code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import json
import os
import shutil
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 AutoConfig, BertConfig, GPTaConfig
from tr... | 91 |
from __future__ import annotations
def a__ ( snake_case__ : list[int] ):
if len(snake_case__ ) == 0:
return array
_UpperCAmelCase,_UpperCAmelCase : List[str] = min(snake_case__ ), max(snake_case__ )
# Compute the variables
_UpperCAmelCase : T... | 643 | 0 |
"""simple docstring"""
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowercase : int = datasets.load_iris()
__lowercase : Union[str, Any] = np.array(data["data"])
__lower... | 93 | """simple docstring"""
def SCREAMING_SNAKE_CASE ( snake_case):
return 1 if digit in (0, 1) else (digit * factorial(digit - 1))
def SCREAMING_SNAKE_CASE ( snake_case):
__snake_case = 0
__snake_case = number
while duplicate > 0:
... | 93 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowercase__ : Tuple = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
lowerca... | 376 |
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase ( ... | 376 | 1 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}
class snake_c... | 510 |
def _UpperCAmelCase ( A , A ):
'''simple docstring'''
_validate_point(A )
_validate_point(A )
if len(A ) != len(A ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a... | 510 | 1 |
import os
def a_ ( ):
__lowerCAmelCase = os.path.dirname(os.path.realpath(lowerCAmelCase_ ) )
__lowerCAmelCase = os.path.join(lowerCAmelCase_, 'triangle.txt' )
with open(lowerCAmelCase_ ) as f:
__lowerCAmelCase = f.r... | 53 |
'''simple docstring'''
import copy
import random
from transformers import CLIPTokenizer
class SCREAMING_SNAKE_CASE ( lowercase_ ):
'''simple docstring'''
def __init__( self : int , *snake_case : Optional[Any] , **snake_case : Optional[int]... | 517 | 0 |
'''simple docstring'''
import operator as op
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
"""simple docstring"""
lowercase_ : Optional[Any] = []
lowercase_ : List[Any] = lambda _UpperCamelCase , _UpperCamelCase : int(x / y ) ... | 701 |
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( _UpperCamelCase = 200 ):
"""simple docstring"""
lowercase_ : Optional[int] = [1, 2, 5, 10, 20, 50, 100, 200]
lowercase_ : str = [0] * (pence + 1)
lowercase_ : Dict = 1 # base cas... | 640 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase__ : Any = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
... | 98 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case__ ( self : str ) ... | 98 | 1 |
'''simple docstring'''
from math import factorial
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = real
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ... | 542 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler... | 542 | 1 |
'''simple docstring'''
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 lowerCAmelCase__ :
"""simple docstring"""
lowerCAmelCase__ ... | 627 |
"""simple docstring"""
from math import pi, sqrt, tan
def __UpperCAmelCase ( __UpperCamelCase ):
if side_length < 0:
raise ValueError('''surface_area_cube() only accepts non-negative values''' )
return 6 * side_length**2
def __UpperCAmelCase ... | 76 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_: Tuple = logging.get_logger(__name__)
lowerCAmelCase_: str = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class ... | 720 | """simple docstring"""
from collections import deque
class a__ :
def __init__( self, _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ):
'''simple docstring'''
lowercase__ = process_name # process name
lowercase__ = arrival_time # arriva... | 668 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 200 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from... | 200 | 1 |
"""simple docstring"""
from pathlib import Path
import fire
def UpperCAmelCase ( A__: Optional[Any] , A__: Dict , A__: Tuple ) -> List[str]:
__lowerCamelCase : str = Path(lowercase_ )
__lowerCamelCase : int = Path(lowercase_... | 707 |
"""simple docstring"""
import multiprocessing
import os
from typing import BinaryIO, Optional, Union
import fsspec
from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedD... | 263 | 0 |
import numpy
class _UpperCamelCase :
def __init__( self , __UpperCamelCase , __UpperCamelCase )-> None:
__lowerCAmelCase = input_array
# Random initial weights are assigned where first argument is the
# numbe... | 367 |
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self : Tuple , __a : int ) ->Optional[int]:
lowerCamelCase_ : Optional[Any] = n
lowerCamelCase_ : Dict = [None] * self.n
lowerCamelCase_ : in... | 278 | 0 |
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 lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
snak... | 139 |
from __future__ import annotations
def lowerCamelCase__ ( _A ):
'''simple docstring'''
snake_case_ = len(_A )
