

'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
 from PIL import Image






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			'Salesforce/blip-image-captioning-base'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			(
      'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
      'image to caption, and returns a text that contains the description in English.'
  )
  SCREAMING_SNAKE_CASE    :  str							=			'image_captioner'
  SCREAMING_SNAKE_CASE    :  int							=			AutoModelForVisionaSeq

  SCREAMING_SNAKE_CASE    :  str							=			['image']
  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['text']






  def __init__(      self   :     Union[str, Any]					,*lowercase__   :     Dict					,**lowercase__   :     Optional[Any]			):
   requires_backends(self					,['''vision''']			)
   super().__init__(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     "Image"			):
   return self.pre_processor(images=lowercase__					,return_tensors='''pt'''			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Tuple			):
   return self.model.generate(**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   return self.pre_processor.batch_decode(lowercase__					,skip_special_tokens=lowercase__			)[0].strip()




'''simple docstring'''






from __future__ import annotations

from decimal import Decimal

from numpy import array


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = 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							  = 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							  = [[0.0, 0.0], [0.0, 0.0]]
  __lowercase  ,	__lowercase							  = matrix[1][1], matrix[0][0]
  __lowercase  ,	__lowercase							  = -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							  = 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							  = [
      [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							  = (d(matrix[1][1]      ) * d(matrix[2][2]      )) - (
      d(matrix[1][2]      ) * d(matrix[2][1]      )
  )
  __lowercase							  = -(
      (d(matrix[1][0]      ) * d(matrix[2][2]      )) - (d(matrix[1][2]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[1][0]      ) * d(matrix[2][1]      )) - (
      d(matrix[1][1]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][1]      ) * d(matrix[2][2]      )) - (d(matrix[0][2]      ) * d(matrix[2][1]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[2][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[2][0]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[2][1]      )) - (d(matrix[0][1]      ) * d(matrix[2][0]      ))
  )
  __lowercase							  = (d(matrix[0][1]      ) * d(matrix[1][2]      )) - (
      d(matrix[0][2]      ) * d(matrix[1][1]      )
  )
  __lowercase							  = -(
      (d(matrix[0][0]      ) * d(matrix[1][2]      )) - (d(matrix[0][2]      ) * d(matrix[1][0]      ))
  )
  __lowercase							  = (d(matrix[0][0]      ) * d(matrix[1][1]      )) - (
      d(matrix[0][1]      ) * d(matrix[1][0]      )
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __lowercase							  = array(A__      )
  for i in range(3      ):
   for j in range(3      ):
    __lowercase							  = cofactor_matrix[j][i]

        # Inverse of the matrix using the formula (1/determinant) * adjoint matrix
  __lowercase							  = 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.'''      )

