code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
A : Union[str, Any] = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1_000}
def _a ( lowerCamelCase_ ):
snake_case : Optional[int] =0
snake_case : List[str] =0
while index < len(_UpperCA... | 349 |
'''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_squeezebert import SqueezeBertTokenizer
snake_case_ : Tuple = loggin... | 212 | 0 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase = logging.get_logger(__name__)
__UpperCamelCase = [
['attention', 'attn'],
['encoder_atten... | 185 |
def UpperCamelCase_( _A :str )-> str:
UpperCamelCase__ = ""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def UpperCamelCase_( _A :str )-> dict[str, str]:
UpperCamelCase__ = [chr(i... | 185 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor
__snake_case = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lo... | 200 |
"""simple docstring"""
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
__snake_case = logging.get_logger(__name__)
__snake_case = '▁'... | 200 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class _UpperCamelCase ( unittest.Test... | 703 |
'''simple docstring'''
def _A ( A ) -> list:
lowercase : Optional[Any] = False
while is_sorted is False: # Until all the indices are traversed keep looping
lowercase : List[str] = True
for i in range(0 ,len(A ) - 1 ,2 ): # iterating over... | 425 | 0 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes... | 80 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
... | 635 | 0 |
'''simple docstring'''
import argparse
a = "docs/source/_static/js/custom.js"
def __magic_name__ ( __UpperCAmelCase ) -> Tuple:
'''simple docstring'''
with open(__UpperCAmelCase , encoding="""utf-8""" , newline="""\n""" ) as f:
__SCRE... | 711 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequen... | 13 | 0 |
from __future__ import annotations
from collections.abc import Callable
def _A ( SCREAMING_SNAKE_CASE__ : Callable[[int | float], int | float] , SCREAMING_SNAKE_CASE__ : int | float , SCREAMING_SNAKE_CASE__ : int | float , SCREAMING_SNAKE_CASE__ : int = 100 ... | 658 |
from __future__ import annotations
def _A ( SCREAMING_SNAKE_CASE__ : tuple[int, int] , SCREAMING_SNAKE_CASE__ : int ):
UpperCamelCase , UpperCamelCase :List[Any] = position
UpperCamelCase :Any = [
(y + 1, x + 2),
(y - 1, x + 2)... | 658 | 1 |
'''simple docstring'''
import unittest
from transformers import BigBirdConfig, 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():
import jax
fr... | 654 | '''simple docstring'''
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
"The `image_to_image.py` script is outdated. Please use directly `from diffusers import"
" StableDiffusionImg2ImgPipeline` instead."
)
| 654 | 1 |
import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class __lowercase :
"""simple docstring"""
_UpperCAmelCase : Optional[Union[str, Path]] = None
_UpperCAmelCase : bool = False
_UpperCAm... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
from __future__ import annotations
from math import ceil, floor, sqrt
def _lowercase ( lowercase__ = 2_0_0_0_0_0_0 ):
__lowerCAmelCase : list[int] = [0]
__lowerCAmelCase : int
for idx in range(1 , ceil(sqrt(target * 2 ) * 1.1 ) ):
triangle_nu... | 583 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import... | 583 | 1 |
"""simple docstring"""
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import Pr... | 609 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ = logging.get_logger(__name__)
A_ = {
'''s-JoL/Open-Llama-V1''': '''https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json''',
}
cla... | 609 | 1 |
"""simple docstring"""
import logging
import re
import pytorch_quantization
import pytorch_quantization.nn as quant_nn
import torch
from pytorch_quantization import calib
from pytorch_quantization.tensor_quant import QuantDescriptor
_snake_case = logging.getLogger(__name__)
_sn... | 659 |
"""simple docstring"""
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
_snake_case = WebClient(token=os.environ["CI_SLACK_BOT_TOKEN"])
def sna... | 659 | 1 |
'''simple docstring'''
from functools import lru_cache
def snake_case ( a_ : int ) -> set:
"""simple docstring"""
UpperCamelCase_ : Any = 2
UpperCamelCase_ : int = set()
while i * i <= n:
if n % i:
... | 208 |
'''simple docstring'''
from __future__ import annotations
from collections import namedtuple
def snake_case ( a_ : float , a_ : float , a_ : float ) -> tuple:
"""simple docstring"""
UpperCamelCase_ : Tuple = namedtuple("""r... | 208 | 1 |
"""simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
... | 197 | """simple docstring"""
import os
from pathlib import Path
from unittest.mock import patch
import pytest
import zstandard as zstd
from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
OfflineModeIsEnabled,
cached_path,
fsspec_get,
... | 197 | 1 |
"""simple docstring"""
import colorsys
from PIL import Image # type: ignore
