code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from __future__ import annotations
import typing
from collections import Counter
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : typing.Counter[int] = Counter()
for base in range(1 , max_p... | 3 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_speech_available,
is_tf_available,
is_torch_available,
)
lowercase : Dict = {
'configuration_speec... | 3 | 1 |
from __future__ import annotations
import math
import numpy as np
from numpy.linalg import norm
def snake_case_ ( lowerCAmelCase_ : np.ndarray , lowerCAmelCase_ : np.ndarray ):
return math.sqrt(sum(pow(a - b , 2 ) for a, b in zip(lowerCAmelCase_ , lowerCAmelCase_ ) ... | 306 |
import torch
from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde
from .test_schedulers import SchedulerCommonTest
@require_torchsde
class lowerCAmelCase ( __a ):
'''simple docstring'''
_A :... | 306 | 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_ba... | 136 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
d... | 173 | 0 |
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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENA... | 359 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
snake_case_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
snake_case_ = [file for file in filepaths if file != ... | 216 | 0 |
from collections import Counter
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
__lowerCAmelCase : List[Any] =datasets.load_iris()
__lowerCAmelCase : Tuple =np.array(data['data'])
__lowerCAmelCase : Dict =np.array(data['target'])
__lowerCAmelCase... | 9 |
"""simple docstring"""
def UpperCamelCase ( _lowerCAmelCase : int ) -> int:
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
_UpperCAmelCase : Optional[Any] = 1
_UpperCAmelCase : List[str] = 1
while repunit:
_UpperCAmelCase : Tuple = ... | 246 | 0 |
def lowerCamelCase__ ( a ) -> bool:
return str(a ) == str(a )[::-1]
def lowerCamelCase__ ( a ) -> int:
return int(a ) + int(str(a )[::-1] )
def lowerCamelCase__ ( a = 1_00_00 ) -> int:
_A: Tuple = ... | 362 |
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
UpperCAmelCase__ : Any = '.'
# Internal TensorFlow ops tha... | 301 | 0 |
import requests
_UpperCAmelCase : Union[str, Any] = "" # <-- Put your OpenWeatherMap appid here!
_UpperCAmelCase : int = "https://api.openweathermap.org/data/2.5/"
def A ( lowercase = "Chicago" , lowercase = APPID ) -> Optional[int]:
'''simple docstring''... | 222 |
"""simple docstring"""
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def __a ( ):
UpperCAmelCase_ : List[Any] = {
"repo_name": ["test_repo1", "test_repo2", "test_repo3"],
"p... | 61 | 0 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acce... | 27 |
"""simple docstring"""
def A_ ( snake_case_ : list[int] ):
'''simple docstring'''
if not numbers:
return 0
if not isinstance(snake_case_ ,(list, tuple) ) or not all(
isinstance(snake_case_ ,snake_case_ ) for number in numbers ):
... | 27 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
l... | 13 |
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: List[str] = [0] * len(_UpperCAmelCase )
SCREAMING_SNAKE_CASE_: List[Any] = []
SCREAMING_SNAKE_CASE_: str = []
SCREAMING_SNAKE_CASE_: List[str] = 0
for values in graph.values():
... | 13 | 1 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def _lowerCAmelCase ( UpperCAmelCase : ... | 157 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_SCREAMING_SNAKE_CASE : List[str] = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBi... | 157 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class __lowerCAmelCase :
lowercase = 42
lowercase = None
lowercase = ... | 316 |
"""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_param... | 316 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase : Optional[int] = logging.get_logger(__name__)
lowerCamelCase : Any = {
'''xlm-mlm-en-2... | 352 |
import math
class _a :
def __init__( self : List[Any] , _SCREAMING_SNAKE_CASE : Any=0 )-> Optional[Any]: # a graph with Node 0,1,...,N-1
lowerCAmelCase__ : Optional[int] = n
lowerCAmelCase__ : List[Any] = [
[math.inf fo... | 211 | 0 |
"""simple docstring"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from ...utils import deprecate
from ..controlnet.multicontrolnet import MultiControlNetModel # noqa: F401
fro... | 64 |
"""simple docstring"""
from typing import Dict, Optional
import numpy as np
import datasets
UpperCAmelCase : Tuple = "\nIoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union\nbetween the predicted segmentation and the ground truth... | 136 | 0 |
'''simple docstring'''
import operator as op
__snake_case ="scaler.pt"
__snake_case ="pytorch_model"
__snake_case ="random_states"
__snake_case ="optimizer"
__snake_case ="scheduler"
__snake_case ="pytorch_model.bin"
__snake_case ="pytorch_model.bin.index... | 369 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
f... | 55 | 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_backbone ... | 75 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from... | 214 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ = {
"""configuratio... | 353 |
"""simple docstring"""
from typing import Any
def _snake_case ( lowercase__ ):
if not input_list:
return []
_lowerCamelCase : Any = [input_list.count(lowercase__ ) for value in input_list]
_lowerCamelCase : Dict = max... | 12 | 0 |
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import regex
