

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

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



@add_end_docstrings(UpperCamelCase_       )
class       __snake_case  (    UpperCamelCase_       ):

  def __init__(					self						,		*_A						,		**_A):
      super().__init__(*_a						,		**_a)
      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 						lowerCAmelCase__     (					self						,		_A=None						,		_A=None						,		_A=None):
      SCREAMING_SNAKE_CASE_									=						{}
      SCREAMING_SNAKE_CASE_									=						{}

      if prompt is not None:
          SCREAMING_SNAKE_CASE_									=						prompt

      if generate_kwargs is not None:
          SCREAMING_SNAKE_CASE_									=						generate_kwargs
      if max_new_tokens is not None:
          if "generate_kwargs" not in forward_kwargs:
              SCREAMING_SNAKE_CASE_									=						{}
          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')
          SCREAMING_SNAKE_CASE_									=						max_new_tokens
      return preprocess_params, forward_kwargs, {}

  def __call__(					self						,		_A						,		**_A):
      return super().__call__(_a						,		**_a)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=None):
      SCREAMING_SNAKE_CASE_									=						load_image(_a)

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

          SCREAMING_SNAKE_CASE_									=						self.model.config.model_type

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

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

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

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

      else:
          SCREAMING_SNAKE_CASE_									=						self.image_processor(images=_a						,		return_tensors=self.framework)

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

      return model_inputs

  def 						lowerCAmelCase__     (					self						,		_A						,		_A=None):


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

      if generate_kwargs is None:
          SCREAMING_SNAKE_CASE_									=						{}
      # 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.
      SCREAMING_SNAKE_CASE_									=						model_inputs.pop(self.model.main_input_name)
      SCREAMING_SNAKE_CASE_									=						self.model.generate(_a						,		**_a						,		**_a)
      return model_outputs


  def 						lowerCAmelCase__     (					self						,		_A):
      SCREAMING_SNAKE_CASE_									=						[]
      for output_ids in model_outputs:
          SCREAMING_SNAKE_CASE_									=						{
              """generated_text""": self.tokenizer.decode(
                  _a						,		skip_special_tokens=_a						,		)
          }
          records.append(_a)
      return records








def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		list ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    if index == number_of_items:
        return 0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						0
    SCREAMING_SNAKE_CASE_									=						knapsack(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   index + 1       )
    if weights[index] <= max_weight:
        SCREAMING_SNAKE_CASE_									=						values[index] + knapsack(
            _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   max_weight - weights[index] ,   index + 1       )
    return max(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()


