

"""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(UpperCamelCase_)
class      __a							(UpperCamelCase_):






     '''simple docstring'''

     def __init__(						self	,    *_a	,    **_a  )  ->      List[Any]:


           """simple docstring"""


           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 					_a			(						self	,    _a=None	,    _a=None	,    _a=None  )  ->      List[str]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      List[str]      		=	{}
           SCREAMING_SNAKE_CASE__   :      int      		=	{}

           if prompt is not None:
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	prompt

           if generate_kwargs is not None:
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	generate_kwargs
           if max_new_tokens is not None:
                 if "generate_kwargs" not in forward_kwargs:
                       SCREAMING_SNAKE_CASE__   :      Tuple      		=	{}
                 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__   :      Any      		=	max_new_tokens
           return preprocess_params, forward_kwargs, {}

     def __call__(						self	,    _a	,    **_a  )  ->      Union[str, Any]:


           """simple docstring"""


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

     def 					_a			(						self	,    _a	,    _a=None  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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__   :      Optional[int]      		=	self.model.config.model_type

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

                 elif model_type == "pix2struct":
                       SCREAMING_SNAKE_CASE__   :      int      		=	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__   :      Optional[Any]      		=	self.image_processor(images=_a	,    return_tensors=self.framework  )
                       SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	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__   :      int      		=	self.image_processor(images=_a	,    return_tensors=self.framework  )

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

           return model_inputs

     def 					_a			(						self	,    _a	,    _a=None  )  ->      List[str]:


           """simple docstring"""


           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__   :      Union[str, Any]      		=	None

           if generate_kwargs is None:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	{}
           # 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__   :      Dict      		=	model_inputs.pop(self.model.main_input_name  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	self.model.generate(_a	,    **_a	,    **_a  )
           return model_outputs





     def 					_a			(						self	,    _a  )  ->      Optional[int]:


           """simple docstring"""


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







"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




