code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import... | 694 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Optional[int] = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/re... | 694 | 1 |
from __future__ import annotations
def lowercase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if resistance < 0:
... | 719 |
def lowercase_ ( _UpperCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = []
__lowercase = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
__lower... | 527 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logging.get_l... | 217 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
lowerCAmelCase_ = TypeVar('T')
class _A ( Generic[T] ):
def __init__( self : Optional[Any] , _A : T ) -> Union[str, Any]:
... | 217 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class SCREAMING_SNAKE_CASE__ ( __A ):
def __init__( ... | 708 |
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, loggi... | 68 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase ):
print(F"Vertex\tShortest Distance from vertex {src}" )
for i, d in enumerate(lowerCamelCase ):
print(F"{i}\t\t{d}" )
def lowerCAmelCase_ ( lowerCamelCase , ... | 21 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
... | 21 | 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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"hustvl/yolos-sm... | 362 |
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, BlipaProcessor, BlipImageProcess... | 362 | 1 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__n... | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/faceb... | 634 | 0 |
'''simple docstring'''
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
_lowerCAmelCase = TypeVar('''KEY''')
_lowerCAmelCase = TypeVar('''VAL''')
@dataclass(frozen=SCREAMING_SNAKE_CASE__ ... | 399 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 399 | 1 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
SCREAMING_SNAKE_CASE = True
except (ImportError, ModuleNotFoundError):
SCREAMING_SNAKE_CASE = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('p... | 94 | def snake_case (__lowercase ) -> int:
'''simple docstring'''
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]
_snake... | 670 | 0 |
from collections.abc import Callable
def a ( SCREAMING_SNAKE_CASE_ : Callable[[float], float] , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float ):
"""simple docstring"""
UpperCamelCase : Optional[int] ... | 721 |
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def a ( SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : np.ndarray , SCREAMING_SNAKE_CASE_ : int ... | 643 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A_ = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "Lla... | 270 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp i... | 270 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __A ( metaclass=a ):
"""simple docstring"""
A_ = ['flax']
def __init__( self , *_lowerCamelCase , **_lowerCamelCase )-> List[str]:
... | 702 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
... | 318 | 0 |
'''simple docstring'''
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def a__ ( _SCREAMING_SNAKE_CASE : Any , _SCREAMING_SNAKE_CASE : D... | 71 |
'''simple docstring'''
from statistics import mean, stdev
def a__ ( _SCREAMING_SNAKE_CASE : list , _SCREAMING_SNAKE_CASE : int = 3 ) -> list:
"""simple docstring"""
UpperCAmelCase_ : Dict = min(_SCREAMING_SNAKE_CASE )
UpperCAmelCase... | 71 | 1 |
'''simple docstring'''
def _A ( A ) -> Optional[Any]:
lowercase : Union[str, Any] = abs(UpperCamelCase__ )
lowercase : int = 0
while n > 0:
res += n % 1_0
n //= 1_0
return res
def _A ( A ) -> Tuple:
lowercase : int ... | 720 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 425 | 0 |
'''simple docstring'''
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class __SCREAMING_SNAKE_CASE ( yaml.SafeLoader ):
'''simple docstring'''
def _lowerCamelCase ( self : Optional[int] ,UpperCamelCase ... | 125 |
'''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_... | 44 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ = None ) -> list[list[str]]:
"""simple docstring"""
__UpperCAmelCase : str = word_bank or []
# create a table
... | 10 | '''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : Any = logging.get_logger(__name__)
_a : int = {
"facebook/s2t-wav2vec2-large-en-de": (
"https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main... | 10 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a ( unitte... | 4 |
'''simple docstring'''
from math import factorial
def SCREAMING_SNAKE_CASE__ ( __A , __A , __A ) -> float:
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the f... | 495 | 0 |
def lowerCamelCase__ ( snake_case_ : int ) -> bool:
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
__snake_case = str(snake_case_ )
__snake_case = ''''''.join(sorted(sna... | 388 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switchi... | 388 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import Backbo... | 3 |
# 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... | 30 | 0 |
"""simple docstring"""
def lowercase (_snake_case = 1000 ) -> int:
'''simple docstring'''
__UpperCamelCase = 2**power
__UpperCamelCase = str(_snake_case )
__UpperCamelCase = list(_snake_case )
__UpperCamelCase = 0
for i in list_num:
... | 228 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_A = "."