def _lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : int ):
lowercase__ : Tuple = x
lowercase__ : int = ... | 200 |
'''simple docstring'''
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | 685 | 0 |
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import e... | 655 |
def snake_case_ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if len(_SCREAMING_SNAKE_CASE ) != len(_SCREAMING_SNAKE_CASE ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zer... | 655 | 1 |
import argparse
import torch
from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging
logging.set_verbosity_info()
def _a ( SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ ... | 663 |
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobert... | 663 | 1 |
'''simple docstring'''
import os
__a = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : str = 0
snake_case__ : List[Any] = 0
while index < len(__A ) - 1:
... | 708 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> Dict: # noqa: E741
snake_case__ : List[Any] = len(_lowerCAmelCase )
snake_case__ : str = 0
snake_case__ : str = [0] * n
snake_case__ : Optional[Any] ... | 301 | 0 |
"""simple docstring"""
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_accelerat... | 357 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_sta... | 357 | 1 |
'''simple docstring'''
def lowerCamelCase__ ( __lowerCamelCase : str ):
'''simple docstring'''
_UpperCAmelCase : int =''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def ... | 331 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils... | 331 | 1 |
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMSchedule... | 28 | '''simple docstring'''
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_... | 309 | 0 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Dict:
A_ = tf.convert_to_tensor(UpperCAmelCase__ )
A_ = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ), x... | 717 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia a... | 667 | 0 |
'''simple docstring'''
from __future__ import annotations
def a ( __a , __a , __a , __a ) -> list:
'''simple docstring'''
UpperCamelCase__ :List[str] = []
UpperCamelCase__ , UpperCamelCase__ :Any = input_list[low:mid], input_... | 189 |
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowercase ( A__ ):
"""simple doc... | 189 | 1 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_ava... | 701 |
'''simple docstring'''
def _A ( snake_case__ : int = 4_00_00_00 ):
snake_case__ : int = []
snake_case__ ,snake_case__ : Union[str, Any] = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(snake_case__ )
snake_case__ ,snake_case__ : Any = b, a + b
return s... | 694 | 0 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class snake_case_ ( __A ):
'''simple docstring'''
def snake_case__( self : Tuple ) ->Optional[Any]:
return [
... | 39 |
'''simple docstring'''
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def UpperCAmelCase__ ... | 288 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ : Tuple = {
'''configuration_electra''': [''... | 265 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase_ : List[str] = TypeVar('''T''')
lowerCamelCase_ : Optional[int] = TypeVar('''U''')
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ... | 265 | 1 |
from torch import nn
def _snake_case (__lowercase):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"""Unsupported activation function:... | 23 |
import math
from datetime import datetime, timedelta
def _snake_case (__lowercase):
UpperCamelCase_ = year % 19
UpperCamelCase_ = year % 4
UpperCamelCase_ = year % 7
UpperCamelCase_ = math.floor(year / 100)
UpperCamelCase_ = math.flo... | 23 | 1 |
from sklearn.metrics import mean_squared_error
import datasets
lowercase_ = "\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, ... | 390 | import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have ... | 390 | 1 |
"""simple docstring"""
import unittest
from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow
if is_flax_available():
import jax
from transformers.models.auto.modeling_flax_... | 77 |
"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
def __a ( A=None , A=None ) -> int:
... | 337 | 0 |
'''simple docstring'''
import gc
import inspect
import unittest
import torch
from parameterized import parameterized
from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .te... | 570 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.uti... | 570 | 1 |
'''simple docstring'''
from math import pi, sqrt
def _A ( UpperCAmelCase ):
'''simple docstring'''
if num <= 0:
raise ValueError('math domain error' )
if num > 1_71.5:
raise OverflowError('math range error' )
elif num - int(UpperCAmelCase... | 531 |
'''simple docstring'''
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version('''>=''', FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from t... | 531 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowerCAmelCase ( __snake_case : int , __snake_case : int ) -> list[list[int]]:
