code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.... | 86 |
"""simple docstring"""
import sys
lowerCAmelCase__ = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
... | 645 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE__ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ = {
"""microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
# See all BioGPT models at... | 709 |
from __future__ import annotations
from cmath import sqrt
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int , __lowerCamelCase: int , __lowerCamelCase: int ):
'''simple docstring'''
if a == 0:
raise ValueError("Coefficient 'a' must not be zero." )
lowercase_ = b ... | 601 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.... | 100 |
def __snake_case ( ) -> int:
return 1
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else two_pence(x - 2 ) + one_pence()
def __snake_case ( lowerCAmelCase_ ) -> int:
return 0 if x < 0 else five_pence(x - 5 ) +... | 100 | 1 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 ... | 93 | """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 requir... | 93 | 1 |
'''simple docstring'''
from argparse import ArgumentParser, Namespace
from ..utils import logging
from . import BaseTransformersCLICommand
def _lowerCamelCase ( lowercase : Any ) -> Union[str, Any]:
return ConvertCommand(
args.model_type , args.tf_chec... | 692 | """simple docstring"""
import numpy
# List of input, output pairs
SCREAMING_SNAKE_CASE__ : Optional[Any] =(
((5, 2, 3), 15),
((6, 5, 9), 25),
((11, 12, 13), 41),
((1, 1, 1), 8),
((11, 12, 13), 41),
)
SCREAMING_SNAKE_CASE__ : str =(((515, 22, 13), 555), ((61, 35,... | 434 | 0 |
# 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... | 657 |
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
PILImageResampling,
get_image_s... | 657 | 1 |
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_in... | 2 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__magic_name__ : Any = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_... | 281 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}
try:
if not is_torch_available():
raise Opt... | 49 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 1 |
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> List[str]:
'''simple docstring'''
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
lowercase__ : str = mf_knapsack(i - 1 , lowercase_ , ... | 12 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Stabl... | 698 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_availa... | 712 |
"""simple docstring"""
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from p... | 401 | 0 |
"""simple docstring"""
import json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .con... | 673 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
__magic_name__ = "<<<<<<< This should probably be modified because it mentions: "
__magic_name__ = ... | 155 | 0 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase ):
snake_case__ = Sw... | 530 |
from __future__ import annotations
from fractions import Fraction
def SCREAMING_SNAKE_CASE__ ( __lowerCAmelCase , __lowerCAmelCase ):
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def SCREAMING_SNAKE_CASE__ ( __lowerCAme... | 530 | 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_start_do... | 93 | from math import isqrt, loga
def lowerCAmelCase__ ( a__ ) ->list[int]:
'''simple docstring'''
_UpperCamelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , a__ , a__ ):
_Upper... | 547 | 0 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from tra... | 716 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def SCREAMING_SNAKE_CASE ( ):
lowercase = HfArgumentParser(lowercase_ )
lowercase = parser.parse_args_into_dataclasses()[0]
lowerca... | 653 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import Hugging... | 354 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
lowercase_ = logging.get_logge... | 354 | 1 |
'''simple docstring'''
import qiskit
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCamelCase : List[str] = qiskit.Aer.get_backend("aer_simulator" )
# Create a Quantum Circ... | 713 | '''simple docstring'''
from __future__ import annotations
lowerCAmelCase_ : Optional[Any] = """Muhammad Umer Farooq"""
lowerCAmelCase_ : str = """MIT"""
lowerCAmelCase_ : Optional[Any] = """1.0.0"""
lowerCAmelCase_ : Union[str, Any] = """Muhammad Umer Farooq"""
lowerCAmelCa... | 204 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_... | 474 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case : Optional[int] = logging.get_logger(__name__)
snake_case : Optional[int] = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://h... | 445 | 0 |
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__snake_case = [
"""EAGER""",
"""AOT_EAGER""",
"""INDUCTOR""",
"""NVFUSER""",
"""AOT_NVFUSER"""... | 718 | '''simple docstring'''
def A_ ( SCREAMING_SNAKE_CASE_ = "The quick brown fox jumps over the lazy dog" , ) ->bool:
lowercase_ = set()
# Replace all the whitespace in our sentence
lowercase_ = input_str.replace(""" """ , """""" )
for alpha in input_str:
if "a" <= alpha.lower(... | 603 | 0 |
from copy import deepcopy
class A_ :
"""simple docstring"""
def __init__( self : Optional[int] ,__A : list[int] | None = None ,__A : int | None = None ) -> None:
if arr is None and size i... | 67 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
snake_case = Lock()
def SCREAMING_SNAKE_CASE__ ( snake_case__ :Optional[int] , snake_case__ :Union[str, Any] , snake_case__ :Tuple ... | 67 | 1 |
"""simple docstring"""
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_a : int= Lock()
def __UpperCAmelCase ( UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Li... | 709 | """simple docstring"""
import math
import tensorflow as tf
from packaging import version
def __UpperCAmelCase ( UpperCAmelCase_ : Union[str, Any] ) -> Any:
'''simple docstring'''
__snake_case : List[str] = tf.convert_to_tensor(Up... | 192 | 0 |
'''simple docstring'''
UpperCamelCase_ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowercase__( __UpperCamelCase: int ,__UpperCamelCase: Dict... | 28 |
"""simple docstring"""
a__ : Optional[int] = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
... | 589 | 0 |
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, pa... | 303 |
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
lowerCamelCase : str = 2_9_9_7_9_2_4_5_8
# Symbols
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase : Union[str, Any] = symbols('ct x y z')
def lowercase... | 303 | 1 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ ,lowerCAmelCase__ ):
if len(lowerCAmelCase__ ) == 0:
return False
lowerCamelCase_ : Union[str, Any] = len(lowerCAmelCase__ ) // 2
if a_list[midpoint] == item:
ret... | 364 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
_lowercase : List[str] =5_0000
_lowercase : str =5000
_lowercase , _lowercase : List[str] =os.path.split(__file__)
_lowercase : Union[str, A... | 364 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
import yaml
UpperCAmelCase__ ="docs/source/en/_toctree.yml"
def lowerCAmelCase_ ( UpperCamelCase__ : str ):
"""simple docstring"""
__lowercase = defaultdict(UpperCamelC... | 442 |
"""simple docstring"""
def lowerCAmelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : list ):
"""simple docstring"""
_enforce_args(UpperCamelCase__ , UpperCamelCase__ )
if n == 0:
return 0
__lowercase = float("""-inf""" )
for i in range(1 ,... | 442 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[Any] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vivit-b-16x2-kine... | 529 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = prime_factors(A_ )
if is_square_free(A_ ):
return -1 if len(A_ ) % 2 else 1
return 0
if __name_... | 529 | 1 |
import json
import os
from typing import Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''vocab_file''': '''vocab.json''',
'''merges_file''': '''merges.txt''',
}
... | 219 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json''',
'''microsoft/markuplm-large''': '''https://huggingfac... | 219 | 1 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.uti... | 636 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _UpperCamelCase ( lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Tuple =(... | 636 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase : Optional[Any] = logging.get_logger(__name__)
lowercase : Optional[int] = {
'microsoft/swinv2-tiny-patch4-window8-256': (
'https://huggingface.co/microsoft/swinv2-tiny... | 94 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Dict , _lowerCamelCase : Tuple , _lowerCa... | 94 | 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 logging
SCREAMING_SNAKE_CASE__ : List[str] = logging.get_logger(__name__)
... | 298 |
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 TFXLMRobertaModel
... | 298 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_A : List[Any] ={}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 4 |
'''simple docstring'''
_A : Optional[Any] ='''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''
def __UpperCamelCase ( _lowercase ) -> bytes:
# Make sure the supplied data is a bytes-like object
if not isinstance(_lowercase, _lowercase ):
_lo... | 4 | 1 |
a = {
"a": "AAAAA",
"b": "AAAAB",
"c": "AAABA",
"d": "AAABB",
"e": "AABAA",
"f": "AABAB",
"g": "AABBA",
"h": "AABBB",
"i": "ABAAA",
"j": "BBBAA",
"k": "ABAAB",
"l": "ABABA",
"m": "ABABB",
"n": "ABBAA",
"o": "ABBAB",
"p... | 518 |
# 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
# full vocab, merges ... | 518 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Optional[Any] = logging.get_logger(__name__)
A_ : List[Any] = {
'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
# See all BioGPT models at https://huggingfac... | 64 |
class _a :
'''simple docstring'''
def __init__( self ):
A__ : str = """"""
A__ : Any = """"""
A__ : List[Any] = []
def __A ( self , A__ , A__ ):
if m == -1:
return n + 1
... | 64 | 1 |
def __lowerCAmelCase( _SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
_A = F"Input value of [number={number}] must be an integer"
raise TypeError(_S... | 27 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A_ : Optional[Any] ={