# Internal TensorFlow ops tha... | 228 | 1 |
'''simple docstring'''
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.uti... | 11 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/... | 472 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a_ :int = logging.get_logger(__name__)
a_ :List[Any] = {
"""ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class lower... | 720 |
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
a_ :str = version.parse(version.parse(torch.__version__).... | 250 | 0 |
'''simple docstring'''
from __future__ import annotations
def A_ ( _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[int] , _lowerCAmelCase : list[list[str]] , _lowerCAmelCase : int , ):
... | 44 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
__UpperCAmelCase = loggi... | 642 | 0 |
import random
def UpperCAmelCase__ ( lowercase__ , lowercase__ ) -> tuple:
__lowercase , __lowercase , __lowercase = [], [], []
for element in data:
if element < pivot:
less.append(lowercase__ )
elif el... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
UpperCamelCase__ = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
}
try:
if... | 634 | 0 |
'''simple docstring'''
from collections import deque
from .hash_table import HashTable
class SCREAMING_SNAKE_CASE ( __A ):
"""simple docstring"""
def __init__( self : Tuple , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : Dict ... | 430 |
'''simple docstring'''
def lowercase_ ( __A : int ) -> int:
"""simple docstring"""
if n == 1 or not isinstance(__A , __A ):
return 0
elif n == 2:
return 1
else:
lowercase : Tuple =[0, 1]
for i in range(2 , n + 1 ):
seq... | 94 | 0 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 79 |
'''simple docstring'''
def __A ( lowerCamelCase_ ):
"""simple docstring"""
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def __A ( lowerCamelCase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : ... | 79 | 1 |
"""simple docstring"""
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedC... | 581 |
"""simple docstring"""
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
... | 58 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def _snake_case ( A , A , A , A , A = None , A = None , A = None... | 700 |
'''simple docstring'''
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transforme... | 98 | 0 |
'''simple docstring'''
def a_ ( _UpperCAmelCase : str ) -> List[Any]:
__snake_case : Dict = [0] * len(SCREAMING_SNAKE_CASE__ )
for i in range(1 ,len(SCREAMING_SNAKE_CASE__ ) ):
# use last results for better performance - dynamic programming... | 286 |
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __lowercase :
"""simple docstring"""
_A : float
_A : TreeNode | None = None
_A : TreeNode | None = None
def SCREA... | 480 | 0 |
"""simple docstring"""
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def A_ (__a , __a , __a , __a , ):
'''simple docstring'''
A_ = coefficient_matrix.shape
A_ = constant_matri... | 711 |
"""simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCamelCase_ : Optional[Any] = '''src/tran... | 482 | 0 |
"""simple docstring"""
from copy import deepcopy
class __a :
def __init__( self : str , UpperCAmelCase_ : List[str] = None , UpperCAmelCase_ : Optional[Any] = None )-> Any:
"""simple docstring"""
if arr ... | 554 |
"""simple docstring"""
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.dat... | 180 | 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... | 585 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def _a ( __SCREAMING_SNAKE_CASE : Optional[Any] ... | 585 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case ( _a: Dict , _a: Any , _a: Union[str, Any] , _... | 510 |
"""simple docstring"""
def snake_case ( _a: list )-> bool:
'''simple docstring'''
if not isinstance(_a , _a ):
raise ValueError('Input series is not valid, valid series - [2, 4, 6]' )
if len(_a ) == 0:
raise ValueError('Input list must... | 510 | 1 |
"""simple docstring"""
class snake_case :
def __init__( self : int , a__ : int ) -> Optional[int]:
'''simple docstring'''
_A = n
_A = [None] * self.n
_A = 0 # index of... | 621 |
"""simple docstring"""
