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 |
|---|---|---|---|---|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
loggin... | 569 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.... | 569 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : List[Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/config.json",
"google/fnet-large": "... | 453 |
def UpperCAmelCase__ ( lowerCamelCase ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
lowercase :str = 1
lowercase :Tuple = 1
while repunit:
lowercase :Dict = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
de... | 453 | 1 |
'''simple docstring'''
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models... | 507 |
'''simple docstring'''
from typing import Optional
from torch import nn
from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class a__ ( nn.Module ):
def __init__(self : Union[str, Any], __UpperCAmelCase : int = 16, __UpperCAmelCase : int = 88, ... | 507 | 1 |
from __future__ import annotations
from typing import Any
class a__ :
def __init__( self , UpperCAmelCase = 6 ) -> None:
__a = None
__a = None
self.create_linked_list(UpperCAmelCase )
def __SCREAMING_SNAKE_CASE (... | 246 | from __future__ import annotations
lowerCamelCase_ : List[Any] = {
"""A""": ["""B""", """C""", """E"""],
"""B""": ["""A""", """D""", """E"""],
"""C""": ["""A""", """F""", """G"""],
"""D""": ["""B"""],
"""E""": ["""A""", """B""", """D"""],
"""F""": ["""C"""],
"""G""": ["""C"... | 246 | 1 |
"""simple docstring"""
def lowercase_ ( _lowercase : int = 10**12 ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = 1
UpperCAmelCase : List[str] = 0
UpperCAmelCase : Dict = 1
UpperCAmelCase : Union[str, An... | 595 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
lowercase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase = [... | 272 | 0 |
import os
import time
import numpy as np
import onnxruntime as ort
a = '''1'''
a = '''0'''
a = '''1'''
a = ort.SessionOptions()
a = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
a = ['''TensorrtExecutionProvider''', '''CU... | 709 |
import cva
import numpy as np
class UpperCamelCase__ :
def __init__( self : List[str] , UpperCamelCase__ : float , UpperCamelCase__ : int ):
'''simple docstring'''
if k in (0.04, 0.06):
lowercas... | 650 | 0 |
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.functiona... | 73 |
'''simple docstring'''
from math import factorial
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> float:
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
... | 75 | 0 |
def A ( lowercase = 4_000_000 ) -> int:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase , UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(lowercase )
UpperCamelCase , UpperCamelCase = b, a + b
return sum(lower... | 714 |
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
_UpperCAmelCase : Any = "\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and ... | 3 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfi... | 582 |
'''simple docstring'''
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 BioG... | 582 | 1 |
'''simple docstring'''
from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def __lowerCAmelCase ( snake_case : str = "isbn/0140328726" ) -> dict:
__lowerCamelCase: Tuple = olid.strip().strip("""/""" ) # Remove leadin... | 715 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, tor... | 189 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Rea... | 126 |
'''simple docstring'''
import os
from datetime import datetime as dt
from github import Github
_A: Any = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def _lowerCAmelCase ( )-> Optional[int]:
__UpperCAmelCase = Github(os.environ['GITHUB_TOKEN... | 126 | 1 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerB... | 721 | """simple docstring"""
import argparse
import ast
import logging
import os
import sys
import pandas as pd
import torch
from tqdm import tqdm
from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_loggin... | 635 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ : Union[str, Any] = {
'''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
'''tokeniza... | 105 |
def lowerCAmelCase_ ( _lowercase : list , _lowercase : int , _lowercase : int = 0 , _lowercase : int = 0) -> int:
"""simple docstring"""
a__ : str = right or len(_lowercase) - 1
if left > right:
return -1
elif list_dat... | 136 | 0 |
"""simple docstring"""
import string
from math import logaa
def _UpperCamelCase ( _A , _A ) -> int:
"""simple docstring"""
_UpperCAmelCase = document.translate(
str.maketrans("""""" , """""" , string.punctuation ) ... | 713 |
"""simple docstring"""
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ... | 19 | 0 |
from __future__ import annotations
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ):
"""simple docstring"""
UpperCAmelCase = len(__UpperCAmelCase )
# If row is equal to the size of the bo... | 333 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acc... | 501 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( A__ , A__ ) -> bool:
"""simple docstring"""
lowerCamelCase__ = len(A__ )
lowerCamelCase__ = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not ... | 274 |
"""simple docstring"""
def UpperCAmelCase__ ( A__ ) -> list[int]:
"""simple docstring"""
if length <= 0 or not isinstance(A__ , A__ ):
raise ValueError("Length must be a positive integer." )
return [n * (2 * n - 1) for n in range(A__ )]
if __name__ == "__main__":
... | 274 | 1 |
