


"""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

__snake_case		=       logging.get_logger(__name__)


@add_end_docstrings(a__				)
class     __lowerCamelCase  (					a__				):

     '''simple docstring'''


     def __init__(      self    ,				*__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							str:
          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    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None							)       ->							int:
          _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    ,				__UpperCAmelCase    ,				**__UpperCAmelCase							)       ->							List[Any]:
          return super().__call__(__UpperCAmelCase    ,				**__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							int:
          _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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							Optional[int]:
          # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
          # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Optional[int]:
          _a    	=				[]
          for output_ids in model_outputs:
               _a    	=				{
                   '''generated_text''': self.tokenizer.decode(
                       __UpperCAmelCase    ,				skip_special_tokens=__UpperCAmelCase    ,				)
               }
               records.append(__UpperCAmelCase							)
          return records



"""simple docstring"""






def       A_   (   _lowerCAmelCase     :					list,     _lowerCAmelCase     :					list,     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     _a    	=				len(_lowerCAmelCase       )
     _a    	=				[[0] * n for i in range(_lowerCAmelCase       )]
     for i in range(_lowerCAmelCase       ):
          _a    	=				y_points[i]

     for i in range(2,     _lowerCAmelCase       ):
          for j in range(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				(
                   (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()