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
# Return True if there is node that has not iterated.
_A = [False] * len(__lowercase )
_A = []
queue.append(__lowercase )
_A = True... | 621 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : Union[str, Any] = {
... | 476 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__lowercase : List[str] = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE... | 476 | 1 |
import datasets
from .evaluate import evaluate
lowerCAmelCase__ = """\
@inproceedings{Rajpurkar2016SQuAD10,
title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
booktitle={EMNLP},
year={2016}
}
"""
lowerCA... | 716 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITION... | 626 | 0 |
"""simple docstring"""
import subprocess
import sys
from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch
class UpperCamelCase_ ( a_ ):
@require_torch
def UpperCamelCase_ ( self ) ->... | 673 |
"""simple docstring"""
import math
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return math.sqrt(lowerCAmelCase ) * math.sqrt(lowerCAmelCase ) == num
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
... | 673 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
class a__ ( __A ):
"""simple docstring"""
__UpperCamelCase : Optional[int] = 'bert-generation'
def __init__(self , __lowercase=5_03_58 , __lowercase=10_24... | 474 |
'''simple docstring'''
from manim import *
class a__ ( __A ):
"""simple docstring"""
def _snake_case (self ):
__lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
__lowerCAmelCase = Rectangle... | 474 | 1 |
"""simple docstring"""
import os
def __magic_name__ ( __snake_case : str = "matrix.txt" ) -> int:
with open(os.path.join(os.path.dirname(__snake_case ) , __snake_case ) ) as in_file:
lowercase : Optional[Any] = in_f... | 361 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://... | 654 | 0 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case__ ( unittest... | 710 | """simple docstring"""
import heapq
import sys
import numpy as np
SCREAMING_SNAKE_CASE__:Optional[int] = tuple[int, int]
class snake_case__ :
def __init__( self ):
__a = []
__a = set()
def a__ ( self ):
if not self.empty():
retu... | 67 | 0 |
'''simple docstring'''
import operator as op
def _a (__SCREAMING_SNAKE_CASE ):
"""simple docstring"""
_UpperCamelCase =[]
_UpperCamelCase =lambda __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int(x / y ) # noqa: E731 integer division operation
_UpperCamelCa... | 404 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
lowerCAmelCase_ = (UnCLIPScheduler,)
def UpperCamelCase__ ( self : ... | 404 | 1 |
UpperCamelCase = {
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
UpperCamelCase = {
"m": 0,
"km": 3,
"Mm... | 677 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase = {
"configuration_clip": [
"CLIP_PRETRAINED_CO... | 677 | 1 |
from math import isqrt
def UpperCAmelCase ( a_ ) -> list[int]:
"""simple docstring"""
__A = [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_ ):
... | 55 |
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
A__ = logging.get_logger(__name__)
A__ = {'''v... | 252 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_fl... | 719 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils im... | 195 | 0 |
def _SCREAMING_SNAKE_CASE ( __lowercase : Any , __lowercase : List[str] , __lowercase : Optional[Any] , __lowercase : int ) -> int:
"""simple docstring"""
__A = len(snake_case__ ), len(grid[0] )
if (
m... | 637 | import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 382 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_bac... | 701 |
def UpperCamelCase_ ( __a ) -> int:
if not isinstance(__a , __a ):
raise TypeError("only integers accepted as input" )
else:
a__ : Union[str, Any] = str(abs(__a ) )
a__ : Dict = [list(__a ) for char in rang... | 151 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase ) -> Dict:
if not is_accelerate_available():
return method
snake_case__ = version.parse(... | 33 | from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Optional[int] = {
"""configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
"""feature_extraction_mctct""": ["""MC... | 197 | 0 |
from __future__ import annotations
def _lowerCamelCase ( a_ : list[int]): # This function is recursive
lowerCamelCase :Union[str, Any] = len(a_)
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
... | 49 | import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import wr... | 49 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_en... | 1 | import numpy as np
def __lowerCAmelCase ( _A ,_A ,_A = 1E-12 ,_A = 100 ,):
"""simple docstring"""
assert np.shape(_A )[0] == np.shape(_A )[1]
