




"""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   =       logging.get_logger(__name__)



@add_end_docstrings(__UpperCAmelCase   )
class      a__				(	__UpperCAmelCase   ):


   def __init__(      self      :    Any      ,  *UpperCamelCase_      :    List[str]      ,  **UpperCamelCase_      :    Any):


      """simple docstring"""




      super().__init__(*_lowerCamelCase      ,  **_lowerCamelCase)
      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       a_     (      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Optional[int]=None      ,  UpperCamelCase_      :    Dict=None      ,  UpperCamelCase_      :    Any=None):


      """simple docstring"""




      __UpperCAmelCase	: List[Any]		=					{}
      __UpperCAmelCase	: str		=					{}

      if prompt is not None:
         __UpperCAmelCase	: int		=					prompt

      if generate_kwargs is not None:
         __UpperCAmelCase	: List[str]		=					generate_kwargs
      if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
            __UpperCAmelCase	: Dict		=					{}
         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")
         __UpperCAmelCase	: Optional[int]		=					max_new_tokens
      return preprocess_params, forward_kwargs, {}


   def __call__(      self      :    Optional[int]      ,  UpperCamelCase_      :    Union[str, List[str], "Image.Image", List["Image.Image"]]      ,  **UpperCamelCase_      :    Dict):


      """simple docstring"""




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


   def       a_     (      self      :    Dict      ,  UpperCamelCase_      :    List[str]      ,  UpperCamelCase_      :    Dict=None):


      """simple docstring"""




      __UpperCAmelCase	: List[Any]		=					load_image(_lowerCamelCase)

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

         __UpperCAmelCase	: List[str]		=					self.model.config.model_type

         if model_type == "git":
            __UpperCAmelCase	: Dict		=					self.image_processor(images=_lowerCamelCase      ,  return_tensors=self.framework)
            __UpperCAmelCase	: List[str]		=					self.tokenizer(text=_lowerCamelCase      ,  add_special_tokens=_lowerCamelCase).input_ids
            __UpperCAmelCase	: List[str]		=					[self.tokenizer.cls_token_id] + input_ids
            __UpperCAmelCase	: List[str]		=					torch.tensor(_lowerCamelCase).unsqueeze(0)
            model_inputs.update({"input_ids": input_ids})

         elif model_type == "pix2struct":
            __UpperCAmelCase	: int		=					self.image_processor(images=_lowerCamelCase      ,  header_text=_lowerCamelCase      ,  return_tensors=self.framework)

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

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

      else:
         __UpperCAmelCase	: int		=					self.image_processor(images=_lowerCamelCase      ,  return_tensors=self.framework)

      if self.model.config.model_type == "git" and prompt is None:
         __UpperCAmelCase	: Union[str, Any]		=					None

      return model_inputs


   def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    Dict=None):


      """simple docstring"""




      if (
          "input_ids" in model_inputs
          and isinstance(model_inputs["input_ids"]      ,  _lowerCamelCase)
          and all(x is None for x in model_inputs["input_ids"])
      ):
         __UpperCAmelCase	: Union[str, Any]		=					None

      if generate_kwargs is None:
         __UpperCAmelCase	: Union[str, Any]		=					{}
      # 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.
      __UpperCAmelCase	: List[Any]		=					model_inputs.pop(self.model.main_input_name)
      __UpperCAmelCase	: Tuple		=					self.model.generate(_lowerCamelCase      ,  **_lowerCamelCase      ,  **_lowerCamelCase)
      return model_outputs


   def       a_     (      self      :    str      ,  UpperCamelCase_      :    Union[str, Any]):


      """simple docstring"""




      __UpperCAmelCase	: int		=					[]
      for output_ids in model_outputs:
         __UpperCAmelCase	: Any		=					{
             "generated_text": self.tokenizer.decode(
                 _lowerCamelCase      ,  skip_special_tokens=_lowerCamelCase      ,  )
         }
         records.append(_lowerCamelCase)
      return records








"""simple docstring"""





def 					_UpperCamelCase    (			UpperCamelCase							)			->			int:


			"""simple docstring"""

			if not grid or not grid[0]:
						raise TypeError("The grid does not contain the appropriate information"							)

			for cell_n in range(1				,       len(grid[0]							)							):
						grid[0][cell_n] += grid[0][cell_n - 1]
			__UpperCAmelCase	: int		=					grid[0]

			for row_n in range(1				,       len(UpperCamelCase							)							):
						__UpperCAmelCase	: int		=					grid[row_n]
						__UpperCAmelCase	: str		=					fill_row(UpperCamelCase				,       UpperCamelCase							)
						__UpperCAmelCase	: List[str]		=					grid[row_n]

			return grid[-1][-1]

def 					_UpperCamelCase    (			UpperCamelCase				,       UpperCamelCase							)			->			list:


			"""simple docstring"""
			current_row[0] += row_above[0]
			for cell_n in range(1				,       len(UpperCamelCase							)							):
						current_row[cell_n] += min(current_row[cell_n - 1]				,       row_above[cell_n]							)

			return current_row


if __name__ == "__main__":
					import doctest

					doctest.testmod()


