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"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( A_, unittest.TestCase ... | 247 |
"""simple docstring"""
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPI... | 247 | 1 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __A ( lowerCAmelCase_ ):
_UpperCAmelCase : str = {}
_UpperCAmelCase : str = job["""started_at"""]
_UpperCAmelCase : List[str] = jo... | 350 |
'''simple docstring'''
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_... | 170 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase__ ( _UpperCAmelCase , _UpperCAmelCase ):
"""simple docstring"""
if len(_UpperCAmelCase ) == 0:
return False
A_ : Union[str, Any] = len(_UpperCAmelCase ) // 2
if ... | 286 |
"""simple docstring"""
def UpperCAmelCase__ ( _UpperCAmelCase ):
"""simple docstring"""
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
raise TypeError('\'float\' object cannot be interpreted as an integer' )
if isinstance(_UpperCAmelCase , _UpperCAmelCas... | 286 | 1 |
"""simple docstring"""
def lowerCAmelCase (__UpperCamelCase : Optional[Any] , __UpperCamelCase : List[str] ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 352 | """simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcesso... | 85 | 0 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from t... | 113 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase (SCREAMING_SNAKE_CASE_ : int , SCREAMING_SNAKE_CASE_ : Any=False ) -> str:
SCREAMING_SNAKE_CASE = ... | 113 | 1 |
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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
... | 41 |
def lowerCAmelCase_ ( _snake_case : int ) -> bool:
'''simple docstring'''
if not isinstance(_snake_case , _snake_case ):
__magic_name__ : Union[str, Any] = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_snake_case )
... | 41 | 1 |
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 PatchingSpec
from ..... | 87 |
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
A : str = logging.get_logger(__name__)
class __A( a ):
def... | 6 | 0 |
"""simple docstring"""
import functools
from typing import Any
def __UpperCAmelCase ( UpperCAmelCase_ : str , UpperCAmelCase_ : list[str] ) -> bool:
'''simple docstring'''
if not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or len(Up... | 95 | """simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
fro... | 95 | 1 |
import argparse
import os
import re
lowerCAmelCase__ : Dict = '''src/diffusers'''
# Pattern that looks at the indentation in a line.
lowerCAmelCase__ : Optional[Any] = re.compile(r'''^(\s*)\S''')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__ : Dict = ... | 143 | import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.h... | 143 | 1 |
"""simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffu... | 202 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer
_lowerCAmelCase : List[str] = {"vocab_file": "vocab.txt", "tokenizer... | 202 | 1 |
def __A ( __lowerCAmelCase )-> int:
"""simple docstring"""
if not grid or not grid[0]:
raise TypeError('The grid does not contain the appropriate information' )
for cell_n in range(1 , len(grid[0] ) ):
grid[0][cell_n] += grid[0][ce... | 39 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
_A = [
"""good first issue""",
"""feature request""",
"""wip""",
]
def a__ ( ) -> str:
UpperCAmelCase__ : Union[str, Any] = Github(os.environ["""GITHUB_TOKEN"""] ... | 171 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transf... | 269 |
import sys
lowercase_ = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6689664895044... | 269 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={
'''studio-ousia/luke-base''': '''https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json''',
'''studio-ousia/luke-large''': '''https://huggingface.co/studio-ousia/lu... | 19 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A ={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except Optional... | 19 | 1 |
from __future__ import annotations
from math import pi, sqrt
def a_ ( __lowercase : float , __lowercase : float ) -> tuple:
if inductance <= 0:
raise ValueError('Inductance cannot be 0 or negative' )
elif capacitance <= 0:
raise ValueError('Capacitance cannot be ... | 130 |
def a_ ( __lowercase : List[Any] ) -> Tuple:
_snake_case = len(__lowercase )
for i in range(length - 1 ):
_snake_case = i
for k in range(i + 1 , __lowercase ):
if collection[k] < collection[least]:
_snake_case = k
... | 130 | 1 |
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import Mo... | 62 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logg... | 170 | 0 |
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case__ (A__ ):
"""simple docstring"""
def __init__( self ) -> int:
"""simple docstring"""
self.test()
def SCREAMING_SNAKE... | 350 |
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class snake_case__ (ctypes.Structure ):
"""simple docstring"""
__lowerCAmelCase :Dict = [("size", c... | 266 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : List[str] = logging.get_logger(__name__)
_a : Dict = {
'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
# See all... | 44 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_SCREAMING_SNAKE_CASE : int = {
... | 85 | 0 |
"""simple docstring"""
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def __SCREAMING_SNAKE_CASE ( A_ = 8 ):
lowerCAmelCase__ : str = ascii_letters + digits + punctuation
return "".join(s... | 360 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig''',
... | 74 | 0 |
'''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
_A : Dict =loggin... | 41 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import... | 41 | 1 |