# Ensure proper dimensionality.
assert np.shape(_A )[0] == np.shape(_A )[0]
# Ensure inputs ... | 398 | 0 |
from datetime import datetime
import requests
def snake_case_ ( lowerCAmelCase_ : str ):
__lowercase : Any = """https://downloadgram.net/wp-json/wppress/video-downloader/video?url="""
__lowercase : Dict = requests.get(base_url + url ... | 709 |
from __future__ import annotations
def snake_case_ ( lowerCAmelCase_ : str , lowerCAmelCase_ : list[str] | None = None ):
__lowercase : Tuple = word_bank or []
# create a table
__lowercase : int = len(lower... | 649 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def a__ ( _SCREAMING_SNAKE_CASE : Optional[int] ... | 71 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class _snake_case (metaclass=__SCREAMING_SNAKE_CASE):
__A : Any =["speech"]
def __init__( self ,*_snake_case ,**_snake_case ):
requires_backends(self ,["speech"] )
class _s... | 71 | 1 |
def _UpperCamelCase (a__ :Optional[int] , a__ :int ):
"""simple docstring"""
return "\n".join(
f"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(numb... | 706 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument("--dump_path", default=None, type=str, ... | 548 | 0 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class __snake_case :
'''simple docstring'''
def __init__( self , __SCREAMING_SNAKE_CASE ):
snake_case__ : List[Any] = str(id_ )
snake_case__ : Dict... | 38 |
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class _snake_case ( lowercase__):
UpperCamelC... | 413 | 0 |
import string
from math import logaa
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
snake_case_ = document.translate(
str.maketrans('' , '' , string.punctuation ) ).replace('\n' , '' )
snake_case_ = document_without_punctuation.split(' ' ) # word tokenization
retur... | 46 |
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_t... | 46 | 1 |
import math
def lowerCamelCase_(lowerCamelCase_ , lowerCamelCase_ ) -> float:
if (
not isinstance(lowerCamelCase_ , (int, float) )
or power_factor < -1
or power_factor > 1
):
raise ValueError("power_factor must be a valid float value be... | 323 |
# 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 ... | 323 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
impo... | 670 |
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[str] , snake_case_ : int , snake_case_ : Optional[Any]=None , snake_case_ : List[str]=None ):
UpperCamelCase_: List[Any] = data
UpperCamelCase_: ... | 670 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE : int = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenizer"],
}... | 635 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
... | 635 | 1 |
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
lowerCamelCase = logging.get_logger(__name__)
lowerCamelC... | 207 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderO... | 207 | 1 |
from __future__ import annotations
from PIL import Image
# Define glider example
UpperCamelCase = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0... | 45 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
_A : Optional[int] = object()
# For specifying empty leaf dict `{}`
_A : Tuple = object()
def __snake_case... | 100 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {
"configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP",... | 489 |
'''simple docstring'''
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import f... | 489 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 9 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( UpperCamelCase__ ):
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name_... | 407 | 0 |
def _lowercase ( ) -> Tuple:
lowerCamelCase =0
for i in range(1 , 10_01 ):
total += i**i
return str(_UpperCAmelCase )[-10:]
if __name__ == "__main__":
print(solution())
| 269 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class __A ( a ):
@staticmethod
@abstractmethod
def _snake_case ( UpperCAmelCase_ ):
raise NotImplementedError()
@abstractmethod
def _snake_case ( self ):
rai... | 269 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transform... | 88 |
import os
from datetime import datetime as dt
from github import Github
lowercase__ =[
'good first issue',
'feature request',
'wip',
]
def __UpperCamelCase ( ):
__a : int = Github(os.environ['''GITHUB_TOKEN'''] )
__a : Dict = g.get_rep... | 521 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def lowerCamelCase_ ( UpperCAmelCase_ : Any ) -> List[Any]:
'''simple docstring'''
if not is_accelerate_available():
return metho... | 706 |
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 require_tensorflow_text, require... | 648 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__UpperCAmelCase : Tuple = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if numb... | 63 |
from math import loga
def lowerCamelCase__ ( __lowerCAmelCase : int ):
"""simple docstring"""
if a < 0:
raise ValueError("Input value must be a positive integer" )
elif isinstance(__lowerCAmelCase , __lowerCAmelCase ):
raise TypeError("Inp... | 290 | 0 |
"""simple docstring"""
class __magic_name__ :
def __init__( self ):
"""simple docstring"""
_lowerCAmelCase = """"""
_lowerCAmelCase = """"""
_lowerCAmelCase = []
def _lowerCamelCase ( self , __ma... | 720 |
