code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 85 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@re... | 121 | 0 |
__lowerCamelCase : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def SCREAMING_SNAKE_CASE ( snake_case_ : bytes ):
# Make sure the supplied data is a bytes-like object
if not isinstance(snake_case_ , snake_case_ ... | 286 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"""an... | 286 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase = logging.get_logger(__name__)
__Up... | 69 | """simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
... | 69 | 1 |
from __future__ import annotations
def A__ ( __lowerCamelCase, __lowerCamelCase = None, __lowerCamelCase = None ):
if start is None:
SCREAMING_SNAKE_CASE_ = 0
if end is None:
SCREAMING_SNAKE_CASE_ = len(a__ ) - 1
if start >= end:
return
SCRE... | 360 |
from __future__ import annotations
import time
from collections.abc import Sequence
from random import randint
from matplotlib import pyplot as plt
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
if not arr:
return None, None, 0
if low == high:
return low, ... | 257 | 0 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
lowerCamelCase ... | 199 |
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ... | 199 | 1 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCamelCase_ )
class _snake_case ( UpperCamelCase_ ):
_lowercase : str = field... | 362 |
from scipy.stats import pearsonr
import datasets
a_ : Optional[int] = '\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption t... | 327 | 0 |
'''simple docstring'''
def snake_case_ ( __SCREAMING_SNAKE_CASE : int = 1000 ):
"""simple docstring"""
lowercase_ : Optional[int] = 2**power
lowercase_ : Any = str(__SCREAMING_SNAKE_CASE )
l... | 93 |
'''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, AgentT... | 93 | 1 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''microsoft/unispeech-large-1500h-cv''': (
'''https://huggingface.co/mi... | 175 |
"""simple docstring"""
from typing import Any, Dict, List, Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_vision_available():
from PIL import Image
from ..image_utils import l... | 175 | 1 |
'''simple docstring'''
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 lowerCamelCas... | 31 |
import math
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> int:
if not isinstance(lowercase ,lowercase ):
snake_case : List[Any] = f"""Input value of [number={number}] must be an integer"""
raise TypeError(lowercase )
if number < 1:
snake... | 124 | 0 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils impor... | 346 |
'''simple docstring'''
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmel... | 346 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
A__ : Union[str, Any] ='''naver-clova-ix/donut-base'''
class UpperCAmelCase ( unittest.TestCase ):
def lowercase__ ( self : List[Any] ) -> int:
... | 70 |
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTok... | 70 | 1 |
'''simple docstring'''
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easie... | 229 |
'''simple docstring'''
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase = """src/transformers"""
# This is to make... | 229 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''facebook/convnextv2-tiny-1k-224''': '''... | 5 |
import heapq as hq
import math
from collections.abc import Iterator
class lowerCamelCase__ :
def __init__(self , UpperCAmelCase ) -> Any:
_lowercase =str(id_ )
_lowercase =None
_lowercase =None
_lowercase ... | 5 | 1 |
"""simple docstring"""
from itertools import permutations
def lowercase (SCREAMING_SNAKE_CASE_ : tuple ) -> bool:
if num[3] % 2 != 0:
return False
if (num[2] + num[3] + num[4]) % 3 != 0:
return False
if num[5] % 5 != 0:
... | 38 |
"""simple docstring"""
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils i... | 38 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
A_ = {
'''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],... | 64 | def __SCREAMING_SNAKE_CASE ( __UpperCamelCase : int , __UpperCamelCase : int ) -> str:
"""simple docstring"""
if a < 0 or b < 0:
raise ValueError("""the value of both inputs must be positive""" )
SCREAMING_SNAKE_CASE__ = str(bin... | 219 | 0 |
from datetime import datetime as dt
import os
from github import Github
lowercase : int = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def UpperCAmelCase_ ():
__UpperCamelCase : Tuple ... | 171 |
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
lowercase : Any = logging.get_logger(__name__)
lowercase : Dict = {"vocab_file... | 171 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : Optional[Any] = {
'Intel/dpt-large': 'https://huggingface.... | 3 |
'''simple docstring'''
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_ten... | 349 | 0 |
import random
def UpperCAmelCase_( a__ , a__ , a__ = False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : dict = {i: [] for i in range(a__ )}