__A : list[list[int]] = []
create_all_state(1 , __snake_case , __snake_case ... | 703 |
'''simple docstring'''
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxMod... | 338 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a : Union[str, Any] = {
'''configuration_instructblip''': [
'''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
''... | 633 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {
'''microsoft/unispeech-sat-base-100h-libri-ft''': (... | 633 | 1 |
def lowerCamelCase_ ( _a : int = 10**12 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = 1
UpperCAmelCase_ : List[Any] = 0
UpperCAmelCase_ : Tuple = 1
UpperCAmelCase_ : Union[str, Any] = 1
w... | 705 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase_ = 50000
UpperCamelCase_ = 5000
UpperCamelCase_ ,UpperCamelCase_ = os.path.split(__file__)
UpperCamelCase_ = os.path.join(RESULTS_BAS... | 322 | 0 |
import re
from ..utils import cached_file
# docstyle-ignore
snake_case__ : Union[str, Any] = '\nHuman: <<task>>\n\nAssistant: '
snake_case__ : str = 'huggingface-tools/default-prompts'
snake_case__ : Optional[int] = {'chat': 'chat_prompt_template.txt', 'run': 'run_prom... | 408 |
"""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"""],
}
... | 77 | 0 |
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, ... | 700 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __magic_name__ :
def __init__( self : Optional[int] ) -> Optional[Any]:
UpperCAmelCase = ""
UpperCAmelCase = ""
UpperCAmelCase = []
UpperCAmel... | 1 | 0 |
def lowerCAmelCase ( UpperCamelCase__ : list ) -> list:
"""simple docstring"""
__SCREAMING_SNAKE_CASE: Tuple = False
while is_sorted is False: # Until all the indices are traversed keep looping
__SCREAMING_SNAKE_CASE: Any = True... | 202 |
import unittest
from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test... | 202 | 1 |
"""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 .formatt... | 327 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from... | 327 | 1 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> str:
"""simple docstring"""
__UpperCAmelCase : list[list[str]] = [[] for _ in range(lowerCamelCase__ )]
__UpperCAmelCase : Dict =... | 168 | '''simple docstring'''
def _lowercase ( lowerCamelCase__ = 1000 ) -> int:
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = 2**power
__UpperCAmelCase : Optional[int] = 0
while n:
__Upper... | 168 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
t... | 708 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase = {
'configuration_mobilenet_v2': [
'MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileNetV2Config',
'MobileNetV2Onnx... | 185 | 0 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class _lowercase ( _A , unittest.Tes... | 385 |
import os
__lowerCamelCase : int = {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 1_00, """D""": 5_00, """M""": 10_00}
def A__ ( _a : str ):
'''simple docstring'''
snake_case__ : int =0
snake_case__ : Any =0
wh... | 385 | 1 |
"""simple docstring"""
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()
SCREAM... | 708 |
"""simple docstring"""
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerT... | 509 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],... | 646 | """simple docstring"""
import shutil
import tempfile
import unittest
from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio
from .test_feature_extraction_clap import floats_list
@require... | 646 | 1 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class _lowerCamelCase ( _lowercase ):
def __lt__(self , __a ) -> Optional[int]:
return self[... | 544 |
"""simple docstring"""
def a__ ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 544 | 1 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
_lowercase = Lock()
def _A (UpperCamelCase : Tuple , UpperCamelCase : List[Any] , UpperCamelCase : List[str] , UpperCamelCase : List[st... | 157 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import... | 157 | 1 |
_snake_case = 8.314_4598
def lowercase_( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K" )
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or... | 714 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | 231 | 0 |
import random
def _UpperCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) ->int:
UpperCAmelCase = a[left_index]
UpperCAmelCase = left_index + 1
for j in range(left_index + 1 , lowerCAmelCase_ ):
if a[j] < pivot:
UpperCAmelCase... | 377 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __low... | 377 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( A_ ):
__a = ["""image_processor""", """tokenizer"""]
__a = """ChineseCLIPImageProcessor"""... | 430 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def _UpperCAmelCase ( __lowerCamelCase : str ) -> None:
_snake_case , _snake_case = analyze_text(__lowerCamelCase )
