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 __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of t... | 121 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCamelCase : str = {
'configuration_jukebox': [
'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
'JukeboxConfig',
'JukeboxPriorConfig',
... | 121 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Tuple = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
'''CLIPSegTextConfig'... | 710 |
import pytest
SCREAMING_SNAKE_CASE : Optional[Any] = "__dummy_dataset1__"
SCREAMING_SNAKE_CASE : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"w... | 354 | 0 |
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, prepare_image_inputs
... | 583 |
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
snake_case__ = logging.get_logger(__name__)
... | 583 | 1 |
import math
import tensorflow as tf
from packaging import version
def _lowercase ( UpperCamelCase__ : Union[str, Any] ):
__A : Dict = tf.convert_to_tensor(UpperCamelCase__ )
__A : Optional[int] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ), x.dtype ) ... | 713 |
'''simple docstring'''
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 ... | 540 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class ... | 300 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase : List[str] = {
'''configuration_conditional_detr''': [
'''CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Condition... | 149 | 0 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers impor... | 717 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
... | 40 | 0 |
from typing import Any
def UpperCamelCase_( _A :list )-> list[Any]:
if not input_list:
return []
UpperCamelCase__ = [input_list.count(_A ) for value in input_list]
UpperCamelCase__ = max(_A ) # Gets the maximum count in the input list.
# Gets values of modes
... | 551 |
import re
import string
import numpy as np
import datasets
__UpperCamelCase = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__UpperCamelCase = '\nArgs:\n predictions: List o... | 551 | 1 |
"""simple docstring"""
def a ( __UpperCAmelCase : int = 1_0_0_0 ) -> int:
__magic_name__: Any = 2**power
__magic_name__: Any = str(__UpperCAmelCase )
__magic_name__: str = list(__UpperCAmelCase )
... | 213 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __A ( SCREAMING_SNAKE_CASE_ ):
UpperCAmelCase__ = "ClapFeatureExtractor"
UpperCAmelCase__ = ("RobertaTokenizer", "Rob... | 213 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
fr... | 236 |
'''simple docstring'''
from manim import *
class A_ ( lowerCAmelCase_ ):
def lowercase ( self : Dict ):
_UpperCAmelCase = Rectangle(height=0.5 , width=0.5 )
_UpperCAmelCase = Rectangle(height=0.4_6 , width=0.4_6 ... | 236 | 1 |
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 SPIECE_UNDERLINE, logging
__UpperCAmelCase : Dict = logging.get_logger(__na... | 712 |
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def lowerCamelCase_ ( UpperCamelCase_ = 8 ):
_a : int = ascii_letters + digits + punctuation
return "".join(secrets.choice(UpperCamelCase_ ) fo... | 249 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
SCREAMING_SNAKE_CASE_ : list[list[str]] = [[] for _ in range(_a )]
SCREAMING_SNAKE_CASE_ : Optional[int] = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if ... | 345 |
def UpperCamelCase ( _a = 1 , _a = 1_0_0_0 ) -> int:
'''simple docstring'''
lowercase_ :str = 1
lowercase_ :Union[str, Any] = 0
for divide_by_number in range(_a , digit + 1 ):
lowercase_ ... | 257 | 0 |
def lowerCAmelCase ( UpperCamelCase__ : int ) -> bool:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Tuple = n ** (1 / 3)
return (val * val * val) == n
if __name__ == "__main__":
print(perfect_cube(27))
print(perfect_cube(4))... | 715 |
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : str = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the f... | 146 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def lowercase (_snake_case ,_snake_case ,_snake_case ,_snake_case ) -> Any:
'''simple docstring'''
__UpperCamelCase = 0
# Number of processes finished
__UpperCamelCase = 0
# Display... | 505 | from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class snake_case ( UpperCamelCase_ ):
lowercase_ = ['image_processor', 'tokenizer']
lowercase_ = 'AutoImageProcessor'
lowercase_ = 'AutoTokenizer'
def __init__( self ... | 85 | 0 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __UpperCamelCase ( ):
__UpperCAmelCase : Optional[Any] = HfArgumentParser(_UpperCAmelCase )
__UpperCAmelCase : List[str] = parser.parse_args_i... | 329 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class SCREAMING_SNAKE_CASE__ ( snake_case__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE = '''ClapFeatureExtractor'''
SCREAMING_SNAKE_CASE ... | 329 | 1 |
from numpy import exp, pi, sqrt
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase = 0.0, __UpperCamelCase = 1.0 ):