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 __a ( A__ : Any ):
SCREAMING_SNAKE_CASE ... | 16 |
from collections.abc import Callable
import numpy as np
def __a ( A__ : Callable , A__ : float , A__ : float , A__ : float , A__ : float ):
SCREAMING_SNAKE_CASE = int(np.ceil((x_end - xa) / step_size ) )
SCREAMING_SNAKE_CASE ... | 16 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Any = logging.get_logger(__name__)
A__ : str = {
"hustvl/yolos-s... | 705 |
from math import factorial, radians
def _lowercase ( a_ : float ,a_ : int = 1_8 ,a_ : int = 1_0 ) -> float:
'''simple docstring'''
__magic_name__ = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees ... | 184 | 0 |
import os
import string
import sys
a_ = 1 << 8
a_ = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_FLAG,
"""right""": 67 + ARROW_KEY_FLAG,
"""left""": 68 + ARROW_KEY_FLAG,
... | 175 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}
try:
if not is_torch_available():... | 296 | 0 |
'''simple docstring'''
import logging
import os
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
from tqdm import auto as tqdm_lib
A_ ... | 709 |
'''simple docstring'''
import random
def A_ ( snake_case , snake_case , snake_case = False ):
SCREAMING_SNAKE_CASE:dict = {i: [] for i in range(snake_case )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >... | 465 | 0 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join ... | 408 |
from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
MaskedBertForMultipleChoice,
MaskedBertForQuestionAnswering,
MaskedBertForSequenceClassification,
MaskedBertForTokenClassification,
MaskedBertModel,
)
from .modules import *
| 243 | 0 |
import collections
import importlib.util
import os
import re
from pathlib import Path
_SCREAMING_SNAKE_CASE = """src/transformers"""
# Matches is_xxx_available()
_SCREAMING_SNAKE_CASE = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
_S... | 534 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
# Load... | 534 | 1 |
'''simple docstring'''
lowerCAmelCase : Tuple ='''Input must be a string of 8 numbers plus letter'''
lowerCAmelCase : Optional[Any] ='''TRWAGMYFPDXBNJZSQVHLCKE'''
def UpperCAmelCase_ ( __lowerCamelCase : str ):
if not isinstance(__lowerCamelCase ,__lowerCame... | 172 |
'''simple docstring'''
def UpperCAmelCase_ ( __lowerCamelCase : int ):
if number > 0:
raise ValueError("input must be a negative integer" )
lowercase_ :Optional[Any] = len(bin(__lowerCamelCase )[3:] )
lowercase_ :Optional[int] ... | 172 | 1 |
'''simple docstring'''
import os
def A (__lowerCamelCase :Dict ):
_lowerCAmelCase = len(grid[0] )
_lowerCAmelCase = len(__lowerCamelCase )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
# Check vertically, hor... | 162 |
'''simple docstring'''
def A (__lowerCamelCase :int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
_lowerCAmelCase = f'Input value of [number={number}] must be an integer'
raise TypeError(__lowerCamelCase )
if number < 1:
_lowerCAmelC... | 162 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepI... | 261 |
'''simple docstring'''
def _A ( snake_case__ : list[int] , snake_case__ : list[int] ):
snake_case__ : Tuple = len(snake_case__ )
print('''The following activities are selected:''' )
# The first activity is always selected
snake_case__ : Optional[Any] ... | 261 | 1 |
import re
import string
import numpy as np
import datasets
_lowerCamelCase : Tuple = "\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"
_lowerCamelCase :... | 703 |
from collections.abc import Generator
def _a ( ) -> Generator[int, None, None]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ : Any = 0, 1
while True:
SCREAMING_SNAKE_CASE__ ,SCREA... | 157 | 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 ... | 162 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversationa... | 162 | 1 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
UpperCAmelCase__ : int = ''
UpperCAmelCase__ : List[str] = ''
UpperCAmelCase__ : List[str] = ''
UpperCAmelCase__ : List[str] = 1 # (0 is vertical, 1 is horizontal)
def _A ... | 710 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch
... | 416 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> list:
'''simple docstring'''
if len(lowercase_ ) == 0:
return []
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = min(lowercase_ ),... | 462 |
from __future__ import annotations
import requests
lowerCAmelCase = set(
"""approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc down... | 462 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
UpperCAmelCase : Any = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 47 |
'''simple docstring'''
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers i... | 47 | 1 |
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 transformers... | 66 |
"""simple docstring"""
import functools
def A ( _A, _A ):
"""simple docstring"""
snake_case_ :Optional[Any] = len(_A )
snake_case_ :Optional[int] = len(_A )
@functools.cache
def min_distance(_A, _A ) -> int:
# if firs... | 584 | 0 |
"""simple docstring"""
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def __lowercase ( _a , _a , _a ):
snake_case_ : Tuple = AutoConfig.from_pretrained(_a )
snake_case_ : Tuple = FlaxAutoModelF... | 485 |
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transform... | 485 | 1 |
'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_UpperCAmelCase : Union[str, Any] = (720, 1280) # Height, Width
_UpperCAmelCase : str = (0.4, 0.6) # if height or width lower than this scale, drop it.