_snake_case ... | 430 | 1 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transfor... | 457 |
import argparse
import logging
import os
import datasets
import tensorflow as tf
from transformers import AutoTokenizer
UpperCAmelCase : Any = logging.getLogger(__name__)
def __lowerCamelCase ( ):
'''simple docstring'''
lowerCamelCase = argparse.ArgumentParser(
... | 457 | 1 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A : Any = logging.get_logger(__name__)
__A : Dict = {
'hustvl/yolos-small': 'ht... | 709 |
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,
generate_identified_filename,
... | 698 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
A_ : Optional[Any] = "\\n\n"
A_ : int = "\nPerplexity (PPL) is one of the most common m... | 38 |
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create s... | 8 | 0 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase ( lowerCamelCase_ :int , lowerCamelCase_ :int ):
'''simple docstring'''
if b == 0:
return (1, 0)
(snake_case_) : List[str] = extended_euclid(lowerCamelCase_ , a %... | 719 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaP... | 267 | 0 |
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_common import ModelTesterMixin, ids_tenso... | 0 |
'''simple docstring'''
import argparse
import re
from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pyto... | 11 | 0 |
import math
def lowercase__ ( __A: int ):
'''simple docstring'''
__magic_name__ : List[str] = []
__magic_name__ : Optional[Any] = 2
__magic_name__ : List[str] = int(math.sqrt(__A ) ) # Size of eve... | 704 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase : int = {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}
try:
if not is_torch_avail... | 501 | 0 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_a )
class __a( _a ):
"""simple docstring"""
lowerCAmelCase = field(default='''image-classifi... | 30 |
import numpy as np
import datasets
__a = '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. M... | 30 | 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
SCREAMING_SNAKE_CASE_ : List[str] = logging.get_lo... | 500 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICE... | 500 | 1 |
'''simple docstring'''
a__ : int = 8.314_462 # Unit - J mol-1 K-1
def __snake_case ( SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Any ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 o... | 51 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
fr... | 217 | 0 |
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 712 |
'''simple docstring'''
from scipy.stats import pearsonr
import datasets
a : str = """
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies... | 672 | 0 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configur... | 472 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
__snake_case = {
"""cola""": ... | 472 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_tor... | 197 | """simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 197 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class lowercase__ (... | 475 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"facebook/deit-base-distilled-patch16-22... | 475 | 1 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.ut... | 691 |
'''simple docstring'''
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
__snake_case : Union[st... | 691 | 1 |
import random
from .binary_exp_mod import bin_exp_mod
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase=1000 ) -> List[Any]:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this ... | 79 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class _snake_case ( unittest.TestCase ):
"""simple docstring"""
def __SCREAMING_SNAKE_CASE ( self ) ->... | 697 | 0 |
'''simple docstring'''
def lowerCAmelCase__ ( a_ : list ) -> list:
for i in range(len(a_ ) - 1 , 0 , -1 ):
UpperCAmelCase__ : Dict = False
for j in range(a_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
... | 708 |
'''simple docstring'''
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, ... | 599 | 0 |
lowerCAmelCase : Dict = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
def _lowercase ... | 214 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase : int = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( __a ):
'''simple docstring'''
def __init__( self : Tuple , *low... | 214 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class SCREAMING_SNAKE_CASE__ (... | 258 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( __lowercase , __lowercase , __lowercase ... | 258 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
snake_case__ : List[str] = logging.get_logger(__name__)
snake_case__ : Any = ... | 23 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[int]] ):
def update_area_of_max_square(UpperCamelCase : int , UpperCamelCase : int ) -> int:
# BASE CASE
if row >= rows or col >= cols... | 160 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
"""split_dict""" , [
SplitDict(),