'''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
lowerCAmelCase :Optional[int] = logging.get_logger(__name__)
lowerCA... | 275 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 275 | 1 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils i... | 95 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
UpperCAmelCase : int = logging.get_logger(__name__)
... | 95 | 1 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : Union[str, Any] = len(__lowerCAmelCase )
_UpperCAmelCase : Union[str, Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum o... | 366 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
lowerCamelCase__ = list[list[float | int]]
def __lowerCAmelCase (__lowerCAmelCase , __lowerCAmelCase ):
_UpperCAmelCase : int = len(__lowerCAmelCase )
_Up... | 322 | 0 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
def __magic_name__ ( __snake_case : list[Any] ) -> None:
create_state_space_tree(__snake_case , [] , 0 )
def __magic_name__ ( __snake_case : list[Any] , __sna... | 202 |
"""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(... | 202 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slo... | 45 |
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> str:
if number > 0:
raise ValueError('input must be a negative integer' )
lowerCamelCase__ : str = len(bin(_UpperCAmelCase )[3:] )
lowerCamelCase__ : Dict = bin(abs(_UpperCAmelCase ) - (1 << binary_number_... | 45 | 1 |
"""simple docstring"""
import argparse
from collections import defaultdict
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Dict:
__lowerCAmelCase : Optional[int] = F"""{file}_{class_name}_{test... | 269 |
"""simple docstring"""
from __future__ import annotations
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ) -> list:
__lowerCAmelCase : Dict = []
__lowerCAmelCase , __lowerCAmelCase : Any = ... | 269 | 1 |
"""simple docstring"""
def _lowercase ( __lowerCAmelCase = 200 ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] = [1, 2, 5, 10, 20, 50, 100, 200]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [0] * (pence + 1)
SCREAMING_SNAKE_CASE__ : Tuple = 1 # base case:... | 56 |
"""simple docstring"""
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import... | 56 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See... | 130 |
import argparse
import requests
import torch
from PIL import Image
from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Union[str, Any] = SwinConfig(ima... | 130 | 1 |
UpperCamelCase = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def _A ( lowerCAmelCase_ : dict , lowerCAmelCase_ : Tuple , lowerCA... | 221 |
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.test_... | 221 | 1 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data im... | 318 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 266 | 0 |
'''simple docstring'''
import json
import os
import re
import unittest
from transformers import CodeGenTokenizer, CodeGenTokenizerFast
from transformers.models.codegen.tokenization_codegen import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_comm... | 370 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
A_ : List[Any] = 637_8137.0
A_ : Dict = 635_6752.31_4245
A_ : int = 6_3_7_8_1_3_7
def snake_case_ ( lowerCAmelCase_ ,... | 349 | 0 |
import unittest
from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 52 |
"""simple docstring"""
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_su... | 74 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, 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_comm... | 249 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE ( _a ):
"""simple do... | 249 | 1 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.... | 275 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
"https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/reso... | 275 | 1 |
"""simple docstring"""
lowerCAmelCase__ = [
(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'''... | 133 |
"""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 Tenso... | 133 | 1 |
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def lowerCAmelCase_ ( _snake_case : bool = True , *_snake_case : str , **_snake_case : int ) -> Dict:
'''simple docst... | 281 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsMod... | 281 | 1 |
"""simple docstring"""
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
fro... | 53 |
"""simple docstring"""
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Imag... | 53 | 1 |
"""simple docstring"""
import inspect
import math
import tempfile
import unittest
import numpy as np
from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available... | 45 |
"""simple docstring"""
def lowercase ( lowerCAmelCase__ : str , lowerCAmelCase__ : list[str] ) -> str:
__a = ''''''
for word_or_phrase in separated:
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise Exception('''join(... | 45 | 1 |
"""simple docstring"""
from ....utils import logging
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=2_048 ):
'''simple do... | 317 |
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
... | 317 | 1 |
'''simple docstring'''
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simp... | 56 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def __magic_name__ ( __UpperCAmelCase ) -> str:
'''simple docstring'''
snake_case_ = [
'''encoder.version''',
... | 56 | 1 |
import unittest
from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, i... | 102 |
from __future__ import annotations
def _a ( SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , SCREAMING_SNAKE_CASE_ : float , ):
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cann... | 102 | 1 |
