


'''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

lowerCamelCase				:  Optional[Any]    		=      logging.get_logger(__name__)
@add_end_docstrings(lowercase_	)
class     __lowerCAmelCase		(lowercase_	):





      '''simple docstring'''






      def __init__(self       :       Optional[int]  ,		*UpperCamelCase       :       int  ,		**UpperCamelCase       :       int					):

             '''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  UpperCamelCase__						(self       :       str  ,		UpperCamelCase       :       Tuple=None  ,		UpperCamelCase       :       List[Any]=None  ,		UpperCamelCase       :       List[str]=None					):

             '''simple docstring'''







             lowercase__      =					{}
             lowercase__      =					{}

             if prompt is not None:
                    lowercase__      =					prompt

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



      def __call__(self       :       List[Any]  ,		UpperCamelCase       :       Union[str, List[str], "Image.Image", List["Image.Image"]]  ,		**UpperCamelCase       :       List[str]					):

             '''simple docstring'''







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



      def  UpperCamelCase__						(self       :       Dict  ,		UpperCamelCase       :       Dict  ,		UpperCamelCase       :       List[Any]=None					):

             '''simple docstring'''







             lowercase__      =					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.'''					)

                    lowercase__      =					self.model.config.model_type

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

                    elif model_type == "pix2struct":
                           lowercase__      =					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
                           lowercase__      =					self.image_processor(images=UpperCamelCase  ,		return_tensors=self.framework					)
                           lowercase__      =					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:
                    lowercase__      =					self.image_processor(images=UpperCamelCase  ,		return_tensors=self.framework					)

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

             return model_inputs



      def  UpperCamelCase__						(self       :       Union[str, Any]  ,		UpperCamelCase       :       Optional[int]  ,		UpperCamelCase       :       Optional[int]=None					):

             '''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''']					)
             ):
                    lowercase__      =					None

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






      def  UpperCamelCase__						(self       :       Optional[int]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







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








'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