# if probability is greater or equal than 1, then generate a complete graph
if prob... | 362 |
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterM... | 19 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import Batc... | 152 |
'''simple docstring'''
import requests
a_ = 'YOUR API KEY'
def _a( UpperCamelCase__ : str, UpperCamelCase__ : str = giphy_api_key ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Any ='''+'''.join(query.split() ... | 152 | 1 |
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def lowerCamelCase__ ( UpperCamelCas... | 350 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class UpperCamelCase_ ( _lowerCamelCase , _lowe... | 295 | 0 |
"""simple docstring"""
from importlib import import_module
from .logging import get_logger
_a = get_logger(__name__)
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self : Optional[int], UpperCAmelCase__ : Tuple, UpperCAmelCase__ ... | 17 |
"""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 |
"""simple docstring"""
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
__magic_name__ : int = False
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase )... | 357 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__magic_name__ = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embed... | 255 | 0 |
from torch import nn
class __magic_name__ ( nn.Module ):
"""simple docstring"""
def __init__( self :Any , snake_case :Dict , snake_case :Union[str, Any] ):
'''simple docstring'''
super().__init__()
A_ : List[Any... | 300 | import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def __lowercase ( lowerCamelCase : str , lowerCamelCase : str , **lowerCamelCase : List[Any] ):
UpperCamelCase_ : Union[str, Any] = AutoConfig.from_pretrained(lowerCamelCase ... | 175 | 0 |
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():
import jax.numpy as jnp
f... | 295 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class UpperCamelCase_ ( enum.Enum ):
... | 295 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {
"configuration_informer": [
"INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"I... | 45 |
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 __magic_name__ ( datasets.BeamBasedBuilder):
def UpperCAmelCase__ ( self : Tuple ) -... | 146 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration... | 187 |
'''simple docstring'''
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
... | 187 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class A__ ( metaclass=A__ ):
A__ = ['torch', 'scipy']
def __init__( self : Optional[Any] , *_a : Any , **_a : Tuple ) -> Optional[Any]:
'''simple docstring... | 47 |
'''simple docstring'''
from __future__ import annotations
import math
def _lowerCAmelCase ( _UpperCamelCase : int ) -> bool:
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or num... | 47 | 1 |
"""simple docstring"""
import os
def __A ( a_ :Tuple) -> List[str]:
__a : Optional[int] = len(grid[0])
__a : Dict = len(a_)
__a : Any = 0
__a : Optional[int] = 0
__a : Union[str, Any] ... | 188 |
"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A = logging.get_... | 188 | 1 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.t... | 111 |
from statistics import mean
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> list:
__snake_case: List[Any] = 0
# Number of processes finished
__snake_case: Unio... | 111 | 1 |
'''simple docstring'''
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
... | 357 |
'''simple docstring'''
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER,... | 190 | 0 |
'''simple docstring'''
import re
import string
import numpy as np
import datasets
_A : Union[str, Any] ='''
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
'''
_A... | 41 | """simple docstring"""
def __a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Union[str, Any]:
if height >= 1:
move_tower(height - 1 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
move_dis... | 290 | 0 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class A( UpperCamelCase ... | 208 |
from ..utils import is_flax_available, is_torch_available
if is_torch_available():
from .autoencoder_kl import AutoencoderKL
from .controlnet import ControlNetModel
from .dual_transformer_ad import DualTransformeraDModel
from .modeling_utils import ModelMixin
from .prior_transformer import Prio... | 208 | 1 |
"""simple docstring"""
from __future__ import annotations
import math
def lowerCAmelCase_ ( snake_case_ : list , snake_case_ : list ) ->list:
if len(snake_case_ ) != 2 or len(a[0] ) != 2 or len(snake_case_ ) != 2 or len(b[0] ) != 2:
raise Exception('Mat... | 126 |
"""simple docstring"""
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=1_3_3_7 ,... | 126 | 1 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
a__: Union[str, Any] = get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( enum.Enum ):
__SCREAMING_SNAKE_CASE = '''all_ch... | 39 |
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.'