'''configuration_squeezebert''': [
'''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 274 | 0 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_lowerCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# T... | 704 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase = logging.get_logger(__name__)
_lowerCAmelCase = {
"microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
# See all BioGPT models at... | 245 | 0 |
'''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 as tf
... | 459 |
'''simple docstring'''
import random
from typing import Any
def a_ ( _lowerCAmelCase ) -> list[Any]:
for _ in range(len(_lowerCAmelCase ) ):
__lowerCamelCase : Optional[Any] = random.randint(0 ,len(_lowerCAmelCase ) - 1 )
__lowerCamelCase : str ... | 459 | 1 |
'''simple docstring'''
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_image_inputs
if is_to... | 702 |
'''simple docstring'''
from __future__ import annotations
import sys
from collections import deque
from typing import Generic, TypeVar
__lowerCamelCase : Optional[int] = TypeVar('T')
class UpperCAmelCase ( Generic[T]):
"""simple docstring"""
lowerCAmelCase_ = 42 ... | 271 | 0 |
"""simple docstring"""
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase ... | 174 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .ut... | 174 | 1 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class _SCREAMING_SNAKE_CASE :
def __init__( self : Tuple , __UpperCamelCase : Any ) -> Any:
"""simple docstring"""
snake_case__ : Any = str(id_ )
... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 574 | 0 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : Optional[Any] ={
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConf... | 148 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( snake_case ):
UpperCAmelCase : str = (CMStochasticIterativeScheduler,)
UpperCAmelCase : int ... | 350 | 0 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __snake_case : str = "" ) -> dict[str, float]:
lowercase : Optional[int] = url or "https://www.imdb.com/ch... | 518 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
_A : int = 20_48
_A : List[Any] = 40_96
_A : Any = 42
_A : List[Any] = os.environ.pop("""PROCESS_TRAIN""", """false""")
_A : Union[str, Any] = {"""null""": 0, """sh... | 518 | 1 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
... | 92 |
from __future__ import annotations
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if len(_SCREAMING_SNAKE_CASE ) < k or k < 0:
raise ValueError('Invalid Input' )
lowercase__ = lowercase__ = sum(a... | 235 | 0 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import ... | 109 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A = {
'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}
try:
if not is_to... | 109 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
__a: Tuple = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2former... | 108 |
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)
__magic_name__ =... | 250 | 0 |
'''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
a__ : Dict =False
try:
a__ ... | 434 |
'''simple docstring'''
a__ : dict[str, float] ={
"km/h": 1.0,
"m/s": 3.6,
"mph": 1.609_344,
"knot": 1.852,
}
a__ : dict[str, float] ={
"km/h": 1.0,
"m/s": 0.277_777_778,
"mph": 0.621_371_192,
"knot": 0.539_956_803,
}
def lowercase__ ( _... | 434 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : str , _snake_case : str ) -> int:
'''simple docstring'''
if len(_snake_case ) != len(_snake_case ):
raise ValueError('String lengths must match!' )
_A = 0
for chara,... | 7 |
"""simple docstring"""
import math
from datetime import datetime, timedelta
def _snake_case ( _snake_case : int ) -> datetime:
'''simple docstring'''
_A = year % 19
_A = year % 4
_A = year % 7
_A = math.floor(year / 1_00 )
... | 7 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Optional[int] = logging.get_logger(__name__)
__UpperCAmelCase : Union[str, Any] = {
'disti... | 249 |
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 ...test_con... | 249 | 1 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_co... | 101 |
'''simple docstring'''
def lowerCamelCase__ ( a ):
__snake_case = int(a )
if n_element < 1:
__snake_case = ValueError('a should be a positive number' )
raise my_error
__snake_case = [1]
__snake_case , __sn... | 356 | 0 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase__ ( a_ : str , a_ : list[str] | None = None , a_ : dict[str, float] | None = None , a_ : bool = False , ) -> tuple[int, float, str]:
UpperCAmelCase__ : List[Any] ... | 599 |
'''simple docstring'''
from collections import Counter
from timeit import timeit
def lowerCAmelCase__ ( a_ : str = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def lowerCAmelCase__ ( a_ : ... | 599 | 1 |
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 import (
AutoProcessor,
... | 101 |
"""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
_A : str = False
try... | 361 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowercase = {'''tokenization_bertweet''': ['''BertweetTokenizer''']}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