# We need to create solution object to save path.
snake_case_ = [[0 for _ in range(_A )] for _ in range(_A )]
snake_cas... | 139 | 1 |
"""simple docstring"""
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
A__ : Tuple = (
'''This metric will be remove... | 153 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( lowercase_ : int , lowercase_ : Union[str, Any] , lowercase_ : str , lowercase_ : Optional[Any] ): # noqa: E741
while r - l > 1:
lowercase = (l + r) // 2
... | 588 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowerCAmelCase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))
import check_copies # noqa: E402
# Thi... | 635 | """simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class _snake_case ( __snake_case ):
"""simple docstring"""
a = "M-CLIP"
def __init__( self : Optional[Any] , _A : List[s... | 635 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ : int = logging.get_logger(__name__)
lowerCamelCase__ : Dict = {
"facebook/dpr-ctx_encoder-single-nq-base": (
"https://huggingface.co/faceboo... | 698 |
"""simple docstring"""
# Copyright 2021 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
#
... | 698 | 1 |
def UpperCamelCase_( lowerCamelCase_ ) -> list:
_lowercase : Any = len(lowerCamelCase_ )
for i in range(1 , lowerCamelCase_ ):
_lowercase : Tuple = collection[i]
_lowercase : str = 0
_lowercase : Optional[int] = i - 1... | 354 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase( _a ):
lowercase_ : List[Any] = ... | 354 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ... | 30 | """simple docstring"""
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __A ( SCREAMING_SNAKE_CASE_ ):
_Upper... | 213 | 0 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
__lowerCamelCase : Optional[Any] = "\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Lia... | 713 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCamelCase : Optional[Any] = {
"facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolv... | 459 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
imp... | 670 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
cla... | 224 | 0 |
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
UpperCamelCase__ = "scheduler_config.json"
class __SCREAMING_SNAKE_CASE ( _a ):
snake_case : ... | 548 |
def _UpperCamelCase (a__ :int ):
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
UpperCamelCase__ = 1
UpperCamelCase__ = 1
while repunit:
UpperCamelCase__ = (10 * repuni... | 548 | 1 |
"""simple docstring"""
def snake_case_ ( A_ : dict ):
'''simple docstring'''
_lowerCamelCase : int = set()
# edges = list of graph's edges
_lowerCamelCase : Optional[int] = get_edges(A_ )
# While there are st... | 83 |
'''simple docstring'''
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
SCREAMING_SNAKE_CASE__ = {1: (1, 1), 2: (2,... | 267 | 0 |
"""simple docstring"""
import numpy as np
import datasets
__a : Any = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean dist... | 702 |
"""simple docstring"""
class _SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self: Any ):
'''simple docstring'''
a__ = {}
def lowercase ( self: Optional[int] ):
'''simple docstr... | 200 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase__ : Optional[Any] = {
'''configuration_whisper''': ['''WHISPER_PRETRAINE... | 105 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def _UpperCAmelCase ( UpperCamelCase: int ):
"""simple docstring"""
if not isinstance(UpperCamelCase , UpperCamelCase ):
raise TypeError("Undefined for non-integers" )
elif precision < 1:
raise ValueError("Und... | 611 | 0 |
"""simple docstring"""
from typing import Callable, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ = logging.get_logger(__name__)
a_ = {
'''microsoft/xprophetnet-large-wiki100-cased''': (
'''https://huggingface.co/microsoft/xpro... | 48 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : list ):
"""simple docstring"""
snake_case_ : Optional[int] = len(SCREAMING_SNAKE_CASE__ )
for i in range(1 , SCREAMING_SNAKE_CASE__ ):
snake_case_ : Tuple ... | 48 | 1 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTokenizer,
Robert... | 495 |
'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
lowercase : Dict = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE__ ( __A=None , ... | 495 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
_lowercase : Tuple ={
"""configuration_speech_to_text""": ["""SPEECH_TO_TEXT_PRE... | 715 |
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.te... | 412 | 0 |
import warnings
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
... | 39 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowercase ( A__ ):
"""simple doc... | 189 | 0 |
"""simple docstring"""
def _A (__a = 50 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_st... | 719 |
"""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... | 176 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CA... | 591 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .to... | 591 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCAmelCase_ : Union[str, Any] = 0b101_100_111_110_110_010_010_000_011... | 713 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""",... | 440 | 0 |