)
| 39 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Optional[Any] =logging.get_logger(__name__)
__lowerCAmelCase : Any ={
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc... | 197 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : List[Any] =logging.get_logger(__name__)
__lowerCAmelCase : Union[str, Any] ={
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-J... | 197 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Dict = {
"""google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",... | 366 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface... | 24 | 0 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
A : str = logging.get_logger(__... | 274 |
def __lowerCamelCase ( __a :int = 1_0_0_0_0_0_0 ) -> int:
"""simple docstring"""
A__ = limit + 1
A__ = [0] * limit
for first_term in range(1 , __a ):
for n in range(__a , __a , __a ):
A__ = firs... | 274 | 1 |
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def __A ( __lowerCamelCase ) ->... | 347 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
__UpperCamelCase : Any = datasets.ut... | 347 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, ... | 318 |
'''simple docstring'''
import numpy
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is t... | 318 | 1 |
'''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/LICENSE-2.... | 170 |
'''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,
... | 170 | 1 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTo... | 243 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase ) ->str:
"""simple docstring"""
return " ".join(
"".join(word[::-1] ) if len(UpperCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
pr... | 243 | 1 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case__(_UpperCamelCase ):
"""simple docstring""... | 121 |
from collections import Counter
from timeit import timeit
def __lowerCamelCase ( lowerCamelCase__ = "" , ):
"""simple docstring"""
return sum(c % 2 for c in Counter(input_str.replace(" " , "" ).lower() ).values() ) < 2
def __lowerCamelCase ( lowerCamelCase__ =... | 121 | 1 |
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 __lowerCamelCase (_a ):
_lowe... | 310 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class __lowerCamelCase (_a ):
_lowercase = """M-CLIP"""
def __init__( self: int,A_: Any=1024,A_: Union[str, Any]=768,**A_: str ):
... | 310 | 1 |
from __future__ import annotations
class a :
def __init__( self , A_ = 0 ):
'''simple docstring'''
_UpperCAmelCase : Tuple = key
def _UpperCAmelCase ( self , A_ , A_ ):
'''simple docstring'''
as... | 189 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class a ( UpperCAmelCase ):
_lowercase = (PNDMScheduler,)
_lowercase = (("num_inference_steps", 5_0),)
def _UpperCAmelCase ( self , **A_... | 189 | 1 |
import fire
from utils import calculate_rouge, save_json
def lowerCAmelCase_ ( __a , __a , __a=None , **__a ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase__: Any =[x.strip() for x in open(__a ).readlines()]
lowerCamelCase__: Dict =[x.... | 10 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ : Dict = logging.get_logger(__name__)
UpperCAmelCase__ : Union[str, Any] = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/... | 121 | 0 |
import socket
def lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = socket.socket(socket.AF_INET ,socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE : Any = socket.gethostname()
SCREAMING_SNAKE_CASE : List[str]... | 358 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterator
class _a :
'''simple docstring'''
def __init__( self, A ):
'''simple docstring'''
... | 246 | 0 |
'''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
A =logging.get_logger(__name__)
A ={
'facebook/levit-128S': 'ht... | 34 |
"""simple docstring"""
import numpy as np
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = 1E-12 , lowerCAmelCase = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowerCAmelCase )[0] == np.shape(lowerCAmelCase )[1]
# Ensure prope... | 171 | 0 |
"""simple docstring"""
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import Mode... | 360 |
"""simple docstring"""
def _snake_case ( _snake_case : int = 50000000 ):
lowerCAmelCase : List[str] = set()
lowerCAmelCase : List[Any] = int((limit - 24) ** (1 / 2) )
lowerCAmelCase : Optional[int] = set(range(3 , prime_square_limit + 1 , ... | 314 | 0 |
"""simple docstring"""
def __a ( __lowerCamelCase, __lowerCamelCase ):
UpperCAmelCase_ : str = ""
for word_or_phrase in separated:
if not isinstance(__lowerCamelCase, __lowerCamelCase ):
raise Exception("join() accepts only strings to be joined" )
joined += ... | 61 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFe... | 273 | 0 |
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = "M-CLIP"
def __init__( self , _a=1_0_2_4 , ... | 15 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( __lowercase ... | 15 | 1 |
'''simple docstring'''
from ... import PretrainedConfig
__SCREAMING_SNAKE_CASE : str = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: ... | 31 |
'''simple docstring'''
import json
from typing import TYPE_CHECKING, 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 loggin... | 4 | 0 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import Realm... | 358 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForS... | 135 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase : Optional[int] ={
