


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

from decimal import Decimal

from numpy import array


def 			_lowercase       (				UpperCamelCase__       :					list[list[float]]			):
 __A  :		List[Any]       =			Decimal

 # Check if the provided matrix has 2 rows and 2 columns
 # since this implementation only works for 2x2 matrices
 if len(UpperCamelCase__			) == 2 and len(matrix[0]			) == 2 and len(matrix[1]			) == 2:
  # Calculate the determinant of the matrix
  __A  :		int       =			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
  __A  :		List[str]       =			[[0.0, 0.0], [0.0, 0.0]]
  __A     ,__A  :		int       =			matrix[1][1], matrix[0][0]
  __A     ,__A  :		Union[str, Any]       =			-matrix[1][0], -matrix[0][1]

  # Calculate the inverse of the matrix
  return [
      [(float(d(UpperCamelCase__			)			) / determinant) or 0.0 for n in row] for row in swapped_matrix
  ]
 elif (
     len(UpperCamelCase__			) == 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
  __A  :		List[str]       =			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
  __A  :		List[str]       =			[
      [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			)],
  ]
  __A  :		int       =			(d(matrix[1][1]			) * d(matrix[2][2]			)) - (
      d(matrix[1][2]			) * d(matrix[2][1]			)
  )
  __A  :		List[Any]       =			-(
      (d(matrix[1][0]			) * d(matrix[2][2]			)) - (d(matrix[1][2]			) * d(matrix[2][0]			))
  )
  __A  :		int       =			(d(matrix[1][0]			) * d(matrix[2][1]			)) - (
      d(matrix[1][1]			) * d(matrix[2][0]			)
  )
  __A  :		List[Any]       =			-(
      (d(matrix[0][1]			) * d(matrix[2][2]			)) - (d(matrix[0][2]			) * d(matrix[2][1]			))
  )
  __A  :		Tuple       =			(d(matrix[0][0]			) * d(matrix[2][2]			)) - (
      d(matrix[0][2]			) * d(matrix[2][0]			)
  )
  __A  :		List[Any]       =			-(
      (d(matrix[0][0]			) * d(matrix[2][1]			)) - (d(matrix[0][1]			) * d(matrix[2][0]			))
  )
  __A  :		str       =			(d(matrix[0][1]			) * d(matrix[1][2]			)) - (
      d(matrix[0][2]			) * d(matrix[1][1]			)
  )
  __A  :		List[str]       =			-(
      (d(matrix[0][0]			) * d(matrix[1][2]			)) - (d(matrix[0][2]			) * d(matrix[1][0]			))
  )
  __A  :		Optional[int]       =			(d(matrix[0][0]			) * d(matrix[1][1]			)) - (
      d(matrix[0][1]			) * d(matrix[1][0]			)
  )

  # Transpose the cofactor matrix (Adjoint matrix)
  __A  :		Any       =			array(UpperCamelCase__			)
  for i in range(3			):
   for j in range(3			):
    __A  :		List[str]       =			cofactor_matrix[j][i]

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

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










'''simple docstring'''
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			tempfile.mkdtemp()

  __A  :		List[Any]       =			BlipImageProcessor()
  __A  :		List[str]       =			BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel'						)

  __A  :		Union[str, Any]       =			BlipProcessor(__lowercase      ,      __lowercase						)

  processor.save_pretrained(self.tmpdirname						)


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).tokenizer


 def  snake_case__   (      self      ,      **__lowercase						):

  """simple docstring"""







  return AutoProcessor.from_pretrained(self.tmpdirname      ,      **__lowercase						).image_processor


 def  snake_case__   (      self						):

  """simple docstring"""







  shutil.rmtree(self.tmpdirname						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			[np.random.randint(255      ,      size=(3, 30, 400)      ,      dtype=np.uinta						)]

  __A  :		Union[str, Any]       =			[Image.fromarray(np.moveaxis(__lowercase      ,      0      ,      -1						)						) for x in image_inputs]

  return image_inputs


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			BlipProcessor(tokenizer=self.get_tokenizer()      ,      image_processor=self.get_image_processor()						)
  processor.save_pretrained(self.tmpdirname						)

  __A  :		Optional[int]       =			self.get_tokenizer(bos_token='(BOS)'      ,      eos_token='(EOS)'						)
  __A  :		Optional[Any]       =			self.get_image_processor(do_normalize=__lowercase      ,      padding_value=1.0						)

  __A  :		List[str]       =			BlipProcessor.from_pretrained(
      self.tmpdirname      ,      bos_token='(BOS)'      ,      eos_token='(EOS)'      ,      do_normalize=__lowercase      ,      padding_value=1.0						)

  self.assertEqual(processor.tokenizer.get_vocab()      ,      tokenizer_add_kwargs.get_vocab()						)
  self.assertIsInstance(processor.tokenizer      ,      __lowercase						)

  self.assertEqual(processor.image_processor.to_json_string()      ,      image_processor_add_kwargs.to_json_string()						)
  self.assertIsInstance(processor.image_processor      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.get_image_processor()
  __A  :		Union[str, Any]       =			self.get_tokenizer()

  __A  :		Dict       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Dict       =			self.prepare_image_inputs()

  __A  :		str       =			image_processor(__lowercase      ,      return_tensors='np'						)
  __A  :		List[Any]       =			processor(images=__lowercase      ,      return_tensors='np'						)

  for key in input_feat_extract.keys():
   self.assertAlmostEqual(input_feat_extract[key].sum()      ,      input_processor[key].sum()      ,      delta=1E-2						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		List[str]       =			self.get_tokenizer()

  __A  :		str       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'

  __A  :		Any       =			processor(text=__lowercase						)

  __A  :		int       =			tokenizer(__lowercase      ,      return_token_type_ids=__lowercase						)

  for key in encoded_tok.keys():
   self.assertListEqual(encoded_tok[key]      ,      encoded_processor[key]						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.get_image_processor()
  __A  :		Dict       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		List[Any]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		Optional[Any]       =			processor(text=__lowercase      ,      images=__lowercase						)

  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)

  # test if it raises when no input is passed
  with pytest.raises(__lowercase						):
   processor()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.get_image_processor()
  __A  :		Optional[int]       =			self.get_tokenizer()

  __A  :		List[str]       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		str       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

  __A  :		Tuple       =			processor.batch_decode(__lowercase						)
  __A  :		Tuple       =			tokenizer.batch_decode(__lowercase						)

  self.assertListEqual(__lowercase      ,      __lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.get_image_processor()
  __A  :		Any       =			self.get_tokenizer()

  __A  :		Any       =			BlipProcessor(tokenizer=__lowercase      ,      image_processor=__lowercase						)

  __A  :		Optional[int]       =			'lower newer'
  __A  :		List[str]       =			self.prepare_image_inputs()

  __A  :		int       =			processor(text=__lowercase      ,      images=__lowercase						)

  # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
  self.assertListEqual(list(inputs.keys()						)      ,      ['pixel_values', 'input_ids', 'attention_mask']						)






