
"""simple docstring"""




from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

a		:       Optional[Any]     						=       logging.get_logger(__name__)







@add_end_docstrings(_lowercase       )
class     lowercase(_lowercase       ):



     def __init__(							self     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         super().__init__(*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )
         requires_backends(self     ,				'vision'   )
         self.check_model_type(
             TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None   )		->     Tuple:







         """simple docstring"""



         a__		=			{}
         a__		=			{}

         if prompt is not None:
             a__		=			prompt

         if generate_kwargs is not None:
             a__		=			generate_kwargs
         if max_new_tokens is not None:
             if "generate_kwargs" not in forward_kwargs:
                 a__		=			{}
             if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                 raise ValueError(
                     '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
                     ' please use only one'   )
             a__		=			max_new_tokens
         return preprocess_params, forward_kwargs, {}



     def __call__(							self     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         return super().__call__(__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     Any:







         """simple docstring"""



         a__		=			load_image(__SCREAMING_SNAKE_CASE   )

         if prompt is not None:
             if not isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ):
                 raise ValueError(
                     f'Received an invalid text input, got - {type(__SCREAMING_SNAKE_CASE   )} - but expected a single string. '
                     'Note also that one single text can be provided for conditional image to text generation.'   )

             a__		=			self.model.config.model_type

             if model_type == "git":
                 a__		=			self.image_processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )
                 a__		=			self.tokenizer(text=__SCREAMING_SNAKE_CASE     ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ).input_ids
                 a__		=			[self.tokenizer.cls_token_id] + input_ids
                 a__		=			torch.tensor(__SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
                 model_inputs.update({'input_ids': input_ids}   )

             elif model_type == "pix2struct":
                 a__		=			self.image_processor(images=__SCREAMING_SNAKE_CASE     ,				header_text=__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )

             elif model_type != "vision-encoder-decoder":
                 # vision-encoder-decoder does not support conditional generation
                 a__		=			self.image_processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )
                 a__		=			self.tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )
                 model_inputs.update(__SCREAMING_SNAKE_CASE   )

             else:
                 raise ValueError(f'Model type {model_type} does not support conditional text generation'   )

         else:
             a__		=			self.image_processor(images=__SCREAMING_SNAKE_CASE     ,				return_tensors=self.framework   )

         if self.model.config.model_type == "git" and prompt is None:
             a__		=			None

         return model_inputs



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     str:







         """simple docstring"""



         if (
             "input_ids" in model_inputs
             and isinstance(model_inputs['input_ids']     ,				__SCREAMING_SNAKE_CASE   )
             and all(x is None for x in model_inputs['input_ids']   )
         ):
             a__		=			None

         if generate_kwargs is None:
             a__		=			{}
         # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
         #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
         #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
         #  in the `_prepare_model_inputs` method.
         a__		=			model_inputs.pop(self.model.main_input_name   )
         a__		=			self.model.generate(__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )
         return model_outputs




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			[]
         for output_ids in model_outputs:
             a__		=			{
                 'generated_text': self.tokenizer.decode(
                     __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				)
             }
             records.append(__SCREAMING_SNAKE_CASE   )
         return records




"""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,
        )
    )


