






'''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_					= logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase				)
class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 def __init__(      self	:							Any					,      *__lowercase	:							List[Any]					,      **__lowercase	:							List[Any]      )	->						Union[str, Any]:
       super().__init__(*__lowercase					,      **__lowercase      )
       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      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							str=None					,      __lowercase	:							str=None					,      __lowercase	:							Any=None      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				List[Any]									={}
       SCREAMING_SNAKE_CASE__     :				Tuple									={}

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

       if generate_kwargs is not None:
             SCREAMING_SNAKE_CASE__     :				int									=generate_kwargs
       if max_new_tokens is not None:
             if "generate_kwargs" not in forward_kwargs:
                   SCREAMING_SNAKE_CASE__     :				Optional[int]									={}
             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__     :				List[str]									=max_new_tokens
       return preprocess_params, forward_kwargs, {}




 def __call__(      self	:							Any					,      __lowercase	:							Union[str, List[str], "Image.Image", List["Image.Image"]]					,      **__lowercase	:							Optional[int]      )	->						Tuple:
       return super().__call__(__lowercase					,      **__lowercase      )




 def      __magic_name__	(      self	:							int					,      __lowercase	:							str					,      __lowercase	:							str=None      )	->						Optional[Any]:
       SCREAMING_SNAKE_CASE__     :				str									=load_image(__lowercase      )

       if prompt is not None:
             if not isinstance(__lowercase					,      __lowercase      ):
                   raise ValueError(
                       F"Received an invalid text input, got - {type(__lowercase      )} - but expected a single string. "
                       '''Note also that one single text can be provided for conditional image to text generation.'''      )

             SCREAMING_SNAKE_CASE__     :				Tuple									=self.model.config.model_type

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

             elif model_type == "pix2struct":
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.image_processor(images=__lowercase					,      header_text=__lowercase					,      return_tensors=self.framework      )

             elif model_type != "vision-encoder-decoder":
                   # vision-encoder-decoder does not support conditional generation
                   SCREAMING_SNAKE_CASE__     :				List[str]									=self.image_processor(images=__lowercase					,      return_tensors=self.framework      )
                   SCREAMING_SNAKE_CASE__     :				Dict									=self.tokenizer(__lowercase					,      return_tensors=self.framework      )
                   model_inputs.update(__lowercase      )

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

       else:
             SCREAMING_SNAKE_CASE__     :				List[Any]									=self.image_processor(images=__lowercase					,      return_tensors=self.framework      )

       if self.model.config.model_type == "git" and prompt is None:
             SCREAMING_SNAKE_CASE__     :				List[Any]									=None

       return model_inputs




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Any					,      __lowercase	:							Optional[int]=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''']					,      __lowercase      )
           and all(x is None for x in model_inputs['''input_ids''']      )
       ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=None

       if generate_kwargs is None:
             SCREAMING_SNAKE_CASE__     :				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.
       SCREAMING_SNAKE_CASE__     :				int									=model_inputs.pop(self.model.main_input_name      )
       SCREAMING_SNAKE_CASE__     :				int									=self.model.generate(__lowercase					,      **__lowercase					,      **__lowercase      )
       return model_outputs




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Dict      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Any									=[]
       for output_ids in model_outputs:
             SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
                 '''generated_text''': self.tokenizer.decode(
                     __lowercase					,      skip_special_tokens=__lowercase					,      )
             }
             records.append(__lowercase      )
       return records







'''simple docstring'''
from __future__ import annotations

from collections.abc import Callable

a_					= list[list[float | int]]





def  _a(  UpperCamelCase__				:						Matrix,	UpperCamelCase__				:						Matrix							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(size + 1							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				float

      for row in range(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Tuple									=matrix[row][col]

            SCREAMING_SNAKE_CASE__     :				Optional[int]									=vector[row][0]

      SCREAMING_SNAKE_CASE__     :				Any									=0
      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=0
      while row < size and col < size:
            # pivoting
            SCREAMING_SNAKE_CASE__     :				Any									=max((abs(augmented[rowa][col]							), rowa) for rowa in range(UpperCamelCase__,	UpperCamelCase__							)							)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[pivot_row], augmented[row]

            for rowa in range(row + 1,	UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=augmented[rowa][col] / augmented[row][col]
                  SCREAMING_SNAKE_CASE__     :				Tuple									=0
                  for cola in range(col + 1,	size + 1							):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1,	UpperCamelCase__							):
            for row in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[Any]									=augmented[row][col] / augmented[col][col]
                  for cola in range(UpperCamelCase__,	size + 1							):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row],	1_0							)] for row in range(UpperCamelCase__							)
      ]





def  _a(  UpperCamelCase__				:						list[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				int									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0 for _ in range(UpperCamelCase__							)] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix									=[[0] for _ in range(UpperCamelCase__							)]
      SCREAMING_SNAKE_CASE__     :				Matrix
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int
      SCREAMING_SNAKE_CASE__     :				int

      for x_val, y_val in enumerate(UpperCamelCase__							):
            for col in range(UpperCamelCase__							):
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=(x_val + 1) ** (size - col - 1)
            SCREAMING_SNAKE_CASE__     :				Dict									=y_val

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=solve(UpperCamelCase__,	UpperCamelCase__							)

      def interpolated_func(UpperCamelCase__				:						int							) -> int:
            return sum(
                round(coeffs[x_val][0]							) * (var ** (size - x_val - 1))
                for x_val in range(UpperCamelCase__							)							)

      return interpolated_func





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**1_0
      )





def  _a(  UpperCamelCase__				:						Callable[[int], int] = question_function,	UpperCamelCase__				:						int = 1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				list[int]									=[func(UpperCamelCase__							) for x_val in range(1,	order + 1							)]

      SCREAMING_SNAKE_CASE__     :				list[Callable[[int], int]]									=[
          interpolate(data_points[:max_coeff]							) for max_coeff in range(1,	order + 1							)
      ]

      SCREAMING_SNAKE_CASE__     :				int									=0
      SCREAMING_SNAKE_CASE__     :				Callable[[int], int]
      SCREAMING_SNAKE_CASE__     :				int

      for poly in polynomials:
            SCREAMING_SNAKE_CASE__     :				Any									=1
            while func(UpperCamelCase__							) == poly(UpperCamelCase__							):
                  x_val += 1

            ret += poly(UpperCamelCase__							)

      return ret


if __name__ == "__main__":
  print(F'''{solution() = }''')