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.tes... | 151 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, ... | 151 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=lowercase__ ):
snake_case_ = ["""torch""", """scipy"""]
def __init__( self : Any , *_lowercase : str , **_lowercase : Any ) -> Optional[int]:
r... | 715 | """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=logging.INFO)
__UpperCame... | 227 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from t... | 109 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowerCAmelCase ( UpperCAmelCase__ : str , UpperCAmelCase__ : str , **UpperCAmelCase__ : List[str] ) -> Tuple:
lowerCamelCase_ = AutoConfig.from_... | 272 | 0 |
'''simple docstring'''
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =[]
_UpperCamelCase =set({'''(''', '''[''', '''{'''} )
_UpperCamelCase =set({''')''', ''']''', '''}'''} )
_UpperCamelCase ={'''{''': '''}''', '''[''':... | 271 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_... | 271 | 1 |
import gc
import importlib.metadata
import tempfile
import unittest
from packaging import version
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoTokenizer,
BitsAndBytesConfig,
pipeline,
)
from... | 570 |
import pytest
import datasets
# Import fixture modules as plugins
UpperCAmelCase_ : Optional[int] = ['tests.fixtures.files', 'tests.fixtures.hub', 'tests.fixtures.fsspec']
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Dict ) ... | 570 | 1 |
'''simple docstring'''
from __future__ import annotations
import math
def __snake_case ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : bool , SCREAMING_SNAKE_CASE_ : list[int] , SCREAMING_SNAKE_CASE_ : float ) -> int:
"""simple docstr... | 570 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : int ) -> int:
"""simple docstring"""
if exponent == 1:
return base
if exponent % 2 == 0:
UpperCAmelCase =... | 570 | 1 |
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_datasets, _in... | 105 |
"""simple docstring"""
def __UpperCamelCase ( snake_case__ = 200 ):
A_ : Union[str, Any] = [1, 2, 5, 10, 20, 50, 100, 200]
A_ : int = [0] * (pence + 1)
A_ : Tuple = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(snake_cas... | 180 | 0 |
from timeit import timeit
_A = {
"MALAYALAM": True,
"String": False,
"rotor": True,
"level": True,
"A": True,
"BB": True,
"ABC": False,
"amanaplanacanalpanama": True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key,... | 711 |
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
lowerCAmelCase_ = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
lowerCAmelCase... | 279 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class _lowercase ( _A ):
def __init__( self , a , a=None , a=True , a=None , **a ):
sn... | 385 |
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ..utils.... | 385 | 1 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
snake_case__ = version.parse(importlib_metadata.version('''nltk'''))
if NLTK_VERSION >= version.Version('''3.6.4'''):
from nltk import word_tokenize
snake_case__ = ... | 718 |
import datasets
from .evaluate import evaluate
snake_case__ = '''\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
'''
snake... | 373 | 0 |
'''simple docstring'''
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 (
AudioLD... | 3 | import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vis... | 613 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cach... | 267 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase ( lowerCamelCase_ :list , lowerCamelCase_ :list ):
'''simple docstring'''
if len(lowerCamelCase_ ) != 2 or len(a[0] ) != 2 or len(lowerCamelCase_ ) != 2 or len(b[0] ) != 2:
raise E... | 267 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCamelCase_ : Tuple = logging.get_logger(... | 559 | import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
lowerCamelCase_ : List[Any] = pd.read_csv("""sample_data.csv""", header=None)
lowerC... | 559 | 1 |
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
lowercase__ : Tuple = logging.get_logger(__name__)
lowercase__ : List[str] ... | 718 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ : Any = logging.get_logger(__name__)
lowercase__ : Tuple = ... | 485 | 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 _snake_cas... | 436 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowerCamelCase__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
... | 225 | 0 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxG... | 720 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easi... | 91 | 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 import... | 353 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
A__ : Tuple = [
'good first issue',
'good second issue',
'good difficult issue',
'enhancement',
'new pipeline/model',
'new scheduler',
'wip',
]
def _lowerCAmelCase ( ):
... | 353 | 1 |
'''simple docstring'''
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.... | 368 |
