

'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class       SCREAMING_SNAKE_CASE__     (							a__ ):
    _A            =				["image_processor", "tokenizer"]
    _A            =				"AutoImageProcessor"
    _A            =				"AutoTokenizer"




    def __init__(   self							,	lowercase__=None							,	lowercase__=None							,	**lowercase__     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."							,	__a							,	)
                SCREAMING_SNAKE_CASE_ :     Dict	=			kwargs.pop("feature_extractor"     )

          SCREAMING_SNAKE_CASE_ :     str	=			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__(__a							,	__a     )
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			self.image_processor
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			False




    def __call__(   self							,	*lowercase__							,	**lowercase__     ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_ :     Any	=			kwargs.pop("images"							,	__a     )
          SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			kwargs.pop("text"							,	__a     )
          if len(__a     ) > 0:
                SCREAMING_SNAKE_CASE_ :     List[str]	=			args[0]
                SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			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:
                SCREAMING_SNAKE_CASE_ :     Dict	=			self.image_processor(__a							,	*__a							,	**__a     )
          if text is not None:
                SCREAMING_SNAKE_CASE_ :     Any	=			self.tokenizer(__a							,	**__a     )

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




    def     __lowerCamelCase (   self							,	*lowercase__							,	**lowercase__     ):






          """simple docstring"""






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




    def     __lowerCamelCase (   self							,	*lowercase__							,	**lowercase__     ):






          """simple docstring"""






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




    @contextmanager
    def     __lowerCamelCase (   self     ):






          """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."     )
          SCREAMING_SNAKE_CASE_ :     List[str]	=			True
          SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			self.tokenizer
          yield
          SCREAMING_SNAKE_CASE_ :     int	=			self.image_processor
          SCREAMING_SNAKE_CASE_ :     Optional[int]	=			False




    def     __lowerCamelCase (   self							,	lowercase__							,	lowercase__=False							,	lowercase__=None     ):






          """simple docstring"""






          if added_vocab is None:
                SCREAMING_SNAKE_CASE_ :     Tuple	=			self.tokenizer.get_added_vocab()

          SCREAMING_SNAKE_CASE_ :     List[str]	=			{}

          while tokens:
                SCREAMING_SNAKE_CASE_ :     Optional[int]	=			re.search(r"<s_(.*?)>"							,	__a							,	re.IGNORECASE     )
                if start_token is None:
                      break
                SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			start_token.group(1     )
                SCREAMING_SNAKE_CASE_ :     Any	=			re.search(rF"</s_{key}>"							,	__a							,	re.IGNORECASE     )
                SCREAMING_SNAKE_CASE_ :     List[str]	=			start_token.group()
                if end_token is None:
                      SCREAMING_SNAKE_CASE_ :     Optional[int]	=			tokens.replace(__a							,	""     )
                else:
                      SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			end_token.group()
                      SCREAMING_SNAKE_CASE_ :     int	=			re.escape(__a     )
                      SCREAMING_SNAKE_CASE_ :     Tuple	=			re.escape(__a     )
                      SCREAMING_SNAKE_CASE_ :     str	=			re.search(F"{start_token_escaped}(.*?){end_token_escaped}"							,	__a							,	re.IGNORECASE     )
                      if content is not None:
                            SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			content.group(1     ).strip()
                            if r"<s_" in content and r"</s_" in content:  # non-leaf node
                                  SCREAMING_SNAKE_CASE_ :     Optional[int]	=			self.tokenajson(__a							,	is_inner_value=__a							,	added_vocab=__a     )
                                  if value:
                                        if len(__a     ) == 1:
                                              SCREAMING_SNAKE_CASE_ :     Union[str, Any]	=			value[0]
                                        SCREAMING_SNAKE_CASE_ :     Any	=			value
                            else:  # leaf nodes
                                  SCREAMING_SNAKE_CASE_ :     str	=			[]
                                  for leaf in content.split(r"<sep/>"     ):
                                        SCREAMING_SNAKE_CASE_ :     Optional[Any]	=			leaf.strip()
                                        if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                                              SCREAMING_SNAKE_CASE_ :     int	=			leaf[1:-2]  # for categorical special tokens
                                        output[key].append(__a     )
                                  if len(output[key]     ) == 1:
                                        SCREAMING_SNAKE_CASE_ :     int	=			output[key][0]

                      SCREAMING_SNAKE_CASE_ :     str	=			tokens[tokens.find(__a     ) + len(__a     ) :].strip()
                      if tokens[:6] == r"<sep/>":  # non-leaf nodes
                            return [output] + self.tokenajson(tokens[6:]							,	is_inner_value=__a							,	added_vocab=__a     )

          if len(__a     ):
                return [output] if is_inner_value else output
          else:
                return [] if is_inner_value else {"text_sequence": tokens}




    @property
    def     __lowerCamelCase (   self     ):






          """simple docstring"""






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







    @property
    def     __lowerCamelCase (   self     ):






          """simple docstring"""






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





def    __lowerCamelCase   (      A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						A__						: float						,						)     ->    float:
   lowerCamelCase_						:   List[str]		= [redshift, radiation_density, matter_density, dark_energy]
   if any(p < 0 for p in parameters					):
      raise ValueError("""All input parameters must be positive"""					)

   if any(p > 1 for p in parameters[1:4]					):
      raise ValueError("""Relative densities cannot be greater than one"""					)
   else:
      lowerCamelCase_						:   Union[str, Any]		= 1 - (matter_density + radiation_density + dark_energy)

      lowerCamelCase_						:   int		= (
          radiation_density * (redshift + 1) ** 4
          + matter_density * (redshift + 1) ** 3
          + curvature * (redshift + 1) ** 2
          + dark_energy
      )

      lowerCamelCase_						:   Dict		= hubble_constant * e_a ** (1 / 2)
      return hubble


if __name__ == "__main__":
      import doctest

      # run doctest
      doctest.testmod()

      # demo LCDM approximation
      snake_case__				:	str					       =					0.3

      print(
          hubble_parameter(
              hubble_constant=6_8.3,
              radiation_density=1e-4,
              matter_density=matter_density,
              dark_energy=1 - matter_density,
              redshift=0,
          )
      )





