






from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
  from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Tuple	=   c_float


 @property
 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     return sizeof(self.value)



def        _lowerCamelCase		(  __A      :							int						,						__A      :							int						,						__A      :							int = 0	) ->   int:
    # < 0 is possible if using a dynamic axis
    if dimension <= 0:
        _UpperCAmelCase			: int     					=			fixed_dimension

    dimension -= num_token_to_add
    return dimension

def        _lowerCamelCase		(  __A      :							int						,						__A      :							ParameterFormat	) ->   int:
    return num_parameters * dtype.size


def        _lowerCamelCase		(  __A      :							str	) ->   Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
    from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

    try:
        return AutoProcessor.from_pretrained(__A	)
    except (ValueError, OSError, KeyError):
        _UpperCAmelCase					,	_UpperCAmelCase			: Any     					=			None, None
        try:
            _UpperCAmelCase			: int     					=			AutoTokenizer.from_pretrained(__A	)
        except (OSError, KeyError):
            pass
        try:
            _UpperCAmelCase			: int     					=			AutoFeatureExtractor.from_pretrained(__A	)
        except (OSError, KeyError):
            pass

        if tokenizer is not None and feature_extractor is not None:
            raise ValueError(
                f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''	)
        elif tokenizer is None and feature_extractor is None:
            return None
        elif tokenizer is not None:
            return tokenizer
        else:
            return feature_extractor












from unittest.mock import patch

import datasets
from datasets import Dataset

def        _lowerCamelCase		(  ) ->   Tuple:
    _UpperCAmelCase			: Union[str, Any]     					=			Dataset.from_dict({'''col_1''': [3, 2, 0, 1]}	)

    with patch('''tqdm.auto.tqdm'''	) as mock_tqdm:
        datasets.disable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_not_called()

        mock_tqdm.reset_mock()

        datasets.enable_progress_bar()
        dset.map(lambda __A	: {"col_2": x["col_1"] + 1}	)
        mock_tqdm.assert_called()