'''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, p... | 368 | 1 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class a ( unittest.TestCase ):
def UpperCamelCase__ ( self ):
... | 4 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
__UpperCamelCase : Optional[Any] = tuple[int, int]
class a :
def __init__( self , _snake_case , _snake_case ):
"""simple docstri... | 4 | 1 |
"""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 imp... | 494 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class lowerCAmelCase (... | 494 | 1 |
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowerCamelCase__ : int , lowerCamelCase__ : int ):
'''simple docstring'''
A: list[list[int]] = []
create_all_state(1 , lowerCamelCase__ , lowerC... | 135 |
'''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
__SCREAMING_SNAKE_CASE : Optional[Any... | 135 | 1 |
def UpperCAmelCase__ ( lowercase__ , lowercase__ , lowercase__ ) -> int:
if exponent == 1:
return base
if exponent % 2 == 0:
__lowercase = _modexpt(lowercase__ , exponent // 2 , lowercase__ ) % modulo_va... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_ava... | 634 | 0 |
"""simple docstring"""
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation... | 573 |
"""simple docstring"""
import logging
from transformers.configuration_utils import PretrainedConfig
lowercase = logging.getLogger(__name__)
class lowercase__ ( A ):
'''simple docstring'''
_UpperCAmelCase = '''maske... | 573 | 1 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class __A (datasets.BeamBasedBuilder):
'''simple docstring'''
def l... | 2 |
"""simple docstring"""
from __future__ import annotations
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> list[int]:
snake_case_ = 0
snake_case_ = len(_SCREAMING_SNAKE_CASE ) - 1
while i < j:
if nums[i] + nums[j... | 2 | 1 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_snake_case = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case = [ord(letter) for letter in string.ascii_lowercase]
_snake_case ... | 382 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : list[list[int | float]] ) -> int:
lowerCamelCase_ = len(_lowerCamelCase )
lowerCamelCase_ = len(matrix[0] )
lowerCamelCase_ = min(_lowerCamelCase , _lowerCa... | 549 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vi... | 9 |
'''simple docstring'''
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.proces... | 9 | 1 |
'''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 |
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase__ =get_tests_dir('fixtures/test_se... | 249 | 0 |
def UpperCamelCase_( _A :list , _A :list , _A :int , _A :int , _A :int )-> int:
if index == number_of_items:
return 0
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = knapsack(_A , _A , _A ,... | 719 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = {
'facebook/data2vec-text-base': 'https://hug... | 185 | 0 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_d... | 344 |
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ )-> str:
"""simple docstring"""
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(SCREAMING_SNAKE_CASE_ , ... | 628 | 0 |
import argparse
import os
import torch
from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Tuple:
# encoder.embeddings ... | 719 |
import math
import sys
import cva
import numpy as np
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : np.ndarray , SCREAMING_SNAKE_CASE : float ) -> np.ndarray:
# For applying gaussian function for each element in matrix.
__lowercase = math.sqrt(SCREAMING_SNAKE_CASE... | 688 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common im... | 289 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class UpperCAmelCase_ ( _UpperCamelCase ):
def __lt__( self : int , A : Dict ):
return ... | 289 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ : Dict = len(a_ )
print("""The following activities are selected:""" )
# The first activity is always selecte... | 713 |
'''simple docstring'''
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _SCREAMING_SNAKE_CASE ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) ):
"""simple docstring"""
lowerCAmelCase__ : Option... | 160 | 0 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_snake_case : Tuple = models.Sequentia... | 441 |
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
_snake_case : Tuple = models.Sequentia... | 441 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : str = logging.get_logger(__name__)
_lowercase : Dict = {}
class _UpperCAmelCase ( lowerCAmelCase__ ):
a__ : int = "llama"... | 709 |
"""simple docstring"""
import qiskit
def lowercase__ ( snake_case_ :int , snake_case_ :int ):
__UpperCAmelCase = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
__UpperCAmelCase = qiskit.QuantumCircu... | 397 | 0 |
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 : List[Any] = False
try:
__UpperCamel... | 80 |
"""simple docstring"""
import argparse
import os
# New Code #
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_s... | 682 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowercase (a_ ):