"""simple docstring"""
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"BridgeTower/bridgetower-base": "https://huggingfa... | 221 | """simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
genera... | 221 | 1 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_cha... | 366 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimensio... | 337 | 0 |
"""simple docstring"""
import inspect
import warnings
from typing import Any, Dict, Optional, Union
from packaging import version
def a__ ( *SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Optional[Union[Dict, Any]] = None , SCREAMING_SNAKE_CASE : Dict=True ... | 108 |
'''simple docstring'''
import pytest
a__ : List[str] = '__dummy_dataset1__'
a__ : Optional[int] = '\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-t... | 349 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
lower... | 279 |
import random
from .binary_exp_mod import bin_exp_mod
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase=1_000 ) -> str:
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case_ ... | 279 | 1 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelFo... | 223 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase : Optional[Any] ={
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
... | 223 | 1 |
'''simple docstring'''
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_confi... | 287 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowercase : list[int], lowercase : list[int], lowercase : int ) -> tuple[float, list[float]]:
"""simple docstring"""
_UpperCamelCase = list(range(len(lowercase ) ) )
_UpperCamelC... | 287 | 1 |
from typing import List, Union
import numpy as np
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 PIL import Image
from ..image_utils import load_image
if is_torch... | 133 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
lowercase_ : str ... | 133 | 1 |
"""simple docstring"""
def A_ ( _lowerCAmelCase : int = 4_00_00_00 ):
"""simple docstring"""
_a = []
_a , _a = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(_lowerCAmelCase )
_a , _a = b, a + b
... | 153 |
"""simple docstring"""
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
'''simple docst... | 153 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a__ : int =logging.get_logger(__name__)
a__ : Dict ={
'''weiweishi/roc-bert-base-zh''': '''https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json''',
}
class ... | 53 |
'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules... | 53 | 1 |
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_attention_mask
... | 361 |
'''simple docstring'''
from __future__ import annotations
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> tuple[float, list[float]]:
UpperCAmelCase__ : Optional[Any] = list(range(len(lowerCAmelCase__ ) ) )
UpperCAmelCase... | 299 | 0 |
from ....utils import logging
a__ = logging.get_logger(__name__)
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict=None , ... | 317 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 317 | 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
lowercase : List[str] = """src/transformers"""
lowercase : Dict = ... | 285 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __snake_case ( lowerCAmelCase , unittest.TestCase ):
_a : ... | 285 | 1 |
"""simple docstring"""
def lowercase ( _snake_case : int , _snake_case : int ) ->str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
__snake_case : Tuple = str(bin(_snake_case ) ... | 102 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't ... | 102 | 1 |
def _lowercase ( _UpperCAmelCase = 10 , _UpperCAmelCase = 10_00 , _UpperCAmelCase = True ) -> int:
assert (
isinstance(_UpperCAmelCase , _UpperCAmelCase )
and isinstance(_UpperCAmelCase , _UpperCAmelCase )
and isinstance(_UpperCAmelCase , _Upper... | 262 |
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassif... | 262 | 1 |
'''simple docstring'''
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 104 |
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 __SCREAMING_SNAKE_CASE ( A__ ):
A : Union[List[np.... | 337 | 0 |
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionSchedu... | 145 |
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__UpperCAmelCase = 'Usage of script: script_name <size_of_canvas:int>'
__UpperCAmelCase = [0] * 100 + [1] * 10
random.shuffle(choice)
... | 145 | 1 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_UpperCamelCase ) , _UpperCamelCase )
return number - int(_UpperCamelCase )
if __name__ == "__main__":
print(decimal_iso... | 279 |
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'''):
lowerCAmelCase_ = {
'''linear''': PIL.Image.Resampling.BILINEAR,
'''bilinear''': PIL.Image.Resampling.BILINEAR,
... | 279 | 1 |
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 .tokenization_fnet im... | 36 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
if TYPE_CHECK... | 36 | 1 |
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 ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPo... | 5 |
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def _a ( lowerCamelCase ):
return "".join(sorted(lowerCamelCase ) )
def _a ( lowerCamelCase ):
return word_by_signature[signature(lowerCamelCase )]
_lowerC... | 287 | 0 |
"""simple docstring"""
import sys
from collections import defaultdict
class snake_case :
def __init__( self) ->Union[str, Any]:
a_ = []
def UpperCAmelCase__ ( self , __UpperCAmelCase) ->int:
return self.node_position[vertex]
def UpperCA... | 303 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class snake_case ( SCREAMING_SNAKE_CASE_ ):
a_ : Dict = """Speech2TextFeatureExtractor"""