"""simple docstring"""
import qiskit
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_lowerCAmelCase = qiskit.QuantumCirc... | 309 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase : Dict ... | 372 |
def lowerCamelCase_ ( ):
_a : Optional[int] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
_a : Dict = 6
_a : Any = 1
_a : Union[str, Any] = 1901
_a : Tuple = 0
while year < 2001:
day += 7
... | 471 | 0 |
'''simple docstring'''
import fire
from utils import calculate_rouge, save_json
def __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , **_lowerCAmelCase ):
"""simple docstring"""
UpperCAmelCase = [x.strip() for x in open(_lowerCamelCase... | 717 |
from collections import Counter
from timeit import timeit
def __UpperCamelCase ( _lowerCAmelCase = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __UpperCamelCase ( _lowerCAmelCase = "" ):
... | 405 | 0 |
import os
import textwrap
import pyarrow as pa
import pytest
from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv
from ..utils import require_pil
@pytest.fixture
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] ) -> str:
_UpperCAmelCas... | 300 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: Any , lowerCAmelCase: Optional[Any] , lowerCAmelCase: Any ) -> int:
_UpperCAmelCase : Optional[int] ... | 300 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_a : Tuple= {"configuration_swin": ["SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "SwinConfig", "SwinOnnxConfig"]}
try:
if no... | 702 | """simple docstring"""
import re
import tempfile
from pathlib import Path
import pytest
import yaml
from datasets.utils.readme import ReadMe
# @pytest.fixture
# def example_yaml_structure():
_a : List[str]= yaml.safe_load(
"\\nname: \"\"\nallow_empty: false\nallow_empty_text: ... | 192 | 0 |
import functools
def SCREAMING_SNAKE_CASE__ ( UpperCamelCase__: list[int] , UpperCamelCase__: list[int] ):
# Validation
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not all(isinstance(UpperCamelCase__ , UpperCamelCase__ ) for day in days ... | 6 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def __UpperCamelCase ( snake_case__ , snake_case__ ):
A_ : Optional[int] = list(snake_case__ )
A_ : List[Any] = list(snake_case__ ... | 180 | 0 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
... | 317 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale... | 317 | 1 |
from math import pow
def a__ ( A__, A__, A__, A__, A__, ):
if current_sum == needed_sum:
# If the sum of the powers is equal to needed_sum, then we have a solution.
solutions_count += 1
return current_sum, solutions_count
SCREAMING_SN... | 101 |
def lowerCamelCase_ ( __UpperCamelCase , __UpperCamelCase ):
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
A_ = str(bin(__UpperCamelCase ) )[2:] # remove the leading "0b"
A_ = str(bin(__UpperCamelCase ) )[2:] ... | 141 | 0 |
from collections import Counter
from timeit import timeit
def A_ ( snake_case : str = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def A_ ( snake_case : str ... | 451 |
from collections import Counter
from timeit import timeit
def A_ ( snake_case : str = "" , ) -> bool:
'''simple docstring'''
return sum(c % 2 for c in Counter(input_str.replace(''' ''' , '''''' ).lower() ).values() ) < 2
def A_ ( snake_case : str ... | 451 | 1 |
"""simple docstring"""
import os
def _lowerCamelCase ( __a = "matrix.txt" ):
with open(os.path.join(os.path.dirname(__a ), __a ) ) as in_file:
SCREAMING_SNAKE_CASE_ = in_file.read()
SCREAMING_SNAKE_CASE_ = [[int(__a ) for cell in row.split(''',''' )... | 626 |
"""simple docstring"""
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case ( __lowercase , ... | 626 | 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 (
... | 583 |
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"huggingface/time-series-transformer-tourism-monthly": (
"https://huggingface.co/huggingface/time-s... | 583 | 1 |
def A ( _SCREAMING_SNAKE_CASE ) -> bool:
if not isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ):
raise ValueError("check_bouncy() accepts only integer arguments" )
lowerCamelCase : Optional[int] = str(_SCREAMING_SNAKE_CASE ... | 311 |
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
SCREAMING_SNAKE_CASE__ : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 311 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
'microsoft/markuplm-base': 'https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json',
'microsoft/ma... | 706 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGen... | 534 | 0 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
'''microsoft/unispeech-sat-base... | 4 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase_ = {
'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}
try:
if not is_torch_availabl... | 173 | 0 |
"""simple docstring"""
import math
def a__ ( __lowercase , __lowercase ) -> int:
_A = len(__lowercase )
_A = int(math.floor(math.sqrt(__lowercase ) ) )
_A = 0
while arr[min(__lowercase , __lowercase ) - 1] < x:
... | 621 |
"""simple docstring"""
import argparse
import torch
from transformers import GPTaLMHeadModel, RobertaForMaskedLM
if __name__ == "__main__":
a_ = argparse.ArgumentParser(
description=(
"Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for... | 621 | 1 |