'''simple docstring'''
def _lowerCamelCase ( self ):
'''simple docstring'''
return [
{"col_1": 3, "col_2... | 719 |
# 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 ... | 504 | 0 |
'''simple docstring'''
from typing import Dict
import numpy as np
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException
if is_tf_available():
import tensorflow as tf
from ..tf_utils i... | 531 |
"""simple docstring"""
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql i... | 506 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __magic_name__ ( ) -> List[Any]:
_lowercase : Dict = ArgumentParser(
description=(
... | 677 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase_ :
def __init__( self , _lowerCAmelCase ):
_lowercase : Any = num_of_nodes
_lowercase : list[list[int]] = []
_lowercase : ... | 677 | 1 |
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPException
from fastapi.routi... | 23 |
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_simplif... | 23 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {'''configuration_xlnet''': ['''XLNET_... | 667 |
'''simple docstring'''
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( _snake_case ):
lowercase = (IPNDMScheduler,)
lowercase = (("num_inference_steps", 50),)
def snake_case_ (... | 667 | 1 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowerCAmelCase ( UpperCAmelCase_ ):
"""simple docstring"""
def __init__( self : List[str] , _snake_case : Optional[int] , _snake_case : ... | 9 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''xlm-mlm-en-2048''': '''https://huggingfa... | 154 | 0 |
def a ( lowerCamelCase_ , lowerCamelCase_ ):
'''simple docstring'''
if digit_amount > 0:
return round(number - int(lowerCamelCase_ ) , lowerCamelCase_ )
return number - int(lowerCamelCase_ )
if __name__ == "__main__":
print(decimal_isol... | 716 |
import argparse
import os
import re
A__ : Optional[int] = 'src/transformers'
# Pattern that looks at the indentation in a line.
A__ : Union[str, Any] = re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__ : List[str] = re.compil... | 671 | 0 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor... | 486 | import unittest
from diffusers.models.unet_ad_blocks import * # noqa F403
from diffusers.utils import torch_device
from .test_unet_blocks_common import UNetBlockTesterMixin
class A ( UpperCAmelCase_ , unittest.TestCase ):
__UpperCAmelCase : int = DownBlockaD #... | 486 | 1 |
"""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 ...te... | 401 |
"""simple docstring"""
_lowerCamelCase = '''
# Installazione di Transformers
! pip install transformers datasets
# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e
# rimuovi la modalità commento al comando seguente.
# ! pip install git+https://github.co... | 401 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_... | 22 |
import tempfile
import torch
from diffusers import IPNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE__ =(IPNDMScheduler,)
SCREAMING_SNAKE_CASE__ =(("""num_inference_steps""", 50),... | 693 | 0 |
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 .tokenization_cam... | 707 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...tes... | 409 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slo... | 93 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'YituTech/conv-bert-base': 'https://huggingface.co/YituT... | 132 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {
"configuration_convbert": ["CONVBERT_PRETRAINED_CONFI... | 65 |
"""simple docstring"""
from __future__ import annotations
def lowercase ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> list[str]:
if partitions <= 0:
raise ValueError('''partitions must be a positive number!''' )
if partitions > number_of_bytes:
... | 65 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
lowerCamelCase__ : Union[str, Any] = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", ... | 33 |
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def _lowercase (SCREAMING_SNAKE_CASE ):
'''simple docstring'''
for i in range(0 , SCREAMING_SNAKE_CASE ):
for _ in range(0 , n - i - 1 ): # printin... | 111 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
... | 721 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_s... | 0 | 0 |
'''simple docstring'''
from numpy import exp, pi, sqrt
def A_ ( _lowerCAmelCase : Any , _lowerCAmelCase : float = 0.0 , _lowerCAmelCase : float = 1.0 ):
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (... | 44 |
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
_UpperCAmelCase : Dict = logging.get_logger(__name__)
_UpperCAmelCase : Op... | 668 | 0 |
"""simple docstring"""
def lowercase (snake_case__ : Any ) -> Any:
'''simple docstring'''
if not head:
return True
# split the list to two parts
lowerCAmelCase , lowerCAmelCase = head.next, head
while fast and fast.next:
lowerCAmelCase ... | 529 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a = {
