder... | 715 |
'''simple docstring'''
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipel... | 119 | 0 |
def __lowerCAmelCase ( _UpperCamelCase ) -> int:
'''simple docstring'''
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __lowerCAmelCase ( _UpperCamelCase ) -> bool:
'''simple docstring'''
lowerCamel... | 306 |
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 ...test_modeling_tf_common import TFMode... | 306 | 1 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__( __lowercase , unittest.TestCase ):
... | 712 |
from __future__ import annotations
import requests
def lowerCamelCase__ (__lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Dict = f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
return requests.get(__lowerCamelCase ).json()
def lowerCa... | 381 | 0 |
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=snake_case__ ):
"""simple docstring"""
__magic_name__ = ['torch']
def __init__( self , *__snake_case , **__snake_case ):
requires_backends(self ... | 550 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class _UpperCAmelCase ( nn.Module ):
__lowerCamelCase: int
__lowerCamelCase: jnp.dtype = jnp.floataa
def lowerCAmelCase__ ( self : Optional[Any] ):
... | 620 | 0 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
UpperCamelCase__ = '''\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex ... | 143 |
import qiskit
def UpperCAmelCase__ ( _A , _A ):
"""simple docstring"""
a_ = qiskit.Aer.get_backend('''aer_simulator''' )
a_ = qiskit.QuantumCircuit(4 , 2 )
# encode inputs in qubits 0 and 1
if bita == 1:
qc_ha.x(... | 143 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu... | 359 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
'nielsr/canine-s': 20_48,
}
# Unicode defines 1,114,112 total “codepoints”
low... | 511 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : int, UpperCAmelCase__ : int ) ->str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
A__ : str = str(bin(UpperCAmelCase__ ... | 498 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCAmelCase__ : int, UpperCAmelCase__ : int ) ->str:
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
A__ : str = str(bin(UpperCAmelCase__ ... | 498 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
SCREAMING_SNAKE_CASE__ : int =logging.get_logger(__name__)
class _UpperCAmelCase ( a_ ):
"""simple docstring"""
def _... | 434 | """simple docstring"""
SCREAMING_SNAKE_CASE__ : int =6_5521
def UpperCamelCase ( SCREAMING_SNAKE_CASE_ ) ->int:
_lowerCamelCase : Union[str, Any] = 1
_lowerCamelCase : List[str] = 0
for plain_chr in plain_text:
_lowerCamelCase : Di... | 434 | 1 |
import argparse
import torch
from datasets import load_dataset
from donut import DonutModel
from transformers import (
DonutImageProcessor,
DonutProcessor,
DonutSwinConfig,
DonutSwinModel,
MBartConfig,
MBartForCausalLM,
VisionEncoderDecoderModel,
XLMRobertaTokenizerFast,
)
de... | 96 |
def _A (UpperCamelCase : int , UpperCamelCase : int ) ->int:
'''simple docstring'''
while b:
lowerCamelCase__ ,lowerCamelCase__ : int = b, a % b
return a
def _A (UpperCamelCase : int , UpperCamelCase : int ) ->... | 96 | 1 |
"""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 OptionalDepe... | 129 | '''simple docstring'''
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class __A :
a__ : int
a__ : TreeNode | None = None
a__ : TreeNode | None = None
SCREAMING_SNAKE_CASE_: Union[str, Any] =namedtu... | 78 | 0 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_availab... | 465 |
'''simple docstring'''
def A_ ( snake_case = 100 ):
SCREAMING_SNAKE_CASE:Dict = 0
SCREAMING_SNAKE_CASE:Optional[int] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squares
if __name__ =... | 465 | 1 |
"""simple docstring"""
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_incr... | 102 |
def lowerCAmelCase_ (lowerCAmelCase__: Union[str, Any] , lowerCAmelCase__: Optional[int] ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 0
UpperCAmelCase_: Tuple = len(lowerCAmelCase__ ) - 1
while left <= right:
# avoid d... | 556 | 0 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : List[str] ... | 705 |
from __future__ import annotations
from typing import Any
class lowerCamelCase_ :
def __init__( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase = 0 ):
"""simple docstring"""
__magic_name__ , __magic_name__ :Any ... | 180 | 0 |
"""simple docstring"""
from collections.abc import Generator
def snake_case ( ) -> Generator[int, None, None]:
_snake_case , _snake_case = 0, 1
while True:
_snake_case , _snake_case = b, a + b
yield b
def snake_case ( lower... | 103 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __lowerCAmelCase ( __snake_case = "" ):
__lowerCAmelCase = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
__lowerCAmelCase = BeautifulSoup(re... | 367 | 0 |
'''simple docstring'''
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models im... | 704 |
'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase : str = logging.get_logger(__name__)
# TODO: upload to AWS
lowercase : Optional[Any] = {
'yjernite/retribert-base-uncased': (
'https://... | 343 | 0 |
"""simple docstring"""
import heapq
def __magic_name__ ( UpperCamelCase : dict ) -> set[int]:
a__ = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq work... | 273 |
"""simple docstring"""
def __magic_name__ ( UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float , ) -> float:
a__ = [redshift, radiation_density, matter_densit... | 273 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 291 |
from __future__ import annotations
lowerCamelCase__ = list[list[int]]
# assigning initial values to the grid
lowerCamelCase__ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0... | 291 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class UpperCAmelCase_ ... | 76 |
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
a_ = logging.getLogger(__name__)
if... | 76 | 1 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipel... | 107 | import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
... | 107 | 1 |
def SCREAMING_SNAKE_CASE ( UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ,UpperCAmelCase__ ):
"""simple docstring"""
if index == number_of_items:
return 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAK... | 605 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
... | 605 | 1 |
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
UpperCamelCase_ = logging.get_logger(__name__)
class a ( __UpperCAmelCase ):
def __init__( self : List[Any] , *snake_case__ : Optional[int] , ... | 376 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
UpperCamelCase_ = pytest.mark.integration
@pytest.mark.parametrize("path" , ["paws", "csv"] )
de... | 376 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_snake_case : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 693 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def _A ( __snake_c... | 693 | 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 transformers.image_u... | 302 |
"""simple docstring"""
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
lowerCamelCase_ : Union[str, Any] = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by de... | 302 | 1 |
'''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.ut... | 421 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__magic_name__ = 1.054_571_817E-34 # unit of ℏ : J * s
__magic_name__ = 3E8 # unit of c : m * s^-1
def __magic_name... | 250 | 0 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _lowercase ( __UpperCAmelCase ):
def __i... | 711 |
"""simple docstring"""
from unittest.mock import Mock, patch
from file_transfer.send_file import send_file
@patch('socket.socket' )
@patch('builtins.open' )
def UpperCAmelCase ( a_, a_ ):
'''simple docstring'''
lowerCamelCase : int = Mock()
lowerCamelCase ... | 133 | 0 |
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