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerIm... | 562 |
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
lowercase_ ... | 562 | 1 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformer... | 129 |
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 __lowerCamelCase ( _lowerCAmelCase ... | 129 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, Di... | 226 |
def __lowerCAmelCase ( __magic_name__ = 5_0 ):
_lowercase: Union[str, Any] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for block_start in range(row_length - block_length ):
ways_number[r... | 226 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
snake_case_ : Tuple ={
'''facebook/maskformer-swin-b... | 205 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
snake_case_ : Optional[Any] =Lock()
def UpperCAmelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase_... | 205 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tensorflow_text_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase : Union[str, Any] = {
'''configura... | 686 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def a_ ( __lowercase : Any ) -> List[An... | 686 | 1 |
from typing import Any
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self : Union[str, Any] , lowerCamelCase : Any ) -> str:
"""simple docstring"""
_UpperCAmelCase = data
_UpperCAmelCase = None
class SCREAMI... | 402 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a: Any = logging.get_logger(__name__)
__a: int = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ ( UpperC... | 402 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pi... | 654 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import C... | 654 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
snake_case = {
'''configuration_perceiver''': ['''PER... | 700 |
"""simple docstring"""
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ ) -> bool:
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 404 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _snake_case ( UpperCAmelCase_ ):
__lowerCAmelCase : Union[str, Any] = ['image_processor', 'tokenizer']
__lowerCAmelCase : Union[str, Any] = 'CLIPImagePr... | 12 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def lowercase_ ( __snake_case : Any ) -> List[Any]:
'''simple docstring'''
if "model" in orig_key:
snake_case__ :Any = orig_key.replace("model."... | 241 | 0 |
"""simple docstring"""
__magic_name__ = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",... | 258 |
"""simple docstring"""
from itertools import count
def _A ( __lowercase = 50 ):
"""simple docstring"""
lowerCamelCase__ = [1] * min_block_length
for n in count(__lowercase ):
fill_count_functions.append(1 )
for block_l... | 258 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_SCREAMING_SNAKE_CASE = {
"""iou_prediction_h... | 537 |
"""simple docstring"""
__lowercase : Union[str, Any] = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Z... | 142 | 0 |
'''simple docstring'''
from collections.abc import Generator
from math import sin
def __snake_case ( UpperCAmelCase_ : bytes ):
if len(a_ ) != 32:
raise ValueError("Input must be of length 32" )
lowerCamelCase_ = B''''''
for i in [3, 2, 1, 0]:
little_end... | 718 |
'''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 Con... | 445 | 0 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowerCAmelCase__ = [
# (stable-diffusion, HF Diffusers)
('''time_embed.0.weight''', '''time_em... | 41 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from trans... | 41 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__magic_name__ = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_to... | 718 |
import unittest
from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTest... | 530 | 0 |
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
AutoencoderKL,
DDIMScheduler,
... | 254 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = ... | 254 | 1 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
... | 643 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : List[An... | 643 | 1 |
import math
def _UpperCAmelCase (UpperCamelCase_ : float , UpperCamelCase_ : float ):
'''simple docstring'''
if initial_intensity < 0:
raise ValueError("""The value of intensity cannot be negative""" )
# handling of negative values of initial intensity
... | 429 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __snake_case :
lowerCAmelCase__ = 42
lowerCAmelCase__ = None
... | 429 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( _a , _a ):
"""simple docstring"""
_lowerCamelCase = get_failure_array(_a )
# 2) Step through text searching for pattern
_lowerCamelCase , _lowerCamelCase = 0, 0 # i... | 712 |
import heapq
def _lowerCamelCase ( _a ):
"""simple docstring"""
_lowerCamelCase = []
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, s... | 297 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...uti... | 619 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
d... | 619 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a__ : str ={
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}... | 702 |
'''simple docstring'''
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as ... | 434 | 0 |
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