


"""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      		=			logging.get_logger(__name__)
@add_end_docstrings(__UpperCAmelCase     )
class 					__magic_name__		(			__UpperCAmelCase     ):






       def __init__(   self  :    int  ,     *snake_case__  :    Optional[Any]  ,     **snake_case__  :    Dict				):

              '''simple docstring'''






              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  __snake_case						(   self  :    int  ,     snake_case__  :    Optional[Any]=None  ,     snake_case__  :    int=None  ,     snake_case__  :    Union[str, Any]=None				):

              '''simple docstring'''






              lowercase		:Dict       							=				{}
              lowercase		:Tuple       							=				{}

              if prompt is not None:
                     lowercase		:Any       							=				prompt

              if generate_kwargs is not None:
                     lowercase		:Tuple       							=				generate_kwargs
              if max_new_tokens is not None:
                     if "generate_kwargs" not in forward_kwargs:
                            lowercase		:Tuple       							=				{}
                     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		: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__  :    str				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    int  ,     snake_case__  :    Any=None				):

              '''simple docstring'''






              lowercase		:List[str]       							=				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.'''				)

                     lowercase		:int       							=				self.model.config.model_type

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

                     elif model_type == "pix2struct":
                            lowercase		:Optional[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
                            lowercase		:Optional[Any]       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)
                            lowercase		: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:
                     lowercase		:Optional[Any]       							=				self.image_processor(images=snake_case__  ,     return_tensors=self.framework				)

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

              return model_inputs






       def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    str  ,     snake_case__  :    Any=None				):

              '''simple docstring'''






              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''']				)
              ):
                     lowercase		:Optional[int]       							=				None

              if generate_kwargs is None:
                     lowercase		:List[str]       							=				{}
              # 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		:int       							=				model_inputs.pop(self.model.main_input_name				)
              lowercase		:List[Any]       							=				self.model.generate(snake_case__  ,     **snake_case__  ,     **snake_case__				)
              return model_outputs







       def  __snake_case						(   self  :    int  ,     snake_case__  :    str				):

              '''simple docstring'''






              lowercase		:int       							=				[]
              for output_ids in model_outputs:
                     lowercase		:str       							=				{
                         '''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 decimal import Decimal

from numpy import array






def       lowerCamelCase					(a_						:list[list[float]])     ->							list[list[float]]:
       lowercase		:List[str]       							=				Decimal

       # Check if the provided matrix has 2 rows and 2 columns
       # since this implementation only works for 2x2 matrices
       if len(a_) == 2 and len(matrix[0]) == 2 and len(matrix[1]) == 2:
              # Calculate the determinant of the matrix
              lowercase		:List[str]       							=				float(
                  d(matrix[0][0]) * d(matrix[1][1]) - d(matrix[1][0]) * d(matrix[0][1]))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creates a copy of the matrix with swapped positions of the elements
              lowercase		:Optional[Any]       							=				[[0.0, 0.0], [0.0, 0.0]]
              lowercase							,  lowercase		:List[Any]       							=				matrix[1][1], matrix[0][0]
              lowercase							,  lowercase		:Union[str, Any]       							=				-matrix[1][0], -matrix[0][1]

              # Calculate the inverse of the matrix
              return [
                  [(float(d(a_)) / determinant) or 0.0 for n in row] for row in swapped_matrix
              ]
       elif (
           len(a_) == 3
           and len(matrix[0]) == 3
           and len(matrix[1]) == 3
           and len(matrix[2]) == 3
       ):
              # Calculate the determinant of the matrix using Sarrus rule
              lowercase		:Any       							=				float(
                  (
                      (d(matrix[0][0]) * d(matrix[1][1]) * d(matrix[2][2]))
                      + (d(matrix[0][1]) * d(matrix[1][2]) * d(matrix[2][0]))
                      + (d(matrix[0][2]) * d(matrix[1][0]) * d(matrix[2][1]))
                  )
                  - (
                      (d(matrix[0][2]) * d(matrix[1][1]) * d(matrix[2][0]))
                      + (d(matrix[0][1]) * d(matrix[1][0]) * d(matrix[2][2]))
                      + (d(matrix[0][0]) * d(matrix[1][2]) * d(matrix[2][1]))
                  ))
              if determinant == 0:
                     raise ValueError('''This matrix has no inverse.''')

              # Creating cofactor matrix
              lowercase		:Optional[Any]       							=				[
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
                  [d(0.0), d(0.0), d(0.0)],
              ]
              lowercase		:Optional[int]       							=				(d(matrix[1][1]) * d(matrix[2][2])) - (
                  d(matrix[1][2]) * d(matrix[2][1])
              )
              lowercase		:str       							=				-(
                  (d(matrix[1][0]) * d(matrix[2][2])) - (d(matrix[1][2]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[1][0]) * d(matrix[2][1])) - (
                  d(matrix[1][1]) * d(matrix[2][0])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][1]) * d(matrix[2][2])) - (d(matrix[0][2]) * d(matrix[2][1]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][0]) * d(matrix[2][2])) - (
                  d(matrix[0][2]) * d(matrix[2][0])
              )
              lowercase		:List[str]       							=				-(
                  (d(matrix[0][0]) * d(matrix[2][1])) - (d(matrix[0][1]) * d(matrix[2][0]))
              )
              lowercase		:Union[str, Any]       							=				(d(matrix[0][1]) * d(matrix[1][2])) - (
                  d(matrix[0][2]) * d(matrix[1][1])
              )
              lowercase		:Union[str, Any]       							=				-(
                  (d(matrix[0][0]) * d(matrix[1][2])) - (d(matrix[0][2]) * d(matrix[1][0]))
              )
              lowercase		:Any       							=				(d(matrix[0][0]) * d(matrix[1][1])) - (
                  d(matrix[0][1]) * d(matrix[1][0])
              )

              # Transpose the cofactor matrix (Adjoint matrix)
              lowercase		:Union[str, Any]       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            lowercase		:Any       							=				cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
              lowercase		:str       							=				array(a_)
              for i in range(3):
                     for j in range(3):
                            inverse_matrix[i][j] /= d(a_)

        # Calculate the inverse of the matrix
              return [[float(d(a_)) or 0.0 for n in row] for row in inverse_matrix]
       raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''')