SplitDict({"""train""": SplitInfo(name="""train""" , num_bytes=1337 , num_examples=42 , dataset_name="""m... | 715 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _a (datasets.BeamBasedBuilder ):
'''simple docstring'''
def __A ( self ):... | 64 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Tuple = {
'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
'tokenization_luke': ['LukeTokenizer'],
}
try:
if not is_torch_av... | 557 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[int] , _lowerCamelCase : List[A... | 557 | 1 |
a_ : List[Any] = [
[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 _SCREAMING_SNAKE_CASE ( snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case... | 678 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ : Union[str, Any] = {
'configuration_longt5': ['LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LongT5Config', 'LongT5OnnxConfig'],
}
try:
if not is_to... | 678 | 1 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.fu... | 263 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : Tuple):
A_ : str = [0] * len(lowerCamelCase)
A_ : Union[str, Any] = []
A_ : Union[str, Any] = []
A_ : Tuple = 0
for values in graph.values():
f... | 665 | 0 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_... | 700 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
"""weiweishi/roc-bert-base-zh""": """https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json""",
}
class SCRE... | 218 | 0 |
"""simple docstring"""
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def _lowerCAmelCase ( _UpperCamelCase ):
"""simple docstring"""
if "cls_token" in name:
_lowercase: Tuple ... | 353 |
import warnings
warnings.warn(
"""memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: """
"""`from accelerate import find_executable_batch_size` to avoid this warning.""",
FutureWarning,
) | 137 | 0 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__lowercase : str = """"""
__lowercase : Optional[int] = """"""
__lowercase : List[Any] = """"""
__lowercase : Dict = 1 # (0 is vertical, 1 is horizontal)
def lowerCamelCase... | 702 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ... | 66 | 0 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_... | 638 |
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.co... | 638 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A: List[str] = {
"""configuration_luke""": ["""LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LukeConfig"""],
"""tokenization_luke""": ["""LukeTokeni... | 617 |
'''simple docstring'''
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0... | 617 | 1 |
'''simple docstring'''
import time
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers.generation import (
MaxLengthCri... | 107 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
if is_torch_available():
import torch
from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
... | 428 | 0 |
"""simple docstring"""
import itertools
import random
import unittest
import numpy as np
from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extractio... | 711 |
"""simple docstring"""
snake_case__ : List[Any] = '''Tobias Carryer'''
from time import time
class snake_case_:
def __init__( self : Optional[Any] , UpperCamelCase_ : Optional[Any] , UpperCamelCase_ : Tuple , UpperCamelCase_ : Optional[int... | 637 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import... | 153 |
"""simple docstring"""
import enum
import shutil
import sys
A__ , A__ : Optional[Any] = shutil.get_terminal_size()
A__ : List[Any] = {'UP': 'A', 'DOWN': 'B', 'RIGHT': 'C', 'LEFT': 'D'}
class lowercase__ ( enum.Enum ):
_UpperCAmelCase :Optional[A... | 153 | 1 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCamelCase__( snake_case_ ):
def __init__( self , __Up... | 708 |
'''simple docstring'''
from __future__ import annotations
def lowercase__ ( __UpperCamelCase : list[float] ):
'''simple docstring'''
__lowercase = 0.00
__lowercase = 0
for resistor in resistors:
if resistor <= 0:
__lowercase ... | 339 | 0 |
def __lowerCAmelCase ( UpperCamelCase ) -> List[str]:
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : List[str] = []
lowerCAmelCase__ : Dict = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''... | 678 |
lowerCAmelCase_ = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exp... | 678 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization... | 442 |
"""simple docstring"""
import inspect
import unittest
class lowerCamelCase__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_ ( self : int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
... | 442 | 1 |
# 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 be considered
# sin... | 324 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging
__magic_name__: Dict = logging.ge... | 324 | 1 |
class a__ :
def __init__( self ):
"""simple docstring"""
__lowerCAmelCase = ""
__lowerCAmelCase = ""
__lowerCAmelCase = []
def __SCREAMING_SNAKE_CASE( self , _A , _A ):
