

'''simple docstring'''

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 	lowerCAmelCase  (   _lowercase ):
       lowerCAmelCase_												=			['image_processor', 'tokenizer']
       lowerCAmelCase_												=			'BlipImageProcessor'
       lowerCAmelCase_												=			('BertTokenizer', 'BertTokenizerFast')







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]    ,   __lowercase      :			Dict   ):







            """simple docstring"""







            __lowercase      		=False
            super().__init__(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE   )
            __lowercase      		=self.image_processor







       def __call__(				self      :			List[str]    ,   __lowercase      :			str = None    ,   __lowercase      :			Union[str, Any] = None    ,   __lowercase      :			List[Any] = True    ,   __lowercase      :			Optional[Any] = False    ,   __lowercase      :			Union[str, Any] = None    ,   __lowercase      :			str = None    ,   __lowercase      :			Union[str, Any] = 0    ,   __lowercase      :			List[Any] = None    ,   __lowercase      :			Any = None    ,   __lowercase      :			Tuple = False    ,   __lowercase      :			Dict = False    ,   __lowercase      :			Any = False    ,   __lowercase      :			int = False    ,   __lowercase      :			Optional[Any] = False    ,   __lowercase      :			Any = True    ,   __lowercase      :			Dict = None    ,   **__lowercase      :			Optional[Any]    ,   ):







            """simple docstring"""







            if images is None and text is None:
                 raise ValueError('You have to specify either images or text.'   )

            # Get only text
            if images is None:
                 __lowercase      		=self.tokenizer
                 __lowercase      		=self.tokenizer(
                     text=__SCREAMING_SNAKE_CASE    ,   add_special_tokens=__SCREAMING_SNAKE_CASE    ,   padding=__SCREAMING_SNAKE_CASE    ,   truncation=__SCREAMING_SNAKE_CASE    ,   max_length=__SCREAMING_SNAKE_CASE    ,   stride=__SCREAMING_SNAKE_CASE    ,   pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,   return_attention_mask=__SCREAMING_SNAKE_CASE    ,   return_overflowing_tokens=__SCREAMING_SNAKE_CASE    ,   return_special_tokens_mask=__SCREAMING_SNAKE_CASE    ,   return_offsets_mapping=__SCREAMING_SNAKE_CASE    ,   return_token_type_ids=__SCREAMING_SNAKE_CASE    ,   return_length=__SCREAMING_SNAKE_CASE    ,   verbose=__SCREAMING_SNAKE_CASE    ,   return_tensors=__SCREAMING_SNAKE_CASE    ,   **__SCREAMING_SNAKE_CASE    ,   )
                 return text_encoding

            # add pixel_values
            __lowercase      		=self.image_processor(__SCREAMING_SNAKE_CASE    ,   return_tensors=__SCREAMING_SNAKE_CASE   )

            if text is not None:
                 __lowercase      		=self.tokenizer(
                     text=__SCREAMING_SNAKE_CASE    ,   add_special_tokens=__SCREAMING_SNAKE_CASE    ,   padding=__SCREAMING_SNAKE_CASE    ,   truncation=__SCREAMING_SNAKE_CASE    ,   max_length=__SCREAMING_SNAKE_CASE    ,   stride=__SCREAMING_SNAKE_CASE    ,   pad_to_multiple_of=__SCREAMING_SNAKE_CASE    ,   return_attention_mask=__SCREAMING_SNAKE_CASE    ,   return_overflowing_tokens=__SCREAMING_SNAKE_CASE    ,   return_special_tokens_mask=__SCREAMING_SNAKE_CASE    ,   return_offsets_mapping=__SCREAMING_SNAKE_CASE    ,   return_token_type_ids=__SCREAMING_SNAKE_CASE    ,   return_length=__SCREAMING_SNAKE_CASE    ,   verbose=__SCREAMING_SNAKE_CASE    ,   return_tensors=__SCREAMING_SNAKE_CASE    ,   **__SCREAMING_SNAKE_CASE    ,   )
            else:
                 __lowercase      		=None

            if text_encoding is not None:
                 encoding_image_processor.update(__SCREAMING_SNAKE_CASE   )

            return encoding_image_processor







       def       snake_case (				self      :			int    ,   *__lowercase      :			Tuple    ,   **__lowercase      :			int   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Union[str, Any]    ,   *__lowercase      :			Optional[Any]    ,   **__lowercase      :			Dict   ):







            """simple docstring"""







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







       @property
       def       snake_case (				self      :			List[Any]   ):







            """simple docstring"""







            __lowercase      		=self.tokenizer.model_input_names
            __lowercase      		=self.image_processor.model_input_names
            return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )




"""simple docstring"""




def     __magic_name__		(					UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		UpperCamelCase     :		float							,		)     ->						float:
    a__		=			[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:
        a__		=			1 - (matter_density + radiation_density + dark_energy)

        a__		=			(
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        a__		=			hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
    import doctest

    # run doctest
    doctest.testmod()

    # demo LCDM approximation
    a		:       Any     						=       0.3

    print(
        hubble_parameter(
            hubble_constant=68.3,
            radiation_density=1e-4,
            matter_density=matter_density,
            dark_energy=1 - matter_density,
            redshift=0,
        )
    )