... | 683 |
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class UpperCA... | 683 | 1 |
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params... | 648 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificat... | 648 | 1 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@requir... | 206 |
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
_UpperCamelCase : Union[str, Any] ={'UserAgent': UserAgent().random}
def a__ (__lowercase :Optional[Any] ) -> dict:
_A : str = ... | 206 | 1 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class lowercase__ ( unittest.TestCase ):
def _UpperCAmelCase ( self : str ):
"""simple docstring"""
UpperCAmelCase__ = [... | 277 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
A = logging.get_logger(__name__)
class lowercase__ ( __SCREAMING_SNAKE_CASE ):
def __init__( self : Union[str, Any] , *_lowercase : Any , *... | 277 | 1 |
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
return base * power(__lowercase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print("Raise base to the power of exponent using recursion...")
UpperCAmelCase_ : Dict = int(inp... | 21 | import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtracto... | 558 | 0 |
'''simple docstring'''
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def _SCREAMING_SNAKE_CASE ( ):
print("""Making key files...""" )
make_key_files("""rsa""" , 1024 )
print("""Ke... | 572 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( snake_case_ ):
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_lowercase = 1
_lowercase = 1
while repunit:
_lowercase = (10 * repunit + 1) % divisor
repunit_index += 1
return repunit_index
def _SCREAMING_SN... | 572 | 1 |
'''simple docstring'''
from manim import *
class lowerCAmelCase ( UpperCamelCase_ ):
def _A ( self : Dict ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
lowerCAmelCase__ : List[Any] = Rectangle(he... | 378 |
'''simple docstring'''
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
snake_case = """."""
if __name__ == "__main__":
snake_case = os.path.join(REPO_PATH, """utils/documentation_tes... | 378 | 1 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_... | 460 |
'''simple docstring'''
def _snake_case ( A_ : list ):
"""simple docstring"""
if len(A_ ) <= 1:
return lst
a_ : Any = 1
while i < len(A_ ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a_ , a_ : int = ... | 460 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_availab... | 65 |
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__ ={
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu... | 521 | 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 applicab... | 86 |
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTest... | 86 | 1 |
from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
... | 27 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be c... | 27 | 1 |
from typing import Tuple, Union
from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig
if i... | 721 | from __future__ import annotations
def UpperCAmelCase ( lowercase , lowercase ):
"""simple docstring"""
if b == 0:
return (1, 0)
((__lowercase) , (__lowercase)) = extended_euclid(lowercase , a % b )
__lowercase = ... | 522 | 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_di... | 343 |
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Dict:
'''simple docstring'''
if index == r:
for j in range(_UpperCAmelCase ):
print(data[j], end=' ' )
... | 343 | 1 |
from timeit import timeit
def __UpperCamelCase ( lowerCAmelCase__ : int ):
if number < 0:
raise ValueError('''the value of input must not be negative''' )
__a : int = 0
while number:
number &= number - 1
result += 1
return result
def __UpperC... | 326 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __UpperCamelCase ( lowerCAmelCase__ : ... | 326 | 1 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> List[Any]:
_snake_case = int(lowerCAmelCase_ )
assert noofclusters < len(lowerCAmelCase_ )
# F... | 103 |
from collections.abc import Iterable
from typing import Any
class A :
def __init__( self : Dict , lowercase_ : int | None = None ) -> int:
"""simple docstring"""
_lowerCamelCase : List[Any] =value
_lowerCamelCase : ... | 464 | 0 |
'''simple docstring'''
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAva... | 609 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[Any] = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Lx... | 609 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase__ : Dict =logging.get_logger(__name__)
lowerCAmelCase__ : Any ={
'''facebook/convnextv2-tiny-1k... | 148 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase__ : List[str] =logging.get_logger(__name__)
lowerCAmelCase__ : List[Any] ={
'''vocab_file''': '''vocab... | 148 | 1 |
import pytest
