




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

_lowerCamelCase     :		List[str]								=       logging.get_logger(__name__)


@add_end_docstrings(UpperCAmelCase__					)
class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    def __init__(      self				:     Any ,			*UpperCAmelCase__				:     Dict ,			**UpperCAmelCase__				:     Dict)						->Tuple:





           '''simple docstring'''

           super().__init__(*UpperCAmelCase__ ,			**UpperCAmelCase__)
           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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     List[str]=None ,			UpperCAmelCase__				:     str=None)						->int:





           '''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				:     int ,			UpperCAmelCase__				:     Union[str, List[str], "Image.Image", List["Image.Image"]] ,			**UpperCAmelCase__				:     Tuple)						->int:





           '''simple docstring'''

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


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     Optional[Any] ,			UpperCAmelCase__				:     List[str]=None)						->List[str]:





           '''simple docstring'''

           A__							=   load_image(UpperCAmelCase__)

           if prompt is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise ValueError(
                             f"""Received an invalid text input, got - {type(UpperCAmelCase__)} - 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=UpperCAmelCase__ ,			return_tensors=self.framework)
                         A__							=   self.tokenizer(text=UpperCAmelCase__ ,			add_special_tokens=UpperCAmelCase__).input_ids
                         A__							=   [self.tokenizer.cls_token_id] + input_ids
                         A__							=   torch.tensor(UpperCAmelCase__).unsqueeze(0)
                         model_inputs.update({'''input_ids''': input_ids})

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

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

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

           else:
                  A__							=   self.image_processor(images=UpperCAmelCase__ ,			return_tensors=self.framework)

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

           return model_inputs


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     Dict=None)						->Optional[int]:





           '''simple docstring'''

           if (
               "input_ids" in model_inputs
               and isinstance(model_inputs['''input_ids'''] ,			UpperCAmelCase__)
               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(UpperCAmelCase__ ,			**UpperCAmelCase__ ,			**UpperCAmelCase__)
           return model_outputs





    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     Optional[Any])						->Tuple:





           '''simple docstring'''

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








def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       return " ".join(
           ''''''.join(word[::-1]						) if len(lowercase_						) > 4 else word for word in sentence.split()						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(reverse_long_words("""Hey wollef sroirraw"""))