_lowercase = _LazyModule(__name__, globals()['''_... | 714 |
from __future__ import annotations
from random import random
class __snake_case :
"""simple docstring"""
def __init__( self : Optional[int] ,lowerCAmelCase__ : int | None = None ) -> int:
'''simple docstring'''
lowerCAmelCase_ : Dict ... | 683 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = {
"andreasmadsen/efficient_mlm_m0.40": (
"https://huggingf... | 424 | from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
snake_case = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui Wu and Mike Schu... | 424 | 1 |
'''simple docstring'''
def snake_case_ ():
UpperCAmelCase = []
UpperCAmelCase = 1
while len(_a ) < 1E6:
constant.append(str(_a ) )
i += 1
UpperCAmelCase = ''''''.join(_a )
return (
int(constant[0] )
* int(constant[9] )
... | 358 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffus... | 358 | 1 |
'''simple docstring'''
import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, Lis... | 432 |
'''simple docstring'''
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_lowerCAmelCase = 0B10_11_00_11_11_10_11_00_10_01_00_00_... | 432 | 1 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
__snake_case = """\
@inproceedings{wang2019glue,
title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
author={Wang, Alex and Singh, Amanpr... | 701 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import M... | 400 | 0 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transform... | 397 |
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 ANY
if is_vision_avai... | 354 | 0 |
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReason... | 508 |
'''simple docstring'''
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGene... | 508 | 1 |
from datetime import datetime
import requests
def lowercase_ (A : str ):
snake_case__ : Dict = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
snake_case__ : Any = requests.get(base_url + url ).json()[0]['urls'][0]['src']
... | 478 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
a_ :List[Any] = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_torc... | 478 | 1 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_p... | 706 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
lowercase__ =logging.getLogger(__name__)
... | 511 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 1_00 ) -> int:
__lowerCamelCase : Union[str, Any] = n * (n + 1) * (2 * n + 1) / 6
__lowerCamelCase : Union[str, Any] = (n * (n + 1) / 2) ** 2
return ... | 13 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_available... | 392 | 0 |
from __future__ import annotations
from typing import Any
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
pass
class UpperCamelCase :
'''simple docstring'''
def __init__( self , UpperCa... | 441 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
f... | 441 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import Pr... | 87 |
class UpperCamelCase_ : # Public class to implement a graph
'''simple docstring'''
def __init__( self : str , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : list[list[bool]]) ->None:
'''simple docstring'''... | 87 | 1 |
from __future__ import annotations
def UpperCamelCase ( lowerCAmelCase_ ) -> float:
'''simple docstring'''
if not nums:
raise ValueError('List is empty' )
return sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )
if __name__ == "__main__":
impo... | 703 | UpperCAmelCase_ = {
'''Pillow''': '''Pillow<10.0.0''',
'''accelerate''': '''accelerate>=0.20.3''',
'''av''': '''av==9.2.0''',
'''beautifulsoup4''': '''beautifulsoup4''',
'''black''': '''black~=23.1''',
'''codecarbon''': '''codecarbon==1.2.0''',
'''cookiecutter''': '''cook... | 476 | 0 |
from __future__ import annotations
import pandas as pd
def UpperCAmelCase__ ( __snake_case , __snake_case , __snake_case ) -> list[int]:
_A = [0] * no_of_processes
_A = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in ... | 317 |
from __future__ import annotations
def UpperCAmelCase__ ( __snake_case , __snake_case ) -> bool:
_A = get_failure_array(__snake_case )
# 2) Step through text searching for pattern
_A , _A = 0, 0 # index into text, pattern
while i < len(__snake... | 317 | 1 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_... | 714 |
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTes... | 648 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __a ( unittest.TestCase ):
def UpperCAmelCase__ ( self : Any ):
'''simple docstring... | 109 |
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[str] ) -> Optional[int]:
UpperCamelCase__ : Union[str, Any] = [1]
for i in range(2 , __UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert... | 253 | 0 |
"""simple docstring"""
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class __magic_name__ ( SCREAMING_SNAKE_CASE__ ):
UpperCamelCase_ = (KDPMaDiscreteScheduler,)
UpperCamelCas... | 715 |
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import req... | 272 | 0 |
'''simple docstring'''
import numpy as np
import datasets
_UpperCamelCase : str = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclide... | 541 |