a_ : str = """Speech2TextTokeni... | 303 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class _lowerCamelCase ( unittest.TestCase , _lowercase ):
def snake_case_ (self ) -> List[str]:
UpperCamelCase = l... | 153 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor
lowerCAmelCase__ = logging.get_logger(__name__)
class _lowerCamelCase ( _lowercase ):
def __init__(self , *__a , **__a ... | 153 | 1 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
lowercase_ ... | 356 |
'''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
_lowercase : List[Any] = logging.get_logger(__nam... | 264 | 0 |
"""simple docstring"""
from datetime import datetime
import matplotlib.pyplot as plt
import torch
def UpperCamelCase__ ( lowercase__ : Optional[Any] ):
for param in module.parameters():
snake_case : List[Any] = False
def UpperCamelCase__ ( ... | 148 |
def A__ ( __lowerCamelCase = 10_00 ):
SCREAMING_SNAKE_CASE_ = 2**power
SCREAMING_SNAKE_CASE_ = 0
while n:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 299 | 0 |
def _lowercase ( _UpperCAmelCase , _UpperCAmelCase ) -> float:
def get_matched_characters(_UpperCAmelCase , _UpperCAmelCase ) -> str:
lowerCamelCase =[]
lowerCamelCase =min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 262 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
UpperCAmelCase__ : Dict =logging.get_logger(__name__) # pylint: disable=invalid-name
class __A ( a ):
def _... | 262 | 1 |
from typing import Dict, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 285 |
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Any = logging.get_logger(__name__)
_UpperCAmelCase : Dict = {
"""nielsr/canine-s""": 2048,
}
# Unicode defines 1,114,11... | 285 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXL... | 168 |
"""simple docstring"""
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmel... | 168 | 1 |
import os
def lowerCAmelCase ( lowerCAmelCase_ = "input.txt" )-> int:
with open(os.path.join(os.path.dirname(lowerCAmelCase_ ) , lowerCAmelCase_ ) ) as input_file:
lowerCAmelCase_ : Union[str, Any] = [
[int(lowerCAmelCase_ ... | 262 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from... | 262 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ... | 280 |
'''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... | 280 | 1 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def __UpperCAmelCase ( a_: int = 1_000_000, a_: int = 10 ):
_UpperCAmelCase : defaultdict = defaultdict(a_ )
for outer_width in range(3, (t_limit // 4) + 2 ... | 145 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__a = logging.get_logger(__name__)
class A__ ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : Dict , *lo... | 145 | 1 |
lowercase : Optional[Any] = "Input must be a string of 8 numbers plus letter"
lowercase : int = "TRWAGMYFPDXBNJZSQVHLCKE"
def UpperCAmelCase_ (_lowerCAmelCase : str ):
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__UpperCamelCas... | 171 |
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCAmelCase_ (_lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple ... | 171 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_snake_case = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailab... | 36 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_... | 36 | 1 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf ... | 357 |
'''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... | 107 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print('''Googling.....''')
snake_case_ = '''https://www.google.com/search?q=''' + ''' '''.join(sys.argv[1:])
snake_case_ = requests.get(url, headers... | 214 |
import warnings
from ...utils import logging
from .image_processing_dpt import DPTImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ (__snake_case ):
def __init__( self , *a , **a):
warnings.warn(
'The class D... | 214 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCAmelCase__ : Optional[int] = {
"configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
"tokenization_ctrl": ["CTRLTokenizer"],
... | 356 |
from __future__ import annotations
from random import random
class UpperCamelCase :
def __init__( self , UpperCAmelCase__ = None ):
A__ = value
A__ = random()
A__ = None
A__ = None
def __repr__( self )... | 198 | 0 |
'''simple docstring'''
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class lowerCAmelCase_( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
def __init__( self ,__UpperCAm... | 37 |
"""simple docstring"""
import os
from datetime import datetime as dt
from github import Github
lowercase__ : int = [
'''good first issue''',
'''good second issue''',
'''good difficult issue''',
'''enhancement''',
'''new pipeline/model''',
'''new scheduler''',
'''wip''',... | 264 | 0 |
'''simple docstring'''
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = 0
# if input_string is "aba" than new_input_string become "a|b|a"
_lowerCAmelCase = """"""
_l... | 220 |
'''simple docstring'''
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
A__ : List[str] =TypeVar('''T''')
cla... | 220 | 1 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repli... | 282 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class SCREAMING_SNA... | 282 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils impo... | 350 | import math
class _UpperCAmelCase :
'''simple docstring'''
def __UpperCAmelCase ( self : Dict , lowercase_ : list[list[float]] , lowercase_ : list[int]) -> int:
"""simple docstring"""
_UpperCamelCase = 0.0
_UpperCamelCase ... | 63 | 0 |
'''simple docstring'''
import math
def _A (lowerCAmelCase__ :int = 1_00 ) -> int:
'''simple docstring'''
_a = sum(i * i for i in range(1 , n + 1 ) )