"""configuration_whisper""": ["""WHISPER_PR... | 128 |
import unittest
from transformers import BertGenerationConfig, 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 ModelTeste... | 128 | 1 |
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 import Dataset
from transf... | 223 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCamelCase ( _A ):
'''simple docstring'''
__UpperCamelCase : Union[str, Any] = ["""image_processor""", """tokenizer"""]
__UpperCamelCase : List[Any] = """AutoImageProcesso... | 223 | 1 |
"""simple docstring"""
def _lowerCAmelCase ( UpperCamelCase_ ):
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
raise TypeError("""'str' obj... | 100 |
'''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.f... | 47 | 0 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProce... | 370 |
"""simple docstring"""
def snake_case ( A__ = 10_00 ):
UpperCAmelCase_ : Optional[Any] = 2**power
UpperCAmelCase_ : Optional[int] = str(A__ )
UpperCAmelCase_ : Tuple = list(A__ )
UpperCAmelCase_ : Any = 0
... | 253 | 0 |
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
def a_ ( __snake_case : int , __snake_case : int ) -> bool:
"""simple docstring"""
return (
num != den and num % 10 == den // 10 and (n... | 75 |
'''simple docstring'''
import unittest
from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelin... | 75 | 1 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax... | 368 | """simple docstring"""
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf... | 30 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin,... | 174 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from... | 174 | 1 |
def _lowerCAmelCase ( A__: Union[str, Any]=2_8123 ):
'''simple docstring'''
UpperCAmelCase = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for k in range(i + 1 , limit // i + 1 ):
... | 152 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def _lowerCAmelCase ( A__: str , A__: List[str] , A__: str ):
'''simple docstring''... | 152 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slo... | 289 | """simple docstring"""
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, U... | 289 | 1 |
import fire
from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :List[str] = AutoConfig.from_pretrained(__lowerCAm... | 362 | import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase ( ):
'''simple docstring'''
__UpperCamelCase :int = ArgumentParser(
description=(
'''Py... | 105 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__a: Union[str, Any] = {
'''configuration_upernet''': ['''UperNetConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
e... | 198 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_devi... | 233 | 0 |
"""simple docstring"""
import warnings
from contextlib import contextmanager
from ....processing_utils import ProcessorMixin
class __snake_case ( _lowercase):
snake_case__ : Optional[int] = "MCTCTFeatureExtractor"
snake_case__ : Any = "AutoTokenizer"
... | 175 |
"""simple docstring"""
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase__ = [
'''word_e... | 175 | 1 |
from ..utils import DummyObject, requires_backends
class A( metaclass=UpperCamelCase ):
'''simple docstring'''
UpperCamelCase = ['''transformers''', '''torch''', '''note_seq''']
def __init__( self : int , *A_ : Union[str, Any] ... | 204 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import Conf... | 204 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import SequenceFeatu... | 362 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ ( metaclass=__a ):
lowerCAmelCase__ = ['torch', 'transformers', 'onnx']
def __init__( self : int , *_A : Tuple , **_A : ... | 299 | 0 |
'''simple docstring'''
import argparse
import shlex
import runhouse as rh
if __name__ == "__main__":
# Refer to https://runhouse-docs.readthedocs-hosted.com/en/latest/api/python/cluster.html#hardware-setup for cloud access
# setup instructions, if using on-demand hardware
# If user passe... | 63 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase_ = {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', ... | 8 | 0 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase ( _lowerCamelCase : dict , _lowerCamelCase : str , _lowerCamelCase : set , _lowerCamelCase : set , _lowerCamelCase : dict , _lowerCamelCase : dict ... | 363 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int = 1_00_00_00 ):
A__ = set(range(3 , _lowerCamelCase , 2 ) )
primes.add(2 )
for p in range(3 , _lowerCamelCase , 2 ):
if p not in primes:
continue
primes.difference_... | 123 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Union[str, Any] = logging.get_logger(__name__)
A : int = {
'tiiuae/falcon-40b': 'https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json',
'tiiuae/falcon-7b': 'https://huggingface.co/tiiuae/falcon-7b... | 6 | """simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__A = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_to... | 135 | 0 |
def lowerCAmelCase__ ( _a : int , _a : int , _a : list[list[int]] ):
def update_area_of_max_square(_a : int , _a : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
snake_case_ : str = update_... | 36 |
import numpy as np