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import ... | 138 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
__A : Any = datasets.utils.logging.get_logger(__name__)
@dataclass
class __A ( datasets.Bui... | 138 | 1 |
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
... | 369 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
lowerCAmelCase_ = logging.get_logger(__name__)
class lowerCamelCase ( __lowerCAmelCase ):
def __init__( self, *lowercase_, **lowercase_ ) -> None:
war... | 332 | 0 |
'''simple docstring'''
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 AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils... | 311 |
'''simple docstring'''
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment
class UpperCamelCase__ ( unittest.TestCase )... | 311 | 1 |
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase =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
# This is ... | 352 |
'''simple docstring'''
from __future__ import annotations
from random import choice
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] ):
'''simple docstring'''
return choice(__lowerCamelCase )
def lowerCamelCase__ ( __lowerCamelCase : ... | 242 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__lowercase : Optional[Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')
def ... | 27 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowercase : Union[str, Any] = {
'configuration_blenderbot': [
... | 27 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__lowerCamelCase : int = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2... | 370 |
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torc... | 286 | 0 |
import os
import jsonlines
import numpy as np
from tqdm import tqdm
lowerCAmelCase = 2_0_4_8
lowerCAmelCase = 4_0_9_6
lowerCAmelCase = 4_2
lowerCAmelCase = os.environ.pop('''PROCESS_TRAIN''', '''false''')
lowerCAmelCase = {'''null''': 0, '''short''': 1, '''lo... | 295 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _lowerCamelCase( lowercase__=None ... | 295 | 1 |
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
lowerCamelCase :str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
lowerCamelCase :list[int] = [ord(letter) for... | 135 |
'''simple docstring'''
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
lowerCamelCase :Optional[int] ... | 135 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
... | 107 | """simple docstring"""
import math
import flax.linen as nn
import jax.numpy as jnp
def a_ ( _lowerCAmelCase : jnp.ndarray , _lowerCAmelCase : int , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1 , _lowerCAmelCase : float = 1.0E4 , _lowerCAmelCase : bool = False , _lo... | 77 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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 Co... | 350 | import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sent... | 204 | 0 |
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> List[str]:
'''simple docstring'''
print(F"""Vertex\tShortest Distance from vertex {src}""" )
for i, d in enumerate(UpperCamelCase__ ):
print(F"""{i}\t\t{d}""" )
... | 273 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 273 | 1 |
'''simple docstring'''
import html
from ...feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from ...utils import is_bsa_available, logging, requires_backends
if is_bsa_available():
import bsa
from bsa import BeautifulSoup
lowercase =logging.get_logger(__name__)
class __m... | 366 |
'''simple docstring'''
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
Autoen... | 242 | 0 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
lowerCAmelCase : Dict = {
"""facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main... | 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
UpperCAmelCase_ = {
'configuration_vision_encoder_decoder': ['VisionEncoderDecoderConfig', 'VisionEncoderDecoderOnnxConfi... | 12 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
UpperCAmelCase : Tuple = "\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to ME... | 360 |
"""simple docstring"""
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegas... | 313 | 0 |
'''simple docstring'''
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def a_ ( __snake_case : str , __snake_case : str , **__snake_case : Optional[Any] ) -> int:
"""simple docstring"""
... | 75 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 75 | 1 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase : Any = """docs/source/en/_toctree.yml"""
def A_ ( _UpperCAmelCase ):
SCREAMING_SNAKE_CASE_: str = defaultdict(_UpperCAmelCase )
for doc in model_doc:
counts[doc[... | 127 |
import os
# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase : Optional[int] = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def A_ ( ):
SCREAMING_SNAKE_CASE_: List[str] = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
SCREAMI... | 127 | 1 |
'''simple docstring'''
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : bool = Fals... | 23 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def lowerCamelCase__ ( __snake_case, __snake_case, __snake_case, __snake_case=5 ) -> Union[str, Any]:
"""simple docstring"""
assert masked_in... | 194 | 0 |
def __snake_case ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str ) -> List[str]:
return base * power(_lowerCAmelCase , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('''Raise base to the power of exponent using recursion...''')