_a = int(math.pow(sum(range(1 , n + 1 ) ) ,... | 168 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
a_ : str = ... | 168 | 1 |
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
# load base model
_UpperCamelCase : ... | 364 |
'''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 im... | 236 | 0 |
from __future__ import annotations
def lowerCAmelCase_ ( __A ) -> Optional[Any]:
'''simple docstring'''
create_state_space_tree(snake_case_, [], 0, [0 for i in range(len(snake_case_ ) )] )
def lowerCAmelCase_ ( __A, __A, ... | 65 |
"""simple docstring"""
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBe... | 332 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def lowerCamelCase__ ( lowercase = "" ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
SCREAMING_SNAKE_C... | 319 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
snake_case = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependen... | 319 | 1 |
"""simple docstring"""
_A = 8.314_462 # Unit - J mol-1 K-1
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles... | 171 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase ( lowerCAmelCase__ , unitt... | 171 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase = {
'''configuration_roformer''... | 334 |
'''simple docstring'''
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/f... | 334 | 1 |
'''simple docstring'''
import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
fro... | 70 |
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformer... | 107 | 0 |
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
_lowercase = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value netwo... | 365 |
'''simple docstring'''
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
_lowercase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7""... | 229 | 0 |
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
UpperCamelCase__ = False
class a__ ( unittest.TestCase ):
def __SCREAMING_SNAKE_CASE( ... | 92 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase ):
lowercase__ : List[str] = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def __UpperCamelCase ( UpperCAmelCase = 100 ):
lowercase__ : Dict = 1
lowercase__ : Optional[in... | 198 | 0 |
'''simple docstring'''
import os
import pytest
from attr import dataclass
_snake_case = 'us-east-1' # defaults region
@dataclass
class a__ :
_SCREAMING_SNAKE_CASE : str
_SCREAMING_SNAKE_CASE : Tuple = 'arn:aws:iam::558105141721:role/sagemaker_execution_role'
_SCR... | 199 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
_snake_case = logging.getLogger()
@unittest.skip('Temporarily disable the doc ... | 199 | 1 |
"""simple docstring"""
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...te... | 220 |
"""simple docstring"""
from argparse import ArgumentParser
from .env import EnvironmentCommand
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
lowercase ... | 220 | 1 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax imp... | 355 |
'''simple docstring'''
import numpy as np
def __A ( lowerCAmelCase_ ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 170 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
i... | 63 |
'''simple docstring'''
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class __SCREAMING_SNAKE_CASE (lowerCamelCa... | 63 | 1 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class lowercase( __a ):
'''simple docstring'''
lowercase__ = (PNDMScheduler,)
lowercase__ = (("num_infe... | 132 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 10_00 ):
"""simple docstring"""
_snake_case , _snake_case : List[Any] = 1, 1
_snake_case : str = []
for i in range(1 , n + 1 ):
_snake_case : Any = prev_num... | 132 | 1 |
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.utils.data import DataLoad... | 299 |
"""simple docstring"""
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 241 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Optional[int] = ... | 89 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
__A : Any = {"vocab_file": "vocab.txt", "tokenizer_... | 89 | 1 |
__snake_case = 2_56
# Modulus to hash a string
__snake_case = 1_00_00_03
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = len(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ = len(U... | 176 |
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... | 326 | 0 |
"""simple docstring"""
from math import pi
def lowerCamelCase (a_ :int , a_ :int) -> float:
"""simple docstring"""
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 354 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transfor... | 172 | 0 |
"""simple docstring"""
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_configur... | 106 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__: List[str] = logging.get_logger(__name__)
A__: Union[str, Any] ... | 276 | 0 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Any
class lowerCamelCase :
"""simple docstring"""
def __init__(self : int , _A : Any ) -> Tuple:
snake_case = data
... | 354 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers... | 137 | 0 |
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionPipeline,
UNe... | 186 | '''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
_A : Optional[int] = logging.get_logger(__name__)
class _lowercase ( UpperCAmelCase__ ):
'''simple docstring'''
... | 229 | 0 |
import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path
import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset
from t... | 191 |
def _a ( SCREAMING_SNAKE_CASE__ : int ) -> str:
'''simple docstring'''
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(S... | 191 | 1 |
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