def lowerCAmelCase__ ( _a : np.array ):
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = ['''image_processor''', '''tokenizer''']
snake_... | 90 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokeni... | 138 | 0 |
"""simple docstring"""
import math
def lowerCamelCase__ ( a , a ) -> float:
if initial_intensity < 0:
raise ValueError('''The value of intensity cannot be negative''' )
# handling of negative values of initial intensity
if angle < 0 or angle > 3_60:
raise Val... | 350 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE__ )
class UpperCAmelCase ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
... | 301 | 0 |
def lowerCamelCase__ ( snake_case_ : Any ) -> Tuple:
__snake_case = [0] * len(snake_case_ )
__snake_case = []
__snake_case = []
__snake_case = 0
for values in graph.values():
for i in value... | 24 |
"""simple docstring"""
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
a : List[str] = logging.get_logger(__name__)
a : List[Any] ... | 105 | 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_accelerate_ava... | 114 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_att... | 114 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}
class UpperCAmelCase ( __SCREAMING_... | 140 |
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE :List[Any] = logging.get_logger(__name__)
SCREAMING_SN... | 15 | 0 |
from collections.abc import Sequence
def __lowerCAmelCase ( a__ , a__ = False ) -> float:
if not arr:
return 0
__a = 0 if allow_empty_subarrays else float('''-inf''' )
__a = 0.0
for num in arr:
__a = max(0 if allow_empt... | 33 |
import sys
def __lowerCAmelCase ( a__ ) -> Optional[int]:
__a = len(a__ )
__a = [[0 for x in range(a__ )] for x in range(a__ )]
__a = [[0 for x in range(a__ )] for x in range(a__ )]
for chain_length in range(2 , a__ ):
for... | 33 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase : Dict = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CON... | 52 |
from __future__ import annotations
a : str = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
class _a :
def __init__(self, SCREAMING_SNAKE... | 147 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase : Any = ['''image_processor''', '''tokenizer''']
UpperCAm... | 271 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
a = HUGGINGFACE_HUB_CACHE
a = '''config.json'''
a = '''diffusion_pytorch_model.bin'''
a = '''diffusion_flax_model.msgpack'''
a = '''mode... | 271 | 1 |
def _UpperCamelCase ( lowercase__ ):
assert isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ), F'''The input value of [n={number}] is not an integer'''
if number == 1:
return 2
elif number < 1:
__SCREAMING_SNAKE_CASE : ... | 9 |
from __future__ import annotations
from typing import Any
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] ):
create_state_space_tree(SCREAMING_SNAKE_CASE__ , [] , 0 )
def _A ( SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING_SNAKE_CASE__ : list[Any] , SCREAMING... | 259 | 0 |
"""simple docstring"""
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transfo... | 350 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
return [list(range(1_0_0_0 - i , -1_0_0_0 - i , -1 ) ) for i in range(1_0_0_0 )]
UpperCAmelCase =generate_large_matrix()
UpperCAmelCase =(
[[4, 3, 2, -1], [3, ... | 77 | 0 |
"""simple docstring"""
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling... | 246 |
"""simple docstring"""
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def UpperCamelCase ( _lowerCAmelCase : List[str] ) -> Any:
_UpperCAmelCase : List[str] = [
"""encoder.version""",
... | 246 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"xlm-mlm-... | 356 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase_ = {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}
... | 11 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffu... | 204 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils i... | 113 | 0 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteSched... | 329 |
import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch
import multiprocess
import numpy as np
import pytest
from datasets.utils.py_utils import (
NestedDataStructure,
asdict,
iflatmap_unordered,
map... | 329 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpo... | 60 |
def a_ ( lowerCAmelCase_ : int ):
if p < 2:
raise ValueError('p should not be less than 2!' )
elif p == 2:
return True
__lowerCAmelCase = 4
__lowerCAmelCase = (1 << p) - 1
for _ in range(p - 2 ):
__lowerCAmelCase = ((s * s) - 2) % m
return s =... | 284 | 0 |
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
'kwargs, expected' , [
({'num_shards': 0, 'max_num_jobs': 1}, []),
({'num_shards': 10, 'max_num_jobs': 1}, [range(10 )]),
(... | 366 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
lowercase_ = (3, 9, -11, 0, 7, 5, 1, -1)
lowercase_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class A :
"""simple docstring"""
lowerCamelCas... | 282 | 0 |
"""simple docstring"""
from ... import PretrainedConfig
_a = {
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class _lowerCAmelCase ( lowercase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = N... | 17 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__UpperCAmelCase = get_tests_... | 119 | 0 |
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."