'''simple docstring'''
_UpperCamelCase : Dict = range(2, 20 + 1)
_UpperCamelCase : str = [10**k for k in range(ks[-1] + 1)]
_UpperCamelCase : dict[int, dict[int, list[list[int]]]] = {}
def __UpperCAmelCase ( A : List[Any] , A : str , ... | 541 | 1 |
from __future__ import annotations
from math import pow, sqrt
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if (resistance, reactance, impedance).count(0) != 1:
raise ValueError("""One and only one argument must be 0""")
... | 716 |
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
snake_case__ : Any = 1
snake_case__ : Dict = 2
while i * i <= n:
snake_case__ : Dict = 0
while n % i == 0:
n //= i
multiplicity += 1
n_div... | 127 | 0 |
import argparse
import os
import transformers
from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : int = logging.get_logger(__name__)
__lowerCAmelCase : int = {name: getattr(transformers,... | 529 |
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 a_ ( ) -> Dict:
... | 246 | 0 |
from string import ascii_uppercase
SCREAMING_SNAKE_CASE : int = {char: i for i, char in enumerate(ascii_uppercase)}
SCREAMING_SNAKE_CASE : str = dict(enumerate(ascii_uppercase))
def UpperCamelCase ( _a , _a ) -> str:
'''simple do... | 704 |
import unittest
import numpy as np
from transformers import BertConfig, 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
if is_flax_available():
from transf... | 441 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A ( nn.Module ):
lowercase_ = 42
lowe... | 22 |
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 cached_property
fr... | 250 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFo... | 283 | """simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_... | 283 | 1 |
"""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 import AutoProcessor, BlipaProcess... | 426 |
"""simple docstring"""
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
SCREAMING_SNAKE_CASE_ ... | 426 | 1 |
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
snake_case_ : Any = "http://www.mocksite.com/file1.txt"
sna... | 704 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case_ : List[Any] = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must... | 253 | 0 |
import requests
from bsa import BeautifulSoup
def lowercase_ ( _UpperCamelCase = "AAPL" ):
'''simple docstring'''
__lowercase = F'https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'
__lowercase = BeautifulSoup(requests.get(_UpperCamelCase ).text , '''html.parse... | 639 |
# 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 vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to t... | 639 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import re... | 204 | '''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
low... | 204 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_availabl... | 614 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
_SCREAMING_SNAKE_CASE : int = logging... | 550 | 0 |
'''simple docstring'''
import os
def __lowerCamelCase ( ) -> Tuple:
"""simple docstring"""
with open(os.path.dirname(A__ ) + '/p022_names.txt' ) as file:
UpperCamelCase = str(file.readlines()[0] )
UpperCamelCase ... | 324 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class ... | 324 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers imp... | 135 |
'''simple docstring'''
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 imp... | 135 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, 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_... | 251 |
"""simple docstring"""
__UpperCAmelCase = {
'''meter''': '''m''',
'''kilometer''': '''km''',
'''megametre''': '''Mm''',
'''gigametre''': '''Gm''',
'''terametre''': '''Tm''',
'''petametre''': '''Pm''',
'''exametre''': '''Em''',
'''zettametre''': '''Zm''',
'''yottametr... | 251 | 1 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( __snake_case : list ):
if len(__snake_case ) <= 1:
return [tuple(__snake_case )]
_A = []
def generate(__snake_case : int , __snake_case : list ):
if k == 1:
r... | 107 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class UpperCAmelCase ( _snake_case ... | 467 | 0 |
'''simple docstring'''
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from t... | 174 | '''simple docstring'''
import requests
__snake_case : int = 'YOUR API KEY'
def _UpperCAmelCase ( _UpperCamelCase : str, _UpperCamelCase : str = giphy_api_key ) -> list:
A_ = '''+'''.join(query.split() )
A_ = F'''https... | 174 | 1 |
import functools
from typing import Any
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
# Validation
if not isinstance(lowerCamelCase_ , lowerCamelCase_ ) or len(lowerCamelCase_ ) == 0:
raise ValueError('''the string should be not em... | 542 |
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 : Optional[int] = logging.get_logger(__name__)
... | 542 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
_lowerCAmelCase : str = loggi... | 717 |
'''simple docstring'''
import socket
def _A ( ):
snake_case__ : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
snake_case__ : str = socket.gethostname()
