
from __future__ import annotations

import math




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ):
 if len(lowerCAmelCase__  ) != 2 or len(a[0]  ) != 2 or len(lowerCAmelCase__  ) != 2 or len(b[0]  ) != 2:
  raise Exception('''Matrices are not 2x2'''  )
 __a			:  Optional[Any]		    =  [
     [a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
     [a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
 ]
 return new_matrix




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ):
 return [
     [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]  )  )]
     for row in range(len(lowerCAmelCase__  )  )
 ]




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ):
 return [
     [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]  )  )]
     for row in range(len(lowerCAmelCase__  )  )
 ]




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):
 if len(lowerCAmelCase__  ) % 2 != 0 or len(a[0]  ) % 2 != 0:
  raise Exception('''Odd matrices are not supported!'''  )

 __a			:  List[Any]		    =  len(lowerCAmelCase__  )
 __a			:  str		    =  matrix_length // 2

 __a			:  int		    =  [[a[i][j] for j in range(lowerCAmelCase__  ,       lowerCAmelCase__  )] for i in range(lowerCAmelCase__  )]
 __a			:  Any		    =  [
     [a[i][j] for j in range(lowerCAmelCase__  ,       lowerCAmelCase__  )] for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  )
 ]

 __a			:  Tuple		    =  [[a[i][j] for j in range(lowerCAmelCase__  )] for i in range(lowerCAmelCase__  )]
 __a			:  Optional[Any]		    =  [[a[i][j] for j in range(lowerCAmelCase__  )] for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  )]

 return top_left, top_right, bot_left, bot_right




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):
 return len(lowerCAmelCase__  ), len(matrix[0]  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ):
 print('''\n'''.join(str(lowerCAmelCase__  ) for line in matrix  )  )




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ):
 if matrix_dimensions(lowerCAmelCase__  ) == (2, 2):
  return default_matrix_multiplication(lowerCAmelCase__  ,       lowerCAmelCase__  )

 __a    ,	__a    ,	__a    ,	__a			:  Tuple		    =  split_matrix(lowerCAmelCase__  )
 __a    ,	__a    ,	__a    ,	__a			:  List[Any]		    =  split_matrix(lowerCAmelCase__  )

 __a			:  List[str]		    =  actual_strassen(lowerCAmelCase__  ,       matrix_subtraction(lowerCAmelCase__  ,       lowerCAmelCase__  )  )
 __a			:  Dict		    =  actual_strassen(matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )
 __a			:  Tuple		    =  actual_strassen(matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )
 __a			:  Dict		    =  actual_strassen(lowerCAmelCase__  ,       matrix_subtraction(lowerCAmelCase__  ,       lowerCAmelCase__  )  )
 __a			:  Optional[Any]		    =  actual_strassen(matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  )
 __a			:  List[str]		    =  actual_strassen(matrix_subtraction(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  )
 __a			:  Optional[int]		    =  actual_strassen(matrix_subtraction(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  )

 __a			:  int		    =  matrix_addition(matrix_subtraction(matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )
 __a			:  Optional[Any