_lowerCAmelCas... | 354 |
from collections.abc import Sequence
def __snake_case ( _lowerCAmelCase : Sequence[int] | None = None ) -> int:
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
A_ : Any = nums[0]
for i in range(1 , len(_lowerCAmelCase ) ):
... | 70 | 0 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ... | 36 |
"""simple docstring"""
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 Mode... | 100 | 0 |
"""simple docstring"""
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class lo... | 350 |
import random
from typing import Any
def UpperCAmelCase_ ( __UpperCAmelCase : list ) -> list[Any]:
for _ in range(len(__UpperCAmelCase ) ):
SCREAMING_SNAKE_CASE_ = random.randint(0 , len(__UpperCAmelCase ) - 1 )
... | 210 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowercase = {
'''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
'''tokenization_ctrl''': ['''CTRL... | 272 | '''simple docstring'''
from ...utils import logging
from ..ta.modeling_tf_ta import TFTaEncoderModel, TFTaForConditionalGeneration, TFTaModel
from .configuration_mta import MTaConfig
__lowercase = logging.get_logger(__name__)
__lowercase = '''T5Config'''
class a__( lowerCAmelCas... | 272 | 1 |
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
'The converte... | 116 |
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> int:
'''simple docstring'''
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError('''The length of profit and weight must be same.''' )
if max_weight <= 0:
... | 116 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : Optional[int] = {
"funnel-transformer/small": "https://huggingface.co/funnel-transform... | 85 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_mo... | 317 | 0 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 1 , _lowerCamelCase : int = 1_000) -> int:
'''simple docstring'''
__UpperCamelCase : Tuple = 1
__UpperCamelCase : Any = 0
for divide_by_nu... | 355 |
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 10 , _lowerCamelCase : int = 22) -> int:
'''simple docstring'''
__UpperCamelCase : Any = range(1 , _lowerCamelCase)
__UpperCamelCase : int = r... | 151 | 0 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
__A = logging.get_logger(__name__)
__A = {
'''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
# See all DPT ... | 135 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class _lowerCamelCase ( a_ ):
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
return [
... | 242 | 0 |
'''simple docstring'''
import warnings
from pathlib import Path
from typing import List, Tuple, Union
import fire
from torch import nn
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging
__snake_case = logging.get_logger(__name__)
... | 367 |
'''simple docstring'''
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormat... | 219 | 0 |
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_te... | 88 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}
class SCREAMING_SNAKE_CASE__ (__snake_case )... | 214 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
... | 365 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase = '▁'
_UpperCAmelCase = {'vocab_file': 'spiece.model'}
... | 232 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ ... | 201 |
"""simple docstring"""
class A_ :
"""simple docstring"""
def __init__( self :List[Any] , lowercase_ :int ) -> None:
UpperCAmelCase = size
UpperCAmelCase = [0] * size
UpperCAmelCase ... | 78 | 0 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase_ = """\
@misc{wu2016googles,
title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},
author={Yonghui Wu a... | 365 |
def lowerCamelCase_ ( lowerCAmelCase: int )-> bool:
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 260 | 0 |
"""simple docstring"""
from __future__ import annotations
def A__ ( UpperCamelCase , UpperCamelCase , UpperCamelCase , ):
if (stress, tangential_force, area).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
eli... | 292 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : Optional[int] = {
'google/vivit-b-16x2-kinetics400': (
'https://huggingface.co/google/vi... | 292 | 1 |
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
UpperCamelCase = {
'''facebook/mask2former-swin-small-coco-instance''': (
'''https://huggingface.co/facebook/mask2former-swi... | 353 |
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 transformers.utils impor... | 65 | 0 |
"""simple docstring"""
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : List[str] ) -> int:
A = ''
A = ''
A ... | 74 |
"""simple docstring"""
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def __lowerCamelCase ( a_ : str , a_ : Dict , a_ : Any , a_ : str ) -> str:
__SCREAMING_SNAKE_CASE :... | 191 | 0 |
from math import factorial
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int , lowerCAmelCase: int , lowerCAmelCase: float ) -> float:
if successes > trials:
raise ValueError("successes must be lower or equal to trials" )
if trials < 0 or successes < 0:
... | 189 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelera... | 189 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available
__A = {
"""configuration_ernie""": ["""ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ErnieConfig""", """ErnieOnnxConfig"""]... | 293 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__A = logging.getLogger(__name__)
class _lowerCAmelCase ( a ):
"""simple docstrin... | 293 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class a :
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
if len(lowerCAmelCase_ ) != degree + 1:
raise ... | 81 | import colorsys
from PIL import Image # type: ignore
def snake_case ( snake_case__ :float , snake_case__ :float , snake_case__ :int) -> float:
_A = x
_A = y
for step in range(snake_case__): # noqa: B007
... | 81 | 1 |
import copy
import re
class A :
'''simple docstring'''
__lowerCamelCase : Dict = '''hp'''
__lowerCamelCase : List[str] = {}
__lowerCamelCase : Any = None
@classmethod
def a_ ( cls : List[str] ... | 274 |
from sklearn.metrics import fa_score
import datasets
A : Any = '''
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
'''
A : List[Any] = '''
Args:
predictions (`li... | 274 | 1 |
class lowerCamelCase :
"""simple docstring"""
def __init__( self : Tuple ) -> Optional[Any]:
SCREAMING_SNAKE_CASE_ = {}
def __A ( self : Tuple ) -> None:
print(self.vertex )
for i in self.vertex:
... | 352 | from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils... | 305 | 0 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class A__ :
_UpperCAmelCase :float
_UpperCAmelCase :TreeNode | None = None
_UpperCAmelCase :TreeNode | None = None
def A_ ( _lowerCAmelCase ) -> bool:
# Validat... | 52 |
"""simple docstring"""
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__A = logging.getLogger(__name__)
class _lowerCAmelCase ( a ):
"""simple docstrin... | 293 | 0 |
def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> "list[int]":
"""simple docstring"""
if upper_limit < 0:
raise ValueError("""Limit for the Catalan sequence must be ≥ 0""" )
SCREAMING_SNAKE_CASE__ = [0] * (upper_limit + 1)
# Base c... | 204 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : Union[str, Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = len(__UpperCamelCase )
for i in range(length - 1 ):
SCREAMING_SNAKE_CASE__ = i
for k in range(i + 1... | 204 | 1 |
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( A__ ) -> int:
"""simple docstring"""
if not nums:
return 0
UpperCamelCase = nums[0]
UpperCamelCase = 0
for num in nums[1:]:
UpperCamelCas... | 28 |
'''simple docstring'''
from __future__ import annotations
def _a( UpperCamelCase__ : list[int] ):
'''simple docstring'''
if not nums:
return 0
SCREAMING_SNAKE_CASE__ : Dict =nums[0]
SCREAMING_SNAKE_CAS... | 152 | 0 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = len(lowerCamelCase)
for i in range(1, lowerCamelCase):
__lowerCAmelCase = collection[i]
__lowerCAmelCase = 0
__lowerCAmelCase ... | 355 |
'''simple docstring'''
def __magic_name__( lowerCamelCase):
__lowerCAmelCase = 1
__lowerCAmelCase = 2
while i * i <= n:
__lowerCAmelCase = 0
while n % i == 0:
n //= i
multiplicity +=... | 9 | 0 |
"""simple docstring"""
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
"""simple docstring"""
def sna... | 100 |
'''simple docstring'''
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load... | 200 | 0 |
import json
import os
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavin... | 119 | import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class a_ :
'''simple docstring'''
UpperCAmelCase_ = None
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ = False
UpperCAmelCase_ ... | 119 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
'SCUT-DLVCLab/lilt-roberta-en-base': (
'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
... | 145 | '''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
... | 145 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import requests
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_... | 362 |
import qiskit
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : int = 2) -> qiskit.result.counts.Counts:
'''simple docstring'''
__UpperCamelCase : List[str] = qubits
# Using Aer's simulator
__UpperCamelCase : i... | 151 | 0 |
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
__UpperCAmelCase : Any = "\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author = \"Popovi{\'c}, Maja\",\n bo... | 111 |
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from ..image_utils import load_image
if is_torch_available():
import torch... | 111 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__... | 101 |
'''simple docstring'''
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s''',
datefmt='''%m/%d/%Y %H:%M:%S''',
level=logging.INFO,
)
__lowerCamelCase... | 101 | 1 |
"""simple docstring"""
import datasets
lowerCAmelCase_ = '\\n@InProceedings{conneau2018xnli,\n author = "Conneau, Alexis\n and Rinott, Ruty\n and Lample, Guillaume\n and Williams, Adina\n and Bowman, Samuel R.\n ... | 16 |
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class a_ ( lowerCamelCase ):