snake_case__ : Union[str, Any] = 1_23_12
sock.connect((host, port) )
sock.send(B'''Hello server!''' ... | 694 | 0 |
import cva
import numpy as np
class lowercase_ :
def __init__( self , lowercase_ , lowercase_ ):
if k in (0.04, 0.06):
_snake_case : List[Any] = k
_snake_case : int = window_size
else:
... | 670 | from manim import *
class lowercase_ ( __snake_case ):
def UpperCamelCase ( self ):
_snake_case : Tuple = Rectangle(height=0.5 , width=0.5 )
_snake_case : List[str] = Rectangle(height=0.46 , width=0.46 ).se... | 670 | 1 |
'''simple docstring'''
from pathlib import Path
import numpy as np
from PIL import Image
def a__ ( lowerCAmelCase__ ) -> np.ndarray:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
re... | 312 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/main/config.j... | 312 | 1 |
_lowerCAmelCase : Tuple ="""
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
_lowerCAmelCase : Option... | 113 |
_lowerCAmelCase : int ="""
# 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.com/huggingface/transformers.g... | 113 | 1 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
T... | 718 |
def UpperCAmelCase ( _lowerCamelCase : int = 1_000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = -1
SCREAMING_SNAKE_CASE__ : str = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2... | 26 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_imag... | 420 | """simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def lowercase ( a__ : int = 1000000 , a__ : int = 10 ) -> int:
_UpperCamelCase = defaultdict(a__ )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if ou... | 420 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import ... | 456 |
def __lowerCAmelCase ( __lowerCamelCase : int = 3 , __lowerCamelCase : int = 7 , __lowerCamelCase : int = 1000000 ) -> int:
__lowerCAmelCase =0
__lowerCAmelCase =1
for current_denominator in range(1 , limit + 1 ):
__lowerCAmelCase =current_deno... | 456 | 1 |
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ... | 493 |
def UpperCAmelCase_ ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = int(_A )
if n_element < 1:
SCREAMING_SNAKE_CASE__ = ValueError('''a should be a positive number''' )
raise my_error
SCREAMING_SNAKE_CASE__ ... | 493 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..t... | 701 |
"""simple docstring"""
from typing import Any
def lowercase__ ( snake_case_ :list , snake_case_ :list , snake_case_ :dict , snake_case_ :dict , snake_case_ :dict , ):
_validation(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_... | 397 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _snake_case :
UpperCamelCase__ : int
UpperCamelCase__ : int
class _snake_case :
def __init__( self ... | 413 |
"""simple docstring"""
from __future__ import annotations
def _snake_case ( UpperCamelCase : list[int] , UpperCamelCase : int ):
if len(UpperCamelCase ) < k or k < 0:
raise ValueError("""Invalid Input""" )
UpperCAmelCase : Optional[Any] = sum(array[:k] )
for i in r... | 160 | 0 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, rand... | 700 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : List[Any] ):
"""simple docstring"""
UpperCAmelCase_ : str = [0 for i in range(r + 1 )]
# nc0 = 1
UpperCAmelCase_ : Union[str, ... | 389 | 0 |
def __a ( __lowerCAmelCase = 1000 ) -> int:
SCREAMING_SNAKE_CASE : Optional[int] = 2**power
SCREAMING_SNAKE_CASE : Any = str(__lowerCAmelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = list(__lowerCAmelCase )
SC... | 352 |
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, 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,
)... | 352 | 1 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
"microsoft/unispeech-large-1500h-cv": (
"https://huggingface.c... | 426 |
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_... | 426 | 1 |
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
lowerCamelCase__ = logging.getLogger()
@unittest.skip("""Temporarily disable the doc tests.""" ... | 524 | from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
lowerCamelCase__ = 1.0_54_57_18_17E-34 # unit of ℏ : J * s
lowerCamelCase__ = 3E8 # unit of c : m * s^-1
def _lowerCamelCase( ... | 524 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE__ = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
el... | 707 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDM... | 35 | 0 |
import math
def __lowerCamelCase ( _lowerCAmelCase , _lowerCAmelCase ) -> int:
_UpperCAmelCase = len(UpperCAmelCase_ )
_UpperCAmelCase = int(math.floor(math.sqrt(UpperCAmelCase_ ) ) )
_UpperCAmelCase = 0
while arr[min(UpperCAmelCase_ , ... | 684 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UN... | 368 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
"""configuration_jukebox""": [
"""JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""JukeboxConfig""",
"""JukeboxPriorConfig""",
"""JukeboxVQVAEConfig"... | 286 | import json
import os
import unittest
from typing import Tuple
from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from tran... | 286 | 1 |
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