


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

SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)






@add_end_docstrings(_a  )
class _lowerCamelCase(		_a  ):
       def __init__(   self, *lowerCamelCase, **lowerCamelCase)			->  Optional[int]:




           """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      UpperCamelCase	(   self, lowerCamelCase=None, lowerCamelCase=None, lowerCamelCase=None)			->  Dict:




           """simple docstring"""

           _lowercase			:				Any 						=						{}
           _lowercase			:				Union[str, Any] 						=						{}

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

           if generate_kwargs is not None:
               _lowercase			:				Dict 						=						generate_kwargs
           if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                   _lowercase			:				Optional[Any] 						=						{}
               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')
               _lowercase			:				Union[str, Any] 						=						max_new_tokens
           return preprocess_params, forward_kwargs, {}
       def __call__(   self, lowerCamelCase, **lowerCamelCase)			->  Optional[int]:




           """simple docstring"""

           return super().__call__(lowerCamelCase, **lowerCamelCase)
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				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.')

               _lowercase			:				Optional[int] 						=						self.model.config.model_type

               if model_type == "git":
                   _lowercase			:				int 						=						self.image_processor(images=lowerCamelCase, return_tensors=self.framework)
                   _lowercase			:				List[str] 						=						self.tokenizer(text=lowerCamelCase, add_special_tokens=lowerCamelCase).input_ids
                   _lowercase			:				List[str] 						=						[self.tokenizer.cls_token_id] + input_ids
                   _lowercase			:				Optional[Any] 						=						torch.tensor(lowerCamelCase).unsqueeze(0)
                   model_inputs.update({'input_ids': input_ids})

               elif model_type == "pix2struct":
                   _lowercase			:				Optional[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
                   _lowercase			:				Tuple 						=						self.image_processor(images=lowerCamelCase, return_tensors=self.framework)
                   _lowercase			:				List[str] 						=						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:
               _lowercase			:				str 						=						self.image_processor(images=lowerCamelCase, return_tensors=self.framework)

           if self.model.config.model_type == "git" and prompt is None:
               _lowercase			:				Optional[int] 						=						None

           return model_inputs
       def      UpperCamelCase	(   self, lowerCamelCase, lowerCamelCase=None)			->  Optional[int]:




           """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'])
           ):
               _lowercase			:				Optional[Any] 						=						None

           if generate_kwargs is None:
               _lowercase			:				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.
           _lowercase			:				Tuple 						=						model_inputs.pop(self.model.main_input_name)
           _lowercase			:				Optional[int] 						=						self.model.generate(lowerCamelCase, **lowerCamelCase, **lowerCamelCase)
           return model_outputs




       def      UpperCamelCase	(   self, lowerCamelCase)			->  int:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						[]
           for output_ids in model_outputs:
               _lowercase			:				Tuple 						=						{
                   'generated_text': self.tokenizer.decode(
                       lowerCamelCase, skip_special_tokens=lowerCamelCase, )
               }
               records.append(lowerCamelCase)
           return records









'''simple docstring'''




from __future__ import annotations






def 			_lowerCAmelCase     (    __snake_case    :					list[int]					,				__snake_case    :					list[int]					,				__snake_case    :					int     )	->	tuple[float, list[float]]:
     __A		:					int						=			list(range(len(__snake_case     )     )     )
     __A		:					Optional[Any]						=			[v / w for v, w in zip(__snake_case					,				__snake_case     )]
     index.sort(key=lambda __snake_case     : ratio[i]					,				reverse=__snake_case     )

     __A		:					float						=			0
     __A		:					list[float]						=			[0] * len(__snake_case     )
     for i in index:
          if weight[i] <= capacity:
               __A		:					Optional[int]						=			1
               max_value += value[i]
               capacity -= weight[i]
          else:
               __A		:					List[Any]						=			capacity / weight[i]
               max_value += value[i] * capacity / weight[i]
               break

     return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()