


'''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

__UpperCAmelCase    :str				       =     logging.get_logger(__name__)



@add_end_docstrings(_a )
class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	List[str]							,				*snake_case  :	Optional[Any]							,				**snake_case  :	List[str]    )		->							List[Any]:
          super().__init__(*snake_case							,				**snake_case    )
          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 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	List[str]=None							,				snake_case  :	Any=None							,				snake_case  :	Optional[int]=None    )		->							Dict:
          __UpperCAmelCase			:				Optional[int]													=    {}
          __UpperCAmelCase			:				Optional[int]													=    {}

          if prompt is not None:
               __UpperCAmelCase			:				Any													=    prompt

          if generate_kwargs is not None:
               __UpperCAmelCase			:				Optional[Any]													=    generate_kwargs
          if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                    __UpperCAmelCase			:				Union[str, 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'''    )
               __UpperCAmelCase			:				List[Any]													=    max_new_tokens
          return preprocess_params, forward_kwargs, {}

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

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]=None    )		->							str:
          __UpperCAmelCase			:				Optional[int]													=    load_image(snake_case    )

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

               __UpperCAmelCase			:				Tuple													=    self.model.config.model_type

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

               elif model_type == "pix2struct":
                    __UpperCAmelCase			:				int													=    self.image_processor(images=snake_case							,				header_text=snake_case							,				return_tensors=self.framework    )

               elif model_type != "vision-encoder-decoder":
                    # vision-encoder-decoder does not support conditional generation
                    __UpperCAmelCase			:				Tuple													=    self.image_processor(images=snake_case							,				return_tensors=self.framework    )
                    __UpperCAmelCase			:				List[Any]													=    self.tokenizer(snake_case							,				return_tensors=self.framework    )
                    model_inputs.update(snake_case    )

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

          else:
               __UpperCAmelCase			:				Tuple													=    self.image_processor(images=snake_case							,				return_tensors=self.framework    )

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

          return model_inputs

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	List[Any]=None    )		->							Optional[Any]:
          # 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''']							,				snake_case    )
              and all(x is None for x in model_inputs['''input_ids''']    )
          ):
               __UpperCAmelCase			:				int													=    None

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






     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	List[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    []
          for output_ids in model_outputs:
               __UpperCAmelCase			:				Optional[Any]													=    {
                   '''generated_text''': self.tokenizer.decode(
                       snake_case							,				skip_special_tokens=snake_case							,				)
               }
               records.append(snake_case    )
          return records



'''simple docstring'''



from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    int(number**0.5						)
     return number == sq * sq




def 			_a			(		_lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				int													=    x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
     __UpperCAmelCase			:				int													=    x_den * y_den * z_den
     __UpperCAmelCase			:				int													=    gcd(_lowercase    ,      _lowercase						)
     top //= hcf
     bottom //= hcf
     return top, bottom




def 			_a			(		_lowercase      :			int = 35						):





     '''simple docstring'''






     __UpperCAmelCase			:				set													=    set()
     __UpperCAmelCase			:				int
     __UpperCAmelCase			:				Fraction													=    Fraction(0						)
     __UpperCAmelCase			:				tuple[int, int]

     for x_num in range(1    ,      order + 1						):
          for x_den in range(x_num + 1    ,      order + 1						):
               for y_num in range(1    ,      order + 1						):
                    for y_den in range(y_num + 1    ,      order + 1						):
                         # n=1
                         __UpperCAmelCase			:				Optional[int]													=    x_num * y_den + x_den * y_num
                         __UpperCAmelCase			:				Dict													=    x_den * y_den
                         __UpperCAmelCase			:				List[Any]													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Dict													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[int]													=    (
                             x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
                         )
                         __UpperCAmelCase			:				Any													=    x_den * x_den * y_den * y_den
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Tuple													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Union[str, Any]													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				Union[str, Any]													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

                    # n=-1
                         __UpperCAmelCase			:				Union[str, Any]													=    x_num * y_num
                         __UpperCAmelCase			:				List[Any]													=    x_den * y_num + x_num * y_den
                         __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                         z_num //= hcf
                         z_den //= hcf
                         if 0 < z_num < z_den <= order:
                              __UpperCAmelCase			:				Optional[Any]													=    add_three(
                                  _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                              unique_s.add(_lowercase						)

                         # n=2
                         __UpperCAmelCase			:				Optional[Any]													=    x_num * x_num * y_num * y_num
                         __UpperCAmelCase			:				Dict													=    (
                             x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
                         )
                         if is_sq(_lowercase						) and is_sq(_lowercase						):
                              __UpperCAmelCase			:				Any													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				List[Any]													=    int(sqrt(_lowercase						)						)
                              __UpperCAmelCase			:				Any													=    gcd(_lowercase    ,      _lowercase						)
                              z_num //= hcf
                              z_den //= hcf
                              if 0 < z_num < z_den <= order:
                                   __UpperCAmelCase			:				int													=    add_three(
                                       _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase    ,      _lowercase						)
                                   unique_s.add(_lowercase						)

     for num, den in unique_s:
          total += Fraction(_lowercase    ,      _lowercase						)

     return total.denominator + total.numerator


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