





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



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

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




              """simple docstring"""


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

              UpperCamelCase_ 							=	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__(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	self.image_processor
              UpperCamelCase_ 							=	False

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




              """simple docstring"""


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

              UpperCamelCase_ 							=	kwargs.pop("images"       , _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_ 							=	kwargs.pop("text"       , _SCREAMING_SNAKE_CASE		)
              if len(_SCREAMING_SNAKE_CASE		) > 0:
                     UpperCamelCase_ 							=	args[0]
                     UpperCamelCase_ 							=	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:
                     UpperCamelCase_ 							=	self.image_processor(_SCREAMING_SNAKE_CASE       , *_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)
              if text is not None:
                     UpperCamelCase_ 							=	self.tokenizer(_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              if text is None:
                     return inputs
              elif images is None:
                     return encodings
              else:
                     UpperCamelCase_ 							=	encodings["input_ids"]
                     return inputs

       def lowercase			(		self:			int       , *_SCREAMING_SNAKE_CASE:			Tuple       , **_SCREAMING_SNAKE_CASE:			Tuple		)							->			List[str]:




              """simple docstring"""


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

       def lowercase			(		self:			List[str]       , *_SCREAMING_SNAKE_CASE:			int       , **_SCREAMING_SNAKE_CASE:			Tuple		)							->			str:




              """simple docstring"""


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

       @contextmanager
       def lowercase			(		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."		)
              UpperCamelCase_ 							=	True
              UpperCamelCase_ 							=	self.tokenizer
              yield
              UpperCamelCase_ 							=	self.image_processor
              UpperCamelCase_ 							=	False

       @property
       def lowercase			(		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."       , _SCREAMING_SNAKE_CASE       , )
              return self.image_processor_class



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




              """simple docstring"""


              warnings.warn(
                  "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."       , _SCREAMING_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()