'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
'tokenization_biogpt': ['BioGptT... | 529 | 1 |
import argparse
import os
import re
__lowerCamelCase : Any = """src/diffusers"""
# Pattern that looks at the indentation in a line.
__lowerCamelCase : Dict = re.compile(r"""^(\s*)\S""")
# Pattern that matches `"key":" and puts `key` in group 0.
__lowerCamelCase : Tuple ... | 385 |
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 numpy as np
import tensorflow as tf
from transformers import TFCame... | 385 | 1 |
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
_SCREAMING_SNAKE_CASE = [
"""Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell... | 534 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGen... | 534 | 1 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _A ( ):
"""simple docstring"""
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import di... | 41 |
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
lowe... | 345 | 0 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
A_ :int = '''https://www.indeed.co.in/jobs?q=mobile+app+development&l='''
def A ( a_ = "mumbai" ) -> Generator[tuple[str, str], None, None]:
... | 154 |
from collections.abc import Generator
def A ( ) -> Generator[int, None, None]:
__UpperCamelCase , __UpperCamelCase : Tuple =0, 1
while True:
__UpperCamelCase , __UpperCamelCase : Union[str, Any] =b, a + b
yield b
... | 154 | 1 |
'''simple docstring'''
import unittest
import numpy as np
from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
load_numpy,
nightly,
require_onnxruntime,
require_torch_gpu,
)
if is_onnx_available():
i... | 517 |
'''simple docstring'''
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=No... | 517 | 1 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Generic, TypeVar
lowerCamelCase__ = TypeVar('_T')
class _lowerCAmelCase ( Generic[_T] ):
'''simple docstring'''
def __init__( self : List[str] , UpperCamelCase_ ... | 411 |
'''simple docstring'''
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 numpy as np
import tensorflow ... | 411 | 1 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
... | 698 |
"""simple docstring"""
from __future__ import annotations
import math
from collections.abc import Callable
def __A ( a_ : Callable[[int | float], int | float] , a_ : int | float , a_ : int | float , a_ : int = 1_00 , )-> float:
'''simple docstring'''
SCREAMIN... | 698 | 1 |
import argparse
import re
import requests
import torch
# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional impo... | 704 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, required=True, help=''... | 530 | 0 |
# Copyright 2023 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
#
# Unless required by applic... | 81 |
_snake_case : Optional[int] = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
_snake_case : Dict = ["a", "b", "c", "d", "e"]
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
__snake_case ... | 81 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class A_ ( __lowerCamelCase ):
... | 565 |
# Copyright 2023 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
#
# Unless required by applicab... | 565 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _UpperCamelCase ( unittest.TestCase , _A ):
'''simple docstring'''
def lowerCAmelCase__ ( self : Tuple ):
UpperCamelCase_: List[Any] = ... | 548 |
import heapq
import sys
import numpy as np
lowerCamelCase_ : Optional[Any] = tuple[int, int]
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Any ):
UpperCamelCase_: Union[str, Any] = []
UpperCamelCase_: str = set... | 548 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
snake_case_ : Tuple = logging.get_logger(__name__)
snake_case_ : Tuple = [
["attention", "at... | 169 |
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def A (__A : Tuple , __A : List[Any]=None ) -> Optional[int]:
"""simple docstring"""
... | 169 | 1 |
'''simple docstring'''
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def lowerCAmelCase_ ( snake_case_ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase_ = FileLock(str(tmpdir / "f... | 78 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeli... | 585 | 0 |
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_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Union[st... | 440 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowercase__ ( __A ):
__UpperCamelCase = """M-CLIP"""
def __init__( self , _lowercase=1_024 , _lowercase=768 , **_lowercase ):
lowerCAmelCase_ ... | 440 | 1 |
from typing import Dict
from .base import GenericTensor, Pipeline
class __A ( snake_case_ ):
"""simple docstring"""
def __snake_case ( self , a__=None , a__=None , a__=None , **a__):
"""simple docstring"""
... | 114 | 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,
nested... | 417 | 0 |
def _a ( _lowerCAmelCase : float ):
if edge <= 0 or not isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
raise ValueError("Length must be a positive." )
return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)
def _a ( _lowerCAmelCase ... | 704 |
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
c... | 552 | 0 |
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = [0] * len(__SCREAMING_SNAKE_CASE )
lowercase = []
lowercase = [1] * len(__SCREAMING_SNAKE_CASE )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__SCREAMING_SNAK... | 84 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxT... | 84 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