__A : Any = """__dummy_dataset1__"""
__A : List[str] = """
import json
import os
import datasets
REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validatio... | 450 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
__A : Tuple = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import S... | 450 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class lowerCamelCase__ ( A__ ):
@staticmethod
@abstractmethod
def lowerCamelCase_ ( __a : ArgumentParser ):
'''simple docstring'''
r... | 306 |
def __lowerCAmelCase ( _UpperCamelCase = 2000000 ) -> int:
'''simple docstring'''
lowerCamelCase__: Tuple = [0 for i in range(n + 1 )]
lowerCamelCase__: Optional[Any] = 1
lowerCamelCase__: List[str] = 1
... | 306 | 1 |
from __future__ import annotations
from collections import Counter
from random import random
class _SCREAMING_SNAKE_CASE :
def __init__(self):
'''simple docstring'''
__UpperCAmelCase ={}
def A__ (self , UpperCAmelCase):
'''simple docstring'''
... | 718 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ = {
'configuration_albert': ['ALBERT_PRETRAINE... | 142 | 0 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
UpperCamelCase__ = logging.get_logger('''transformers.models.speecht5''')
def a__ ( lowerCAmelCase__... | 75 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
__lowerCamelCase = logg... | 317 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__n... | 226 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils impor... | 226 | 1 |
"""simple docstring"""
import logging
import math
from functools import partial
from typing import Any, Callable, Dict, Iterable, List, Optional, Sequence, Tuple, Union
import torch
from .tensor_utils import tensor_tree_map, tree_map
def lowerCAmelCase_ ( SCREAMING_SNA... | 179 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionCon... | 293 | 0 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def _lowerCamelCase ( ):
lowercase__ : Union[str, Any] = ArgumentParser(
description=(
"""PyTo... | 704 |
"""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_funnel import FunnelTokenizer
__snake_case = logging.get_logger(__name__)
__... | 128 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from tra... | 42 |
import pprint
import requests
lowerCamelCase__ = "https://zenquotes.io/api"
def __A() -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __A() -> list:
"""simple docstring"""
return requests.get(API_ENDPOINT_URL + """... | 612 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _snake_case ( __snake_case ):
_UpperCame... | 71 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {"configuration_unispeech": ["UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP", "UniSpeechConfig"]}
try:
if not is_to... | 71 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_UpperCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_UpperCAmelCase : list[int] = ... | 107 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> List[str]:
"""simple docstring"""
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(__lowerCAmelCase , int(b / 2 ) ) * actual_power(__lowerCAmelCase , int(b / 2 ) )
els... | 252 | 0 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.u... | 250 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
a_ :Optional[Any] ... | 250 | 1 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : str , lowercase_ : str ) -> float:
def get_matched_characters(lowercase_ : str , lowercase_ : str ) -> str:
_lowerCamelCase = []
_lowerCamelCase = min(len(_stra ) , len(_st... | 661 |
"""simple docstring"""
def lowerCAmelCase_( lowercase_ : int = 10 ) -> str:
if not isinstance(lowercase_ , lowercase_ ) or n < 0:
raise ValueError('''Invalid input''' )
_lowerCamelCase = 10**n
_lowerCamelCase = 2_84_33 * (pow(2 , 7_83_04_57 ,... | 661 | 1 |
"""simple docstring"""
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowerCamelCase ( _UpperCamelCase : List[str] ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase : Optional[i... | 299 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : str , UpperCamelCase : List[Any] ):
'''simple docstring'''
__UpperCAmelCase : Any ... | 299 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
... | 608 |
"""simple docstring"""
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
__lowerCamelCase = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kerne... | 608 | 1 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class UpperCamelCase ( unitte... | 110 |
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class UpperCamelCase ( unittest.TestCase ):
"""simple docstring"""
snake_case = JukeboxTokenizer
snake_case = {
"artist": "Zac Brown Band",
"genres": "Coun... | 110 | 1 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
_lowerCAmelCase : Tuple = (3, 9, -11, 0, 7, 5, 1, -1)