# Copyright 2021 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 req... | 70 |
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
_a : ... | 479 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase__ = {
"configuration_squeezebert": [
"SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"SqueezeBertConfig",
"SqueezeBertOnnxConf... | 720 | import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default... | 594 | 0 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 100 ):
"""simple docstring"""
lowerCAmelCase__ : Any = set()
lowerCAmelCase__ : Optional[Any] = 0
lowerCAmelCase__ : List[str] = n + 1 # maximum limit
for a in r... | 565 |
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( UpperCamelCase = 600851475143 ):
"""simple docstring"""
try:
lowerCAmelCase__ : Union[str, Any] = int(UpperCamelCase )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or... | 565 | 1 |
import string
import numpy
def lowerCamelCase( a__ ,a__):
return b if a == 0 else greatest_common_divisor(b % a ,a__)
class A__ :
UpperCAmelCase = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total o... | 191 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def lowerCamelCase( a__ ,a__=() ,a__=None ,a__="no" ,a__="29500"):
_SCREAMING_SNAKE_CASE =False
_SCREAMING_SNAKE_C... | 191 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_atte... | 451 | '''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def A_ ( SCREAMING_SNAKE_CASE_ ) ->List[Any]:
lowercase_ = [
"""encoder.version""",
"""decoder.version""",
"""mo... | 451 | 1 |
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
SC... | 629 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
SCREAMING_SNAKE_CASE__ : int = {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": P... | 629 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',... | 114 |
import copy
import random
from transformers import CLIPTokenizer
class __A ( lowerCamelCase__ ):
"""simple docstring"""
def __init__( self , *a__ , **a__):
"""simple docstring"""
super().__init__(*a__ , **a__)
... | 114 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
__lowerCamelCase = ['small', 'medium', 'large']
__lowerCamelCase = 'lm_head.decoder.weight'
__lowerCamelCase = 'lm_head.weight'
def lowerCAmelCase_ ( _SCREAMING_SNAKE_CASE , _SCR... | 712 |
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
__lowerCamelCase = logging.get_logger(__name__)
class _UpperCamelCase( SCREAMING_SNAKE_CASE ):
def __init__( self : List[Any] , *_lowerCamelCase : int , **_lowerCamelCase ... | 328 | 0 |
"""simple docstring"""
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
__A : Dict = [
'kernels/rwkv/wkv_cuda.cu',
'kernels/rwkv/wkv_op.cpp',
'kernels/deformable_detr/ms_deform_attn.h',
'kernels/deformable_detr/cuda/ms_deform_im... | 575 |
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Imag... | 153 | 0 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def lowerCAmelCase_( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
... | 713 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available
from ...test_... | 20 | 0 |
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
fsspec_head,
ftp_get,
ftp_head,
ge... | 36 |
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
... | 302 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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 BackboneTesterMixin
from ...test_... | 710 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils i... | 671 | 0 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available... | 152 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Optional[Any] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
... | 152 | 1 |
import argparse
import os
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_task_guides.py
A_ : str = 'src/transformers'
A_ : Union[str, Any] = 'docs/so... | 705 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availabl... | 64 | 0 |
"""simple docstring"""
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... | 498 |
"""simple docstring"""
def __lowerCamelCase ( a_ : int = 50 ) -> int:
__SCREAMING_SNAKE_CASE :List[str] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in ... | 498 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _lowerCAmelCase ( A__ , A__ ):
lowercase__ = F'''{sampling_rate}'''
lowercase__ = '1'
lowercase__ = 'f32le'
lowercase__ = [
'ffmpe... | 708 |
import heapq
import sys
import numpy as np
a__ : Dict = tuple[int, int]
class UpperCAmelCase__:
'''simple docstring'''
def __init__( self : List[str]) -> Any:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
def ... | 642 | 0 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
... | 275 |
'''simple docstring'''
from collections.abc import Callable
class __snake_case :
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase : Callable | None = None ) -> None:
# Stores actual heap items.
... | 275 | 1 |
"""simple docstring"""
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
lowercase = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weig... | 703 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowercase = {
'''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxCo... | 24 | 0 |
import numpy as np
def lowerCAmelCase_ ( __a , __a , __a = 1e-12 , __a = 100 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(__lowerCamelCase )[0] == np.shape(__lowerCamelCase )[1]
# Ensure proper dime... | 59 |
"""simple docstring"""
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
... | 560 | 0 |
"""simple docstring"""
def __UpperCAmelCase ( __lowerCamelCase ) -> bool:
lowercase__ : List[str] = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def __UpperCAmelCase ( __lowerCamelCase = 50_00 ) -> int:
lowercase__ : ... | 122 |
"""simple docstring"""
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available... | 122 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class __snake_case( lowercase__ ):
'''simple docstring'''
def __init__( self , ... | 433 |
"""simple docstring"""
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(... | 594 | 0 |
'''simple docstring'''
import numpy as np
def A ( _UpperCAmelCase : str ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 703 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer... | 123 | 0 |
'''simple docstring'''
def _lowerCAmelCase ( lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : int ):
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''' )
for i in range(lowerCamelCase_ ):
for j in range(lowerCamelCase_ ):... | 502 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowercase ( lowerCAmelCase__ ):
'''simple docstring'''
a : List[Any] = ["image... | 502 | 1 |
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism... | 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 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerConfig''',
]... | 40 |
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class lowerCAmelCase_ ... | 40 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbeddi... | 712 | import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVec... | 71 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...tes... | 353 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
Vi... | 353 | 1 |
import argparse
import json
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 A... | 714 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
_lowerCamelCase : Optional[Any] = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("", ... | 196 | 0 |
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __lowercase (datasets.BuilderConfig ):
"""simple docstring"""
... | 101 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_lowerCAmelCase: Optional[Any] = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'S... | 20 | 0 |
from __future__ import annotations
def lowerCamelCase_ ( lowercase__):
return len(set(lowercase__)) == len(lowercase__)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 702 |
'''simple docstring'''
class lowercase :
'''simple docstring'''
def __init__( self : Any , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Optional[int] ) -> Optional[int]:
'''simple docstring'''
lowerCamel... | 187 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn
f... | 332 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"facebook/xmod-base": "https://huggingface.co/f... | 332 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _snake_case ( __snake_case , __snake_case=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
_UpperCamelCase = n - 1
_UpperCamelCase = 0
while d % 2... | 71 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowerCAmelCase = {
"configuration_jukebox": [
"JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
"JukeboxConfig",
"JukeboxPriorConfig",
"JukeboxVQVAEConfig",
],
... | 71 | 1 |
"""simple docstring"""
def UpperCAmelCase ( _lowercase : list[list] ) -> list[list]:
"""simple docstring"""
lowerCAmelCase_ = current_set.copy()
for row_index, row in enumerate(a_ ):
lowerCAmelCase_ = row[0]
for column_index, column ... | 552 |
"""simple docstring"""
UpperCAmelCase = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def lowerCam... | 677 | 0 |
"""simple docstring"""