"""configuration_llama""... | 562 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( _lowerCAmelCase ... | 562 | 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 _lowerCAmelCase ( __UpperCamelCase ):
"""simple ... | 654 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase = 200 ) -> int:
lowerCamelCase__ : Dict = [1, 2, 5, 10, 20, 50, 100, 200]
lowerCamelCase__ : Union[str, Any] = [0] * (pence + 1)
lowerCamelCase__ : List[str] = 1 # base case: 1 way to make 0 ... | 295 | 0 |
def lowerCamelCase_ ( UpperCamelCase__ : List[Any] = 50 ):
'''simple docstring'''
UpperCamelCase__ = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2, 5 ):
for tile_start in r... | 712 | from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list[float], UpperCamelCase__ : int ):
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(UpperCamelCase__ ):
... | 591 | 0 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
UpperCamelCase = TypeVar("T")
def A ( lowercase__ : int ) -> int:
return (position - 1) // 2
def A ( lowercase__ : int ) -> int:
return (2 * position) + 1
def A ... | 45 |
"""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 th... | 361 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__magic_name__ : List[Any] = logging.get_logger(__name__)
__mag... | 602 |
'''simple docstring'''
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__magic_name__ : Optional[int] = 50_000
__magic_name__ : Tuple = 5_000
__magic_name__ , __magic_name__ : List[Any] = ... | 602 | 1 |
from __future__ import annotations
class _snake_case :
def __init__( self: List[Any] , __lowerCamelCase: Union[str, Any]=None ) -> Dict:
__UpperCAmelCase : List[Any] = data
__UpperCAmelCase : Tuple = None
... | 382 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''MIT/ast-finetuned-audioset-10-10-0.4593''': (
'''https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.js... | 382 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpo... | 661 | import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available()... | 661 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ... | 633 |
"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_u... | 633 | 1 |
"""simple docstring"""
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
# Fitting Polynomial Regression to the dataset
from sklearn.prepr... | 690 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase__ ="\nRecall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:\nRecall = TP / (TP + FN)\nWhere TP is the true positives ... | 690 | 1 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
_UpperCAmelCase : int = TypeVar('''T''')
class __magic_name__ ( Generic[T] ):
... | 72 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A ... | 52 | 0 |
"""simple docstring"""
import math
def lowercase__ ( snake_case_ :int ):
if not isinstance(snake_case_ , snake_case_ ):
__UpperCAmelCase = F'''Input value of [number={number}] must be an integer'''
raise TypeError(snake_case_ )
if number < 1:
__... | 717 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : List[Any] = logging.get_logger(__name__)
_lowercase : Tuple = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config... | 397 | 0 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
... | 28 |
# 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 b... | 165 | 0 |
'''simple docstring'''
import argparse
import os
from . import (
ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BART_PRETRAINED_MODEL_ARCHIVE_LIST,
BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE... | 88 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def _lowerCAmelCase ( ) -> List[Any]:
lowercase : Tuple =HfArgumentParser(__magic_name__ )
lowercase : Union[str, Any] =parser.... | 88 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
a_ = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wor... | 76 |
'''simple docstring'''
import operator as op
def lowerCAmelCase_ ( __A : int ):
'''simple docstring'''
snake_case: List[Any] = []
snake_case: Optional[Any] = lambda __A , __A : int(x / y ) # noqa: E731 integer division opera... | 329 | 0 |
from PIL import Image
def _lowerCamelCase ( snake_case ):
_lowerCAmelCase = image.size
_lowerCAmelCase = 0
_lowerCAmelCase = image.load()
for i in range(snake_case_ ):
for j in range(snake_case_ ):
... | 703 | 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 ...test_configurati... | 225 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase = 1_000_000 ) -> int:
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = 1
SCREAMING_SNAKE_CASE__ = {1: 1}
for inputa in range(2 , __UpperCAmelCase ):
SCREAMIN... | 159 |
import math
snake_case__ = 10
snake_case__ = 7
snake_case__ = BALLS_PER_COLOUR * NUM_COLOURS
def lowerCamelCase__ ( a : int = 20 ) -> str:
"""simple docstring"""
a__ :List[str] = math.comb(a , a )
a__ :Optional[int] ... | 395 | 0 |
from __future__ import annotations
import os
from collections.abc import Mapping
_snake_case = tuple[int, int]
class lowerCAmelCase_ :
"""simple docstring"""
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
__Up... | 719 |
import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def _a ( ) -> Union[str, Any]:
... | 567 | 0 |
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