



"""simple docstring"""







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						Tuple							=  ["""image_processor""", """tokenizer"""]
		lowerCAmelCase_							:						Optional[Any]							=  """AutoImageProcessor"""
		lowerCAmelCase_							:						Dict							=  """AutoTokenizer"""



		def __init__(			self	:     List[str]   ,				snake_case	:     int=None   ,				snake_case	:     int=None   ,				**snake_case	:     Optional[Any]					)      ->    Union[str, Any]:






									'''simple docstring'''

									A    =       None
									if "feature_extractor" in kwargs:
																warnings.warn(
																    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																    ' instead.'   ,				snake_case   ,				)
																A    =       kwargs.pop('feature_extractor'					)

									A    =       image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
																raise ValueError('You need to specify an `image_processor`.'					)
									if tokenizer is None:
																raise ValueError('You need to specify a `tokenizer`.'					)

									super().__init__(snake_case   ,				snake_case					)
									A    =       self.image_processor
									A    =       False



		def __call__(			self	:     Tuple   ,				*snake_case	:     str   ,				**snake_case	:     Dict					)      ->    List[str]:






									'''simple docstring'''

									if self._in_target_context_manager:
																return self.current_processor(*snake_case   ,				**snake_case					)

									A    =       kwargs.pop('images'   ,				snake_case					)
									A    =       kwargs.pop('text'   ,				snake_case					)
									if len(snake_case					) > 0:
																A    =       args[0]
																A    =       args[1:]

									if images is None and text is None:
																raise ValueError('You need to specify either an `images` or `text` input to process.'					)

									if images is not None:
																A    =       self.image_processor(snake_case   ,				*snake_case   ,				**snake_case					)
									if text is not None:
																A    =       self.tokenizer(snake_case   ,				**snake_case					)

									if text is None:
																return inputs
									elif images is None:
																return encodings
									else:
																A    =       encodings['input_ids']
																return inputs



		def        A_		(			self	:     int   ,				*snake_case	:     Tuple   ,				**snake_case	:     Tuple					)      ->    List[str]:






									'''simple docstring'''

									return self.tokenizer.batch_decode(*snake_case   ,				**snake_case					)



		def        A_		(			self	:     List[str]   ,				*snake_case	:     int   ,				**snake_case	:     Tuple					)      ->    str:






									'''simple docstring'''

									return self.tokenizer.decode(*snake_case   ,				**snake_case					)



		@contextmanager
		def        A_		(			self	:     Union[str, Any]					)      ->    Optional[Any]:






									'''simple docstring'''

									warnings.warn(
									    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
									    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
									    'your images inputs, or in a separate call.'					)
									A    =       True
									A    =       self.tokenizer
									yield
									A    =       self.image_processor
									A    =       False



		@property
		def        A_		(			self	:     Union[str, Any]					)      ->    Dict:






									'''simple docstring'''

									warnings.warn(
									    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,				snake_case   ,				)
									return self.image_processor_class



		@property
		def        A_		(			self	:     Union[str, Any]					)      ->    Dict:






									'''simple docstring'''

									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,				snake_case   ,				)
									return self.image_processor








"""simple docstring"""







from collections import namedtuple

A     =      namedtuple('from_to', 'from_ to')

A     =      {
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

A     =      {
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}

def 	lowerCAmelCase__      (  lowerCamelCase__					,   lowerCamelCase__					,   lowerCamelCase__      )			->				float:
							A    =       from_type.lower().rstrip('s'      )
							A    =       TYPE_CONVERSION.get(lowerCamelCase__					,   lowerCamelCase__      )
							A    =       to_type.lower().rstrip('s'      )
							A    =       TYPE_CONVERSION.get(lowerCamelCase__					,   lowerCamelCase__      )
							if new_from not in METRIC_CONVERSION:
														A    =       (
														    f"""Invalid 'from_type' value: {from_type!r}.\n"""
														    f"""Conversion abbreviations are: {', '.join(lowerCamelCase__      )}"""
														)
														raise ValueError(lowerCamelCase__      )
							if new_to not in METRIC_CONVERSION:
														A    =       (
														    f"""Invalid 'to_type' value: {to_type!r}.\n"""
														    f"""Conversion abbreviations are: {', '.join(lowerCamelCase__      )}"""
														)
														raise ValueError(lowerCamelCase__      )
							return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()