_lowerCAmelCase : Dict = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __magic_n... | 242 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __magic_name__ ( lowerCAmelCase_ ):
SCREAMING_SNAKE_CAS... | 242 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase__ ( lowercase__ ):
'''simple docstring'''
def __init__( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_... | 700 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
A_ = 1_00
A_ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
A_ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 123 | 0 |
'''simple docstring'''
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class UpperCAmelCase ( lowercase... | 404 |
'''simple docstring'''
class UpperCAmelCase :
"""simple docstring"""
def __init__( self : Tuple ) -> List[Any]:
_UpperCamelCase =''''''
_UpperCamelCase =''''''
_UpperCamelCase =[]
def UpperCamelCase__ ( self : ... | 404 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHea... | 708 |
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCamelCase__ = 1_00
lowerCamelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCamelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime not in primes:
... | 226 | 0 |
'''simple docstring'''
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers... | 440 |
"""simple docstring"""
from typing import Any
def lowercase ( lowerCAmelCase__ : list , lowerCAmelCase__ : list , lowerCAmelCase__ : dict , lowerCAmelCase__ : dict , lowerCAmelCase__ : dict , ) -> list:
_validation(
l... | 695 | 0 |
from collections import Counter
from timeit import timeit
def lowerCamelCase_ ( _lowercase = "" , ) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def lowerCamelCase_ ( _lowercas... | 704 | from __future__ import annotations
def lowerCamelCase_ ( _lowercase , _lowercase , _lowercase ) -> float:
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interes... | 387 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCAmelCase = {
'configur... | 466 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__lowerCAmelCase = {
'sample_size': 32,
'in_channels': 3,
'out_channels': 3,
... | 466 | 1 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __snake_case ( __A):
'''simple docstring'''
UpperCamelCase__ : Any = """ClapFeatureExtractor"""
UpperCamelCase__ : Any = ("""Ro... | 709 |
import os
from datetime import datetime as dt
from github import Github
UpperCAmelCase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""enhancement""",
"""new pipeline/model""",
"""new scheduler""",
"""wip""",
]
def ... | 351 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__A : Any = {
'''tiny.en''': '''h... | 231 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_... | 231 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( ):
'''simple docstring'''
for n in range(1 , 1_0_0_0_0_0_0 ):
yield n * (n + 1) // 2
def _lowerCAmelCase ( __lowerCamelCase:Dict ):
'''simple docstring'''
__magic_na... | 468 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import Att... | 468 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def _snake_case ( snake_case__ : BertModel , snake_case__ : str , snake_case__ : str ):
A = ('dense.weight', 'attention.self.query', 'atte... | 91 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .te... | 294 | 0 |
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 TFCamembertModel
... | 467 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class a ( UpperCAmelCase ):
_lower... | 467 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
UpperCAmelCase = list[tuple[int, int]]
UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0... | 420 | """simple docstring"""
def lowercase ( a__ : float , a__ : int ) -> float:
if digit_amount > 0:
return round(number - int(a__ ) , a__ )
return number - int(a__ )
if __name__ == "__main__":
print(decimal_isolate(1.53, 0))
print(decimal_isolate(35.3_45... | 420 | 1 |
def lowerCamelCase__ ( ) -> List[Any]:
"""simple docstring"""
a__ :Any = []
a__ :Union[str, Any] = 1
while len(a ) < 1e6:
constant.append(str(a ) )
i += 1
a__ :str = "".join(a )
return (
int(constan... | 702 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SE... | 373 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import float... | 555 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a : int = 5_0_0_0_0_0
a , a : Union[str, Any] = os.path.split(__file__)
a : Dict = os.path.joi... | 555 | 1 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ... | 720 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transforme... | 45 | 0 |
'''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_seed
f... | 325 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
... | 325 | 1 |
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()