lowercase = (DDPMParallelScheduler,)
def A__ ( self , **_SCREAMING_SNAKE_CASE ... | 321 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.js... | 369 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_fea... | 254 | 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_backbone ... | 75 |
"""simple docstring"""
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
| 241 | 0 |
'''simple docstring'''
import math
from collections.abc import Callable
def _lowerCamelCase ( lowerCamelCase_ : Callable[[float], float] , lowerCamelCase_ : float , lowerCamelCase_ : float ):
"""simple docstring"""
UpperCAmelCase_ : float ... | 274 | '''simple docstring'''
def _lowerCamelCase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def _lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' ... | 274 | 1 |
"""simple docstring"""
import itertools
import math
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
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 ... | 64 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ (snake_case__ : list[int] , snake_case__ : int ):
"""simple docstring"""
if len(snake_case__ ) < k or k < 0:
raise ValueError("""Invalid Input""" )
_snake_case ... | 64 | 1 |
"""simple docstring"""
import random
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> Optional[Any]:
__lowerCAmelCase : Union[str, Any] = a[left_index]
__lowerCAmelCase : Union[str, Any] = left_index + 1
for j in ... | 362 |
"""simple docstring"""
__snake_case : Any = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66... | 58 | 0 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( __UpperCamelCase , __UpperCamelCase ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase_ : list[list[int]] = []
create_all_state(1 , lowercase__ , ... | 241 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : ... | 9 | 0 |
def A__ ( __lowerCamelCase = 1, __lowerCamelCase = 10_00 ):
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 0
for divide_by_number in range(__lowerCamelCase, digit + 1 ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = numerator
fo... | 257 |
__UpperCAmelCase = [
(10_00, "M"),
(9_00, "CM"),
(5_00, "D"),
(4_00, "CD"),
(1_00, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = {'''... | 257 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = lo... | 119 |
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 ...te... | 119 | 1 |
'''simple docstring'''
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
... | 123 |
'''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.utils... | 123 | 1 |
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
cached_file,
get_file_f... | 348 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__snake_case = get_tes... | 310 | 0 |
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_onn... | 39 |
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class SCREAMING_SNAKE_CASE__ ( UpperCamelCase__ ):
__SCREAMING_SNAKE_CASE = '''MCTCTFeatureExtractor'''
__SCREAMING_SNAKE_CASE = '''AutoTokenizer'''
... | 39 | 1 |
import cmath
import math
def _A ( SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float , SCREAMING_SNAKE_CASE__ : float ):
UpperCamelCase :Dict = math.radians(SCREAMING_SNAKE_CASE__ )
UpperCamelCase :... | 259 |
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 ...test_configuration_commo... | 259 | 1 |
def _SCREAMING_SNAKE_CASE ( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
_UpperCAmelCase = generate_large_matrix()
_UpperCAmelCase = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, ... | 232 |
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 logging
_UpperCAmelCase ... | 232 | 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
from ..auto import CONFIG_MAPPING
A_ : Tuple = logging.get_logger(__name__)
A_ ... | 333 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
... | 333 | 1 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
A__ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def a_ ( _... | 369 |
'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import... | 0 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
CenterCrop,
Compose,
Normalize,
... | 185 |
'''simple docstring'''
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
A__ : str = [
# tf -> hf
("""/""", """."""),
("""layer_""", """layers."""... | 185 | 1 |
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokeniza... | 368 |
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class lowerCamelCase :
"""simple docstring"""
... | 191 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}
try:
if... | 46 |
'''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... | 319 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
# See all GPTNeoX models at https://h... | 368 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class __a ( unittest.TestCase ):
def A ( self : List[Any] ):
lowerCAmelCase_ : Dict = Vector([1, 2, 3] )... | 28 | 0 |
import argparse
import importlib
from pathlib import Path
# Test all the extensions added in the setup
_lowerCAmelCase : List[str] = [