_lowercase : Dict = '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.g... | 397 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import... | 397 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A_ = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_available():
raise Option... | 393 | '''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( snake_case_ : list[int | float] , snake_case_ : int , snake_case_ : int ) -> int | float:
'''simple docstring'''
if len(snake_case_ ) == 0:
raise ValueError("""find_max() ... | 427 | 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 appli... | 567 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxCon... | 567 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a_ = {'configuration_reformer': ['REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ReformerConfig']}
try:
if not ... | 25 |
'''simple docstring'''
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,
EfficientFormerForImageClassificationWithTe... | 24 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case :Optional[Any] ={
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}
try:
if not is_torch_avail... | 224 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__snake_case :Any ={
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
tr... | 224 | 1 |
'''simple docstring'''
import argparse
import os
from pathlib import Path
import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download
from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import ... | 41 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_a : Optional[Any] = logging.get_logger(__name__)
_a : Tuple = {
"camembe... | 56 | 0 |
UpperCamelCase__ : str = [
(1_000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def _UpperCAmelCase ( _SCREAMING_SNAKE_CASE : List[str]... | 715 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
UpperCamelCase__ : Optional[int] = datasets.utils.log... | 620 | 0 |
'''simple docstring'''
import unittest
from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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 Token... | 13 |
"""simple docstring"""
__A : Optional[int] = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''
... | 499 | 0 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
snake_case_ = logging.get_logger(__name__)... | 701 |
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
fr... | 262 | 0 |
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : int ):
if digit_amount > 0:
return round(number - int(__lowerCamelCase ) , __lowerCamelCase )
return number - int(__lowerCamelCase )
if __name__ == "__main__":
... | 63 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
_lowerCamelCase : Union[str, Any] = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # n... | 686 | 0 |
def SCREAMING_SNAKE_CASE_ ( __lowerCamelCase: int = 1 , __lowerCamelCase: int = 1000 ):
lowercase_ = 1
lowercase_ = 0
for divide_by_number in range(__lowerCamelCase , digit + 1 ):
lowercase_ = []
lowercase_ = numerator
for _ in range(1 , digit + 1 ):
... | 711 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
Trai... | 601 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : str ) -> bool:
SCREAMING_SNAKE_CASE_ : Optional[Any] =0
for ch in input_str:
SCREAMING_SNAKE_CASE_ : List[Any] =ord(UpperCAmelCase_ )
SCREAMING_SNAKE_CASE_ : str =pow(2... | 443 |
from math import ceil, sqrt
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : int = 1_0_0_0_0_0_0 ) -> int:
SCREAMING_SNAKE_CASE_ : List[Any] =0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
... | 443 | 1 |
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 ModelTesterMixin, ... | 714 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = list(range(len(SCREAMING_SNAKE_CASE ) ) )
lowercase__ = [v / w for v, w in zip(SCREAMING_SNAKE_CASE , ... | 429 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
a = {}
try:
if not is_sentencepiece_available():
raise OptionalDependenc... | 109 |
'''simple docstring'''
import gc
import unittest
from transformers import CTRLConfig, 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_modelin... | 109 | 1 |
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def __UpperCAmelCase ( UpperCamelCase__ :int ) -> typing.Counter[int]:
snake_case__ : typing.Counter[int] = Counter()
for base in range(1 , ... | 574 |
'''simple docstring'''
import argparse
import os
import re
_lowercase : str ="src/transformers"