... | 552 |
import argparse
import logging
import os
import time
import timeit
import datasets
import numpy as np
import pycuda.autoinit # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset,... | 552 | 1 |
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase ( lowercase_ , lowercase_ , lowercase_=None ) -... | 12 |
import inspect
import unittest
from math import floor
from transformers import CvtConfig
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 Co... | 323 | 0 |
"""simple docstring"""
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_availa... | 710 |
"""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 __lowercase ( _UpperCAmelCase):
"""simple docstri... | 48 | 0 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, a... | 45 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
ex... | 112 | 0 |
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
raise ValueError('''check_bouncy() accepts only integer arguments''' )
UpperCAmelCase_ : List[Any] = str(UpperCamelCase__ )... | 720 |
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__ ( _lowercase ):
'''simple docstring'''
... | 300 | 0 |
"""simple docstring"""
import functools
def _lowerCamelCase ( _UpperCamelCase , _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = len(_UpperCamelCase )
__lowerCAmelCase = len(_UpperCamelCase )
@functools.cache
def min_distance(... | 636 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .token... | 636 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def UpperCamelCase ( _A : str , _A : Dict , _A : Tuple )-> Optional[Any]:
"""simple ... | 232 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : int = {
"configuration_electra": ["ELECTRA_PRETRAINED_CONFIG... | 232 | 1 |
"""simple docstring"""
def _SCREAMING_SNAKE_CASE (_UpperCAmelCase : int , _UpperCAmelCase : int ):
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(_UpperCAmelCase , int(b / 2 ) ) * actual_power(_UpperCAmelCase , int(b / 2 ) )
else:
return a * actual_pow... | 4 | from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a : List[Any] = logging.get_logger(__name__)
__a : List[Any] = {
"""MIT/ast-finetuned-audioset-10-10-0.4593""": (
"""https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/c... | 534 | 0 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart im... | 215 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_conf... | 215 | 1 |
"""simple docstring"""
_lowerCAmelCase = {str(digit): digit**5 for digit in range(1_0)}
def lowerCamelCase__ ( _lowerCamelCase ):
'''simple docstring'''
return sum(DIGITS_FIFTH_POWER[digit] for digit in str(_lowerCamelCase ) )
def lowerCamelCase__ ( ... | 259 |
'''simple docstring'''
from __future__ import annotations
import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def _A ( snake_case , snake_case , snake_case , snake_case , ) -> list[float]:
_lowercase , _lowercase : Union[st... | 245 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers im... | 391 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowercase ( A__ , A__ ):
'''simple do... | 391 | 1 |
'''simple docstring'''
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_wa... | 69 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowercase : Union[str, Any] = {"configuration_ibert": ["IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "IBertConfig", "IBertOnnxConfig"]}
try:
if no... | 564 | 0 |
'''simple docstring'''
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 = 'src/transformers'... | 709 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
... | 242 | 0 |
"""simple docstring"""
from __future__ import annotations
import os
from collections.abc import Mapping
snake_case = tuple[int, int]
class UpperCAmelCase :
def __init__( self : List[Any] , __lowerCamelCase : set[int] , __l... | 103 |
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowerCAmelCase_ = {
'''... | 39 | 0 |
"""simple docstring"""
import baseaa
def UpperCAmelCase__ ( lowerCAmelCase__ :str ) -> Union[str, Any]:
'''simple docstring'''
return baseaa.baaencode(string.encode("""utf-8""" ) )
def UpperCAmelCase__ ( lowerCAmelCase__ :bytes ) -> Optional[i... | 710 | """simple docstring"""
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
__lowerCAmelCase : Optional[Any] =(
"""4S 3H 2C 7S 5H""",
"""9D 8H 2C 6S 7H""",
"""2D 6D 9D TH 7D""",
"""TC 8C 2S JH 6C""",
... | 197 | 0 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( UpperCamelCase__ , UpperCamelCase__ ) -> tuple[int, int]:
"""simple docstring"""
if b == 0:
return (1, 0)
((A) , (A)) = extended_euclid(UpperCamelCase__ , ... | 690 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class lowerCamelCase__ :
def __init__( self : Optional[Any] , _lowercase : int=2 , _lowercase : Option... | 690 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configu... | 703 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
loggi... | 119 | 0 |
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