"kernels/rwkv/wkv_cuda.cu",
"kernels/rwkv/wkv_op.cpp",
"kernels/deformable_detr/ms_deform_attn.h",
"kernels/deformable_detr/cuda/ms_deform_i... | 218 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCAmelCase : Tuple = logging.get_logger(__name__)
_lowerCAmelCase : Union[str, Any] = {"ctrl": "https://huggingface.co/ctrl/resolve/main/config.json"}
class __magic_name__ ( lo... | 218 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Union[str, Any] = {
"""facebook/s2t-wav2vec2-large-en-de""": (
"""https://huggingface.co/facebook/s2t-wav2vec2-large-en-d... | 368 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
_a : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailabl... | 46 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE__ ( __UpperCAmelCase , __UpperCAmelCase ) -> Tuple:
lowercase__: Any = len(_A )
lowercase__: Tuple = len(_A )
lowercase__: List[Any] = (
first_str_length if first_str_length > second_str_length else sec... | 177 |
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 __UpperCamelCase ( _A , _A ):
lowerCAmelCase_ = args.log_outputs
lowerCAmelCase_ = ... | 278 | 0 |
from __future__ import annotations
def __magic_name__ ( A , A = None , A = None , A = False , ) -> tuple[int, float, str]:
snake_case = cipher_alphabet or [chr(A ) for i in range(9_7 , 1_2_3 )]
# If the argument is None or the user provided an empty ... | 371 |
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase ( __lowerCAmelCase ):
snake_case_ = ''''''
snake_case_ = (
None # pr... | 332 | 0 |
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemant... | 39 |
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
_UpperCAmelCase = {} # Mapping from char to TrieNode
_UpperCAmelCase = False
def UpperCamelCase ( s... | 39 | 1 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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 Conf... | 367 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import log... | 22 | 0 |
"""simple docstring"""
import csv
import tweepy
# Twitter API credentials
UpperCAmelCase_ : Union[str, Any] = """"""
UpperCAmelCase_ : Any = """"""
UpperCAmelCase_ : List[Any] = """"""
UpperCAmelCase_ : Tuple = """"""
def _A (__a ) -> Tuple:
"""simple... | 91 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta... | 59 | 0 |
import math
class lowercase_ :
def __init__( self , __UpperCamelCase=0 ): # a graph with Node 0,1,...,N-1
"""simple docstring"""
UpperCamelCase_ = n
UpperCamelCase_ = [
[math.inf for j in range(0 , __UpperCamelCase ... | 261 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('''To use the rich extension, install rich with `pip install rich`''')
| 261 | 1 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import torch
from einops import rearrange, reduce
from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffuser... | 42 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import requi... | 0 | 0 |
'''simple docstring'''
A = 9.8_06_65
def lowerCAmelCase__ ( lowerCamelCase : float ,lowerCamelCase : float ,lowerCamelCase : float = g ):
if fluid_density <= 0:
raise ValueError('Impossible fluid density' )
if volume... | 351 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import Patchi... | 227 | 0 |
"""simple docstring"""
_a = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __a ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase... | 61 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_A = {"configuration_xglm": ["XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XG... | 231 | 0 |
"""simple docstring"""
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCAmelCase (__UpperCamelCase : int ):
"""simple docstring"""
__UpperCamelCase =prime_factors(__UpperCamelCase )
if is_square_free(__UpperCamelCase ... | 85 | """simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
... | 85 | 1 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
f... | 84 |
'''simple docstring'''
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six... | 28 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class __a ( lowerCamelCase__ ):
__snake_case : Dict = 'encoder-decoder'
__snake_case : int = True
... | 356 |
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 ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__U... | 28 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase : Any = logging.get_logger(__name__)
UpperCamelCase : Dict = {
"google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
# See all PEGASUS m... | 316 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def ... | 316 | 1 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE :Optional[int] = re.compile(R'([A-Z]+)([A-Z][a-z])')
SCREAMING_SNAKE_CASE :Optional[int] = re.compile(R'([a-z\d])([A-Z])')
SCREAMING_SNAKE_CASE :List[str] = re.compile(R'(?<!_)_(?!_)')
... | 356 |
import numpy
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : List[str] ,A : numpy.ndarray ,A : numpy.ndarray ):
__A = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previou... | 124 | 0 |
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