# Pattern that looks at the indentation in a line.
_lowercase : List[Any] =re.compile(R"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : Optional[Any] ... | 574 | 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 transformers.... | 16 |
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=__snake_case ):
"""simple docstring"""
__A = ["""flax""", """transformers"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase ):
"""simple docstring"""
req... | 187 | 0 |
def SCREAMING_SNAKE_CASE_ ( UpperCAmelCase_ : list ) -> list:
SCREAMING_SNAKE_CASE_ : Tuple =False
while is_sorted is False: # Until all the indices are traversed keep looping
SCREAMING_SNAKE_CASE_ : Optional[Any] =True
... | 717 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having multipl... | 431 | 0 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common im... | 2 |
def _A ( SCREAMING_SNAKE_CASE ):
stooge(SCREAMING_SNAKE_CASE ,0 ,len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def _A ( SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i... | 113 | 0 |
_a : Any = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
fro... | 111 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a : str = {"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable(... | 111 | 1 |
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, r... | 333 |
import os
def __UpperCamelCase ( ):
"""simple docstring"""
with open(os.path.dirname(_lowerCAmelCase ) + "/grid.txt" ) as f:
UpperCAmelCase = [] # noqa: E741
for _ in range(20 ):
l.append([int(_lowerCAmelCase ) for x in f.readline().split()] )
... | 333 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCamelCase : str = "The quick brown fox jumps over the lazy dog" , ) -> bool:
__magic_name__ : Union[str, Any] = set()
# Replace all the whitespace in our sentence
__magic_name__ : List[Any] = input_... | 706 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
fr... | 147 | 0 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
__a = logging.get_logger(__name__)
@datacla... | 30 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImagePr... | 294 | 0 |
"""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... | 439 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class a__ ( UpperCamelCase_ ):
sna... | 439 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, l... | 287 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torc... | 287 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE =logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE ={
"microsoft/git-base": "https://huggingface.co/microsoft/git... | 712 | """simple docstring"""
from math import sqrt
def lowercase__( __SCREAMING_SNAKE_CASE : int ):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3... | 477 | 0 |
from collections import defaultdict
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
lowerCamelCase : str = total # total no of tasks (N)
# DP table will h... | 311 | class lowercase : # Public class to implement a graph
def __init__( self : Union[str, Any] , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : list[list[bool]] ) -> None:
'''simple docstring'''
... | 403 | 0 |
import operator as op
def _lowercase ( lowercase__ ):
__lowerCAmelCase : Union[str, Any] = []
__lowerCAmelCase : Optional[int] = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
__lowerCAmelCase : Optio... | 711 |
from __future__ import annotations
from math import ceil, floor, sqrt
def _lowercase ( lowercase__ = 2_0_0_0_0_0_0 ):
__lowerCAmelCase : list[int] = [0]
__lowerCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_nu... | 583 | 0 |
"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTokenizer,
WhisperForConditionalGeneration,
WhisperProcessor,
)
from diffusers import (
Autoenco... | 102 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case_ : List[Any] = {"configuration_vit_mae": ["VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMAEConfig"]}
try:
if not ... | 488 | 0 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> int:
return 1 if digit in (0, 1) else (digit * factorial(digit - 1 ))
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : Dict ) -> bool:
UpperCAmelCase_ : Optiona... | 716 |
'''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 __a (unittest.Te... | 644 | 0 |
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.... | 17 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
_UpperCAmelCase = TypeVar("T")
class _UpperCAmelCase ( Generic[T] ):
'''simple docstring'''
def __init__( self : Tuple , UpperCamelCase__ : T )... | 699 | 0 |
import re
import string
import numpy as np
import datasets
__UpperCamelCase : List[str] = '\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'
__UpperCamelCase : Optional[Any] = '\nArgs:\n... | 34 | import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import A... | 34 | 1 |
'''simple docstring'''
snake_case = 9.80665
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = g ):
"""simple docstring"""
if fluid_density <= 0:
raise ValueError("Impossible fluid density" )
if volume < 0:
raise ValueError("Impossible Ob... | 378 |
'''simple docstring'''
def UpperCAmelCase_ ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if height >= 1:
move_tower(height - 1 , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
move_disk(lowe... | 378 | 1 |
'''simple docstring'''
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weig... | 58 | '''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowercase_ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']... | 58 | 1 |
"""simple docstring"""
from math import isclose, sqrt
def _SCREAMING_SNAKE_CASE ( UpperCamelCase : float , UpperCamelCase : float , UpperCamelCase : float ):
A__ = point_y / 4 / point_x
A__ = 2 * normal_gradient... | 574 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertS... | 574 | 1 |
from __future__ import annotations
import math
class _a :
"""simple docstring"""
def __init__( self , _snake_case ):
_UpperCAmelCase =size
# approximate the overall size of segment tree with given value
_UpperCAmelCase =[0 for i ... | 715 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"split_dict" , [
SplitDict(),
SplitDict({"train": SplitInfo(name="train" , num_bytes=1337 , num_examples=42 , dataset_name="my_dataset" )} ),
Spli... | 592 | 0 |
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