@slow
@require_torch_... | 17 |
import os
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
__SCREAMING_SNAKE_CASE = """."""
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE = os.path.join(REPO_PATH, """utils... | 17 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class lowerCAmelCase_ :
UpperCAmelCase = 42
UpperCAmelCase = None
UpperCAmelCase = None
def _snake_case ( __snake_case ):
# Validation
def is_valid_tree(_... | 10 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceC... | 453 | 0 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
__lowerCamelCase = logging.get_logger(__name__)
class UpperCamelCase_ :
lowercase = None
@experimental
def UpperCamelCase__ ( Uppe... | 307 |
from maths.prime_factors import prime_factors
def UpperCamelCase__ ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ):
_a : Optional[Any] = F'Input value of [number={number}... | 307 | 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_docst... | 418 |
'''simple docstring'''
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE_ ( __A : np.ndarray , __A : tuple[int, int] , __A : tuple[int, int] , __A : bool , ) -> tuple[float | int, list[tuple[int, int]]]:
_SCREA... | 418 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def A_ ( _lowerCAmelCase , _lowerCAmelCase=1000 ) -> Optional[int]:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
UpperCamelCase : Union[str, Any] = n - 1
UpperCamelCase : T... | 38 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A__ ( __snake_ca... | 38 | 1 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ = TypeVar('''T''')
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (position - 1) // 2
def UpperCAmelCase__ ( low... | 47 |
'''simple docstring'''
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase : Any =logging.get_logger(__name__)
... | 440 | 0 |
def __UpperCAmelCase ( a_):
snake_case_ = [0] * len(a_)
snake_case_ = []
snake_case_ = []
snake_case_ = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in r... | 711 |
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
lowercase = transform... | 607 | 0 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
a : Optional[int] = version.parse(version.parse(torch.__version__).base_version) < ver... | 218 |
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowerCAmelCase_ = logging.getLogger(__name__)
if __name__ == "__main__":
l... | 60 | 0 |
'''simple docstring'''
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
a = len(UpperCAmelCase__ )
a = sum(UpperCAmelCase__ )
a = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(... | 708 |
import unittest
from transformers import MobileBertConfig, 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 ConfigTester
from ..... | 32 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoCon... | 300 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int ) -> list[list[int]]:
_UpperCAmelCase : list[list[int]] = []
create_all_state(1 , lowerCAmelCase , lowerCAmelCase , [] , lowerCAmelCase )
return result
... | 300 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_U... | 704 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_featur... | 474 | 0 |
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switc... | 87 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Any = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFI... | 87 | 1 |
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 lowerCamelCase ( __UpperCAmelCase ):
... | 711 |
import sys
_A = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"668966489504452445231617318564030987111217223... | 294 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
... | 267 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
im... | 267 | 1 |
'''simple docstring'''
UpperCAmelCase : Dict = {
'meter': 'm',
'kilometer': 'km',
'megametre': 'Mm',
'gigametre': 'Gm',
'terametre': 'Tm',
'petametre': 'Pm',
'exametre': 'Em',
'zettametre': 'Zm',
'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCAmelCase ... | 47 |
'''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
Up... | 47 | 1 |
"""simple docstring"""
__UpperCamelCase : str = 2_5_6
# Modulus to hash a string
__UpperCamelCase : Union[str, Any] = 1_0_0_0_0_0_3
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ : Dict = len(A_ )
lowerCAmelCase__ : Dict = ... | 450 |
"""simple docstring"""
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_mo... | 450 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
snake_case__ : Optional[Any] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst... | 707 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
snake_case__ : List[str] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( ... | 618 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH... | 531 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device... | 531 | 1 |
from __future__ import annotations
def snake_case_ ( __lowercase ):
return [ord(__lowercase ) - 9_6 for elem in plain]
def snake_case_ ( __lowercase ):
return "".join(chr(elem + 9_6 ) for elem in encoded )
def snake_case_ ( ):
UpperCAmelCa... | 707 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCAmelCase__:
'''simple docstring'''
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Tensor # [batch_size x 3]
A_ : torch.Ten... | 641 | 0 |
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