)
| 293 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : int = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-... | 293 | 1 |
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def __lowerCAmelCase (_UpperCamelCase , _UpperCamelCase=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
__lowerCAmelCase : Tuple = n - 1
__lowerCAmelCase ... | 86 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers impor... | 86 | 1 |
from abc import ABC, abstractmethod
from typing import Optional, Union
from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class lowerCamelCase__( __lowerCamelCase):
def __init__( self: Opti... | 29 |
from typing import Union
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto... | 29 | 1 |
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
UpperCAmelCase__ = '''2.13.1'''
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse('''3.7'''):
raise I... | 5 |
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 __a ( __UpperCamelCase ... | 196 | 0 |
"""simple docstring"""
from argparse import ArgumentParser, Namespace
from typing import Any, List, Optional
from ..pipelines import Pipeline, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from fastapi import Body, FastAPI, HTTPExcept... | 38 |
"""simple docstring"""
from math import sqrt
def lowercase (SCREAMING_SNAKE_CASE_ : int ) -> int:
SCREAMING_SNAKE_CASE = 0
for i in range(1 , int(sqrt(SCREAMING_SNAKE_CASE_ ) + 1 ) ):
if n % i == 0 and i != sqrt(SCREAMING_SNAKE... | 38 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''shi-labs/nat-mini-in1k-224''': '''https://hugg... | 157 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimen... | 195 | 0 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from acceler... | 276 |
import math
def __lowerCamelCase ( ) -> None:
"""simple docstring"""
A__ = input("""Enter message: """ )
A__ = int(input(F'Enter key [2-{len(__a ) - 1}]: ' ) )
A__ = input("""Encryption/Decryption [e/d]: """ ... | 276 | 1 |
'''simple docstring'''
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'pipelines_utils',
'0.22.0',
'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers... | 346 |
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class ... | 78 | 0 |
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@r... | 365 |
import json
import os
import unittest
from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
VOCAB_FILES_NAMES,
BlenderbotSmallTokenizer,
)
from ...test_tokenization_common import TokenizerTesterMixin
class __magic_name__ ( lowerCAmelCase_ , u... | 308 | 0 |
"""simple docstring"""
import os
import sys
import unittest
__A = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_... | 293 |
"""simple docstring"""
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->... | 293 | 1 |
from datetime import datetime as dt
import os
from github import Github
a_ = [
'good first issue',
'good second issue',
'good difficult issue',
'feature request',
'new model',
'wip',
]
def __lowercase ( ):
UpperCamelCase_ : Tuple = Github(os.environ['GITHUB_... | 369 | 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
from transformers.models.big_bird.modeling_fl... | 50 | 0 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
... | 33 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : Union[str, Any] = {
'''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/ma... | 33 | 1 |
"""simple docstring"""
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
_a = logging.get_logger(__name__)
_a = OrderedDict(
[
# B... | 367 |
"""simple docstring"""
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = (PNDMScheduler,)
SCREAMING_SNAKE_CASE__ : str ... | 23 | 0 |
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