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 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCAmelCase : Any = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=Data... | 543 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : int , _lowercase : int = 0 , _lowercase : int = 0) -> int:
"""simple docstring"""
a__ : str = right or len(_lowercase) - 1
if left > right:
return -1
elif list_dat... | 136 | 0 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self : int , UpperCAmelCase__ : list) ->None:
'''simple docstring'''
A__ = set_counts
A__ = max(UpperCAmelCase__)
A__ = len(Uppe... | 177 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tenso... | 177 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mo... | 87 |
"""simple docstring"""
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
A__ : Optional[Any] = '\\n @inproceedings{kakwani2020indicnlpsuite,\n title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trai... | 153 | 0 |
from .testing import (
are_the_same_tensors,
execute_subprocess_async,
require_bnb,
require_cpu,
require_cuda,
require_huggingface_suite,
require_mps,
require_multi_gpu,
require_multi_xpu,
require_safetensors,
require_single_gpu,
require_single_x... | 372 |
__UpperCamelCase : List[str] = 256
# Modulus to hash a string
__UpperCamelCase : int = 1000003
def a_ ( _A , _A ) -> bool:
"""simple docstring"""
snake_case__ = len(_A )
snake_case__ = len(_A )
if ... | 372 | 1 |
def _A ( _lowercase , _lowercase ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError('Impossible fluid density' )
if bulk_modulus <= 0:
raise ValueError('Impossible bulk modulus' )
return (bulk_modulus / density) ** 0.5
if ... | 1 |
'''simple docstring'''
def lowerCAmelCase (__A , __A):
"""simple docstring"""
if digit_amount > 0:
return round(number - int(__A) , __A)
return number - int(__A)
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.345, 1))
... | 11 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_lowercase : int =logging.get_logger(__name__)
_lowercase ... | 574 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .... | 574 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import MutableSequence
class __lowercase :
def __init__( self : Dict ,A : int ,A : MutableSequence[float] ):
'''simple docstring'''
if l... | 65 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class ... | 280 | 0 |
"""simple docstring"""
import functools
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = len(_UpperCAmelCase )
A_ : List[Any] = len(_UpperCAmelCase )
@functools.cache
def min_distance(_UpperCAmelCa... | 361 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
_lowerCamelCase : List[str] = logging.get_logger(__name__)
_lowerCamelCase : str = {
... | 361 | 1 |
"""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 importlib_metadata
__lowerCame... | 490 |
"""simple docstring"""
from collections.abc import Callable
import numpy as np
def lowercase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> np.array:
__magic_name__ = int(np.ceil((x_end - xa) / step_size ) )
__magic_nam... | 490 | 1 |
def UpperCAmelCase_ ( _UpperCAmelCase = 1_0_0 ):
lowerCamelCase_: int = set()
lowerCamelCase_: List[Any] = 0
lowerCamelCase_: Union[str, Any] = n + 1 # maximum limit
for a in range(2 , _UpperCAmelCase ):
for b ... | 584 | 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 TFModelTeste... | 584 | 1 |
'''simple docstring'''
def A__ ( A_ ) -> bool:
return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number
if __name__ == "__main__":
print('''Program to check whether a number is a Perfect number or not...''')
__magic_name__ : Dict = int(input... | 497 |
'''simple docstring'''
import os
import sys
__magic_name__ : str = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoMo... | 497 | 1 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ... | 411 |
'''simple docstring'''
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic... | 411 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( __A : Tuple ) -> Dict:
'''simple docstring'''
create_state_space_tree(SCREAMING_SNAKE_CASE_ , [] , 0 , [0 for i in range(len(SCREAMING_SNAKE_CASE_ ) )] )
def _... | 265 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
... | 340 | 0 |
'''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 UpperCamelCase__ ( __SCREAMING... | 714 |
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 UpperCamelCase__ ( __SCREAMING_SNAKE_CASE ):
... | 597 | 0 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCAmelCase__ =logging.get_logg... | 616 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ):
"""simple docstring"""
return x if y == 0 else greatest_common_divisor(UpperCamelCase__ , x % y )
def lowerCAmelCase_ ( UpperCamelCase__ ... | 616 | 1 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase__( UpperCamelCase__ : str = "https://www.worldometers.info/coronavirus" )->Dict:
A__ = BeautifulSoup(requests.get(UpperCamelCase__ ).text , '''html.parser''' )
A__ = soup.findAll('... | 717 |
import math
def UpperCamelCase__( UpperCamelCase__ : int )->bool:
assert isinstance(UpperCamelCase__ , UpperCamelCase__ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
... | 212 | 0 |
__magic_name__ = {str(digit): digit**5 for digit in range(10)}
def _lowerCAmelCase ( A__: int ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(A__ ) )
def _lowerCAmelCase ( ):
'''simple docstring'''
... | 254 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {
"facebook/encodec_24khz": "https://huggingface.co/facebook/encodec_24khz/res... | 254 | 1 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Any ) -> Optional[int]:
def wrapper(*snake_case__ :Dict , **snake_case... | 535 |
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline # noqa: F401
... | 535 | 1 |
'''simple docstring'''
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def _lowercase ( lowerCamelCase__ : Optional[int], lowerCamelCase__ : Dict, lowerCamelCase__ : List[Any] ):
_a = OmegaConf.lo... | 131 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : int = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",... | 131 | 1 |
import unittest
from transformers import DebertaVaTokenizer, DebertaVaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/spiece.model... | 709 |
"""simple docstring"""
import unittest
from transformers import SPIECE_UNDERLINE
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.tokenization_utils import AddedToken
from ...test_toke... | 370 | 0 |
import numpy as np
import torch
import tqdm
from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler
class UpperCamelCase_ ( UpperCAmelCase__ ):
'''simple docstring'''
... | 87 |
from __future__ import annotations
from collections import namedtuple
def snake_case_ ( lowerCAmelCase_ : float , lowerCAmelCase_ : float , lowerCAmelCase_ : float ):
__lowercase : str = namedtuple("""result""" , """na... | 149 | 0 |
'''simple docstring'''
from ... import PretrainedConfig
UpperCamelCase_ = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''... | 508 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
UpperCamelCase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_... | 508 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.u... | 25 |
def A__ ( lowerCamelCase = 4_00_00_00 ) -> int:
UpperCamelCase_: Dict = []
UpperCamelCase_, UpperCamelCase_: Optional[int] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowerCamelCase )
UpperCamelCase_, UpperCamelCase_: ... | 548 | 0 |
import functools
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
if not isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) or not all(isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) for day in days ):
raise Value... | 102 |
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
assert x is not None
assert y is not None
__lowerCAmelCase = len(UpperCAmelCase__ )
__lowerCAmelCase = len(UpperCAmelCase__ )
# declaring the array ... | 102 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
snake_case = log... | 103 |
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
from transformer... | 297 | 0 |
"""simple docstring"""
from __future__ import annotations
def _lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : list[str] | None = None ):
lowercase__ : List[Any] = word_bank or []
# create a table
lowercase__ : int = len(lowerCamelCase__ ... | 128 |
"""simple docstring"""
import os
def _lowerCamelCase ( lowerCamelCase__ : str = "matrix.txt" ):
with open(os.path.join(os.path.dirname(lowerCamelCase__ ) , lowerCamelCase__ ) ) as in_file:
lowercase__ : Optional[Any] = in_file.read()
lowercase__ : ... | 128 | 1 |
def lowerCAmelCase ( UpperCAmelCase = 100_0000 ) ->int:
"""simple docstring"""
__magic_name__ : str = [i - 1 for i in range(limit + 1 )]
for i in range(2, limit + 1 ):
if phi[i] == i - 1:
for j in ... | 154 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_dat... | 154 | 1 |
"""simple docstring"""
def snake_case ( A__ ,A__ ):
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
UpperCAmelCase_ : List[str] = str(bin(_lowerCamelCase ) )[2:] # remove the leading "0b"
UpperCAmelCase_ : Optional[Any] ... | 716 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCamelCase_ (metaclass=__A ):
__magic_name__ = ['''onnx''']
def __init__( self : List[Any] , *lowerCAmelCase_ : Dict , **lowerCAmelCase_ : Dict ) -> Dict:
r... | 463 | 0 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
UpperCAmelCase_ : List[str] = logging.get_logger(__name__)
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
__magic_... | 21 |
import warnings
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
UpperCAmelCase_ : Any = logging.get_logger(__name__)
Upp... | 21 | 1 |
'''simple docstring'''
from __future__ import annotations
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =0
__lowercase =len(_lowerCAmelCase ) - 1
while i < j:
if nums[i] + nums[j] == target:
... | 454 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConf... | 454 | 1 |
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
return getitem, k
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstr... | 496 |
'''simple docstring'''
import math
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(SCREAMING_SNAKE_CASE__ )
def sna... | 533 | 0 |
def a ( A__ , A__ , A__ ) -> float:
'''simple docstring'''
if principal <= 0:
raise Exception('''Principal borrowed must be > 0''' )
if rate_per_annum < 0:
raise Exception('''Rate of interest must be >= 0''' )
if... | 250 |
import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging
a_ :s... | 250 | 1 |
import math
import tensorflow as tf
from packaging import version
def lowerCamelCase__ ( lowercase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = tf.convert_to_tensor(lowercase )
SCREAMING_SNAKE_CASE : Optional[int] = 0.5 * (1.0 + t... | 62 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Dict... | 347 | 0 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__lowercase : str = logging.get_logger(__n... | 357 |
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Accelerato... | 357 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from . import BaseTransformersCLICommand
def a_ ( _UpperCAmelCase : Dict ) -> List[Any]:
return DownloadCommand(args.model ,args.cache_dir ,args.force ,args.trust_remote_code )
class snake_case__ ( SCREA... | 286 |
'''simple docstring'''
import pickle
import numpy as np
from matplotlib import pyplot as plt
class snake_case__ :
def __init__( self : List[Any] , __a : str , __a : Dict , __a : List[Any] , __a : str , ... | 286 | 1 |
from numpy import exp, pi, sqrt
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 ):
'''simple docstring'''
return 1 / ... | 702 |
"""simple docstring"""
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_UpperCamelCase = ... | 363 | 0 |
"""simple docstring"""
from __future__ import annotations
def a ( __UpperCAmelCase : int | str ) -> bool:
__magic_name__: List[str] = str(__UpperCAmelCase )
return n == n[::-1]
def a ( __UpperCAmelCase : int = ... | 96 | import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class snake_case ( __snake_case ):
"""simple docstring"""
__lowerCAmelCase = (UnCLIPScheduler,)
def snake_case__ ( self , **lowerCAmelCase_ ):
__lower... | 321 | 0 |
def a__ ( a ) -> None:
A_ : List[Any] = generate_pascal_triangle(a )
for row_idx in range(a ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print... | 707 | import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = ... | 236 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( __magic_name__ : Optional[int] ) -> Union[str, Any]:
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
... | 92 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __magic_name__ : list[float] ) -> float:
lowercase : Any =0.0_0
lowercase : Tuple =0
for resistor in resistors:
if resistor <= 0:
l... | 92 | 1 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import... | 702 | import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/microsoft/unispeech-large-1500h... | 452 | 0 |
'''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, A... | 131 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from trans... | 457 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Optional[int] = logging.get_logger(__name__)
__A : Union[str, Any] = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/r... | 95 |
"""simple docstring"""
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 : List[... | 95 | 1 |
'''simple docstring'''
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[str] = logging.get_logger(__name__)
a : Tuple = {
'''huggingface/autoformer-tourism-monthly''': '''https:... | 69 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
... | 69 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 197 | """simple docstring"""
from __future__ import annotations
import math
def UpperCAmelCase__ ( lowerCAmelCase__ :int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowercase = f'{num}: Invalid input, please enter a positive integer.... | 197 | 1 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _UpperCamelCase( __lowerCa... | 47 |
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
lowerCamelCase : Tuple = {
'''kakaobrain... | 149 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def __lowerCamelCase ( __lowerCAmelCase : str = "AAPL" ) -> str:
snake_case = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
snake_case = BeautifulSoup(request... | 517 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_SCREAMING_SNAKE_CASE = "docs/source/en/_toctree.yml"
def __lowerCamelCase ( __lowerCAmelCase : Tuple ) -> Optional[int]:
snake_case = defaultdict(__l... | 517 | 1 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
__magic_name__ = logging.getLogger(__name__)
__magic_name__ ... | 129 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__magic_name__ = {"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_A... | 129 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__magic_name__ = TypeVar('T')
__magic_name__ = TypeVar('U')
class __lowerCAmelCase ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict ,_a... | 703 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data ... | 27 | 0 |
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __lowercase :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
SCREAMI... | 637 |
def _SCREAMING_SNAKE_CASE ( __lowercase : List[Any] ) -> Any:
"""simple docstring"""
stooge(__lowercase , 0 , len(__lowercase ) - 1 )
return arr
def _SCREAMING_SNAKE_CASE ( __lowercase : str , __lowercase : Dict... | 637 | 1 |
'''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 import A... | 700 |
'''simple docstring'''
import unittest
from datasets import load_dataset
from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow
@is_pipeline_test
@require_torch
class _lowerCamelCase ( unittest.TestCase ):
'''s... | 540 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
__a = input('Enter image url: ').strip()
print(f"Downloading image from {url} ...")
__a = BeautifulSoup(requests.get(url).content, 'html.parser')
# The image UR... | 97 |
"""simple docstring"""
from manim import *
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
def lowerCAmelCase_ ( self : Union[str, Any] ):
_A = Rectangle(height=0.5 , width=0.5 )
_A = Rectangle(height=0.46 , w... | 7 | 0 |
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 OptionalDependencyNotAvailable:
from ..... | 664 |
import unittest
import numpy as np
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, prepare_video_inputs
if is_torch_available():
import tor... | 664 | 1 |
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
__snake_case = (
'''This metric will be removed from the library soo... | 1 |
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__ = Lock()
def lowercase ( a , a , a , a , a , a , a ):
'''simple docstring'''
global process_lock
# we perfor... | 631 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemak... | 701 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_uti... | 346 | 0 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = 10**-10 ) -> Tuple:
"""simple docstring"""
lower... | 610 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class UpperCAmelCase__ ( A__ , A__ ):
"""simple ... | 493 | 0 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from tra... | 704 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
a_ : int = {
'configuration_speecht5': [
'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SPEECHT5_PRETRAINE... | 444 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def UpperCamelCase ( __magic_name__ : Lis... | 15 |
'''simple docstring'''
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_... | 11 | 0 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils impor... | 709 |
from math import factorial, radians
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 18 , SCREAMING_SNAKE_CASE__ = 10) -> float:
__snake_case: Union[str, Any] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from de... | 155 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : Union[str, Any] = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise... | 105 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : int ) -> list:
UpperCAmelCase : Union[str, Any] = int(_lowerCAmelCase )
if n_element < 1:
UpperCAmelCase : int = ValueError('''a should be a positive num... | 127 | 0 |
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__a : Dict = """src/diffusers"""
# Matches is_xxx_available()
__a : Dict = re.compi... | 414 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unl... | 414 | 1 |
'''simple docstring'''
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_token... | 442 |
'''simple docstring'''
import argparse
import os
import sys
from unittest.mock import patch
import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main
from transformers impo... | 442 | 1 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def lowerCamelCase ( _UpperCamelCase : Any ) -> Optional[int]... | 299 |
"""simple docstring"""
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
UpperCAmelCase ... | 299 | 1 |
def lowerCamelCase__ ( _a):
SCREAMING_SNAKE_CASE : Optional[Any] = int(_a)
if decimal in (0, 1): # Exit cases for the recursion
return str(_a)
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : int = divmod(_a , 2)
return binary_recursive(_a) + str(_a)
def lowerCam... | 25 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.gene... | 470 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : List[Any] = {
"""configuration_roberta""": ["""ROBERTA_PRET... | 715 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
UpperCAmelCase_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), ... | 541 | 0 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_sta... | 75 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> np.ndarray:
UpperCAmelCase__ ... | 75 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( _a : list[int] , _a : int , _a : int , _a : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
snake_case_ , ... | 114 |
import warnings
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 UpperCAmelCase_ ( SCREAMING_SNAKE_CASE_... | 114 | 1 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int = 1_00_00_00) -> int:
'''simple docstring'''
_lowercase : str = limit + 1
_lowercase : int = [0] * limit
for first_term in range(1 , lowerCAmelCase__):
... | 125 |
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int]=7) -> Any:
'''simple docstring'''
... | 125 | 1 |
"""simple docstring"""
def lowercase__ ( lowercase_ ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase : List[Any] = len(lowercase_ )
for i in range(length - 1 ):
_UpperCamelCase : Optional[int] = i
f... | 51 |
"""simple docstring"""
lowerCamelCase__ = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/... | 51 | 1 |
"""simple docstring"""
import re
def lowercase__( __SCREAMING_SNAKE_CASE : str ):
lowercase_ : str = re.compile(
R'^(?:0|94|\+94|0{2}94)' R'7(0|1|2|4|5|6|7|8)' R'(-| |)' R'\d{7}$' )
return bool(re.search(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )... | 425 | """simple docstring"""
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__SCREAMING_SNAKE_CASE =False
class UpperCamel... | 425 | 1 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
snake_case = """\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian ... | 720 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=lowerCAmelCase ):
'''simple docstring'''
UpperCamelCase_ : Union[str, Any] = ['''keras_nlp''']
def __init__( self : Dict , *UpperCAmelCase_ : Optional[Any] , **U... | 488 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( lowercase_ : int ):
'''simple docstring'''
assert (
isinstance(lowercase_ , lowercase_ ) and number_of_steps > 0
), F'''number_of_steps needs to be positive integer, your input {number_of_steps}'''
if number_of_steps... | 674 |
"""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 ImageProcessingSavingTestMixin,... | 674 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'tanreinama/GPTSAN-2.8B-spout_is_uniform': (
'https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/c... | 380 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def a ( ... | 380 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session" )
def UpperCAmelCase_ (... | 310 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XL... | 200 | 0 |
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 677 |
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,
)
UpperCamelCase = {
"configuration_clip": [
"CLIP_PRETRAINED_CO... | 677 | 1 |
'''simple docstring'''
import os
def UpperCamelCase ( ) -> str:
'''simple docstring'''
with open(os.path.dirname(lowercase_ ) + '''/grid.txt''' ) as f:
lowercase =[] # noqa: E741
for _ in range(2_0 ):
l.append([int(lowercase_ ) for x in f.readline().split()] )
lowe... | 72 |
'''simple docstring'''
from torch import nn
class A ( nn.Module ):
def __init__( self , snake_case_ , snake_case_ ) -> List[Any]:
super().__init__()
_a = class_size
_a = embed_size
# self.mlp1 = nn.Linear(embed_size, emb... | 131 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
UpperCAmelCase: Union[str, Any] = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"""... | 707 |
"""simple docstring"""
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
UpperCAmelCase: str = logging.get_logger(__name__)
def __SCREAMING_SNAKE_CASE ( __Up... | 600 | 0 |
'''simple docstring'''
from __future__ import annotations
def A (__lowerCamelCase :list[int | float] , __lowerCamelCase :int , __lowerCamelCase :int ):
if len(__lowerCamelCase ) == 0:
raise ValueError("""find_max() arg is an empty sequence""" )
if (
... | 5 |
def A ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
A__ = _modexpt(__UpperCamelCase , exponent // 2 , __UpperCamelCase ) % modulo_value
return (x * x) % modul... | 9 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""shi-labs/nat-mini-in1k-224""": """https://hugg... | 611 |
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...ima... | 611 | 1 |
'''simple docstring'''
import numpy as np
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self : Optional[int] ) -> Union[str, Any]:
__magic_name__ = (0, 0)
__magic_name__ = None
__magic_name__ = 0
... | 664 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ : Tuple = logging.get_logger(__name__)
lowerCamelCase__ : Any =... | 698 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
lowercase_ = logging.get_logger(__name__)
lowercase_ = {'vocab_fi... | 718 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by a... | 230 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__lowerCAmelCase = {'processing_wav2vec2_with_lm': ['Wav2Vec2ProcessorWithLM']}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__lowerCAmelCase = _... | 585 |
'''simple docstring'''
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.Te... | 585 | 1 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
fr... | 702 |
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
_validate_point(SCREAMING_SNAKE_CASE__ )
_validate_point(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError('Both points must be in the same n-dimensional space' )
... | 230 | 0 |
"""simple docstring"""
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 lowercase__ ( unittest.TestCase ):
__Up... | 88 |
'''simple docstring'''
import math
import sys
def _lowerCAmelCase ( __snake_case : int ) -> int:
if number != int(__snake_case ):
raise ValueError('the value of input must be a natural number' )
if number < 0:
raise ValueE... | 8 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMod... | 717 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import... | 505 | 0 |
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import... | 587 |
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> bool:
snake_case : set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
snake_case : set[int] = set()
return any(
node not in visited and depth_first_search(... | 587 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : Union[str, Any] = HfArgumentParser(UpperCAmelCase )
__lowerCamelCase ... | 458 |
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__UpperCamelCase : Tuple = {
'tiny.en': 'https://openaipublic.azureedge.net... | 458 | 1 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# ... | 77 | '''simple docstring'''
import numpy as np
def UpperCamelCase__ ( _lowercase : np.array ) -> np.array:
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod() | 523 | 0 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_UpperCamelCase : Any = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understandi... | 709 | """simple docstring"""
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transfor... | 645 | 0 |
def _lowerCamelCase ( __lowerCamelCase = 200_0000 ) -> int:
'''simple docstring'''
UpperCAmelCase__ : int = [0 for i in range(n + 1 )]
UpperCAmelCase__ : Tuple = 1
UpperCAmelCase__ : List[Any] = 1
... | 79 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCamelCase__ : int ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_SCREAMING_SNAKE_CASE : int = 1
_SCREAMING_SNAKE_CASE : List[str] = 1
while repunit:
_SCREAMING_SNAKE_CASE : Tuple ... | 572 | 0 |
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPi... | 221 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTeste... | 221 | 1 |
from __future__ import annotations
def UpperCAmelCase_ ( snake_case__ , snake_case__ = None ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ = word_bank or []
# create a table
lowerCAmelCase__ = len(__snake_case ) + 1
lowerCAmelCase__ ... | 193 |
"""simple docstring"""
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from ... | 88 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( __lowerCamelCase ):
snake_case_ = ["""image_processor""", """tokenizer"""]
snake_case_ ... | 194 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __lowercase ( __lowerCamelCase ):
snake_case_ = """timm_backbone"""
def __init__( self... | 194 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mp... | 545 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_co... | 545 | 1 |
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 snake_case__ ( __A):
'... | 708 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class snake_case__ :
'''simple docstring'''
def __init__( self , a__=2 , a__=3 , a__=64 , a__=None ... | 291 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase ... | 26 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase = logging.getLogger()
... | 26 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class lowerCamelCase__ ( ... | 707 |
from __future__ import annotations
import numpy as np
def UpperCamelCase_( _A :list[float] )-> Union[str, Any]:
return np.maximum(0 , _A )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 185 | 0 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__lowercase : Dict =... | 54 |
"""simple docstring"""
import numpy as np
def UpperCamelCase__ ( lowercase__ : Optional[int] , lowercase__ : Union[str, Any] , lowercase__ : Any , lowercase__ : Dict , lowercase__ : List[str] ):
snake_case : Optional[int] = int(np.ceil((x_en... | 134 | 0 |
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ... | 351 |
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless requi... | 351 | 1 |
"""simple docstring"""
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _lowerCamelCase ( UpperCAmelCase_ : int ) -> Optional[Any]:
"""simple docstring"""
def is_in_ci... | 104 |
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
... | 130 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from trans... | 705 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMS... | 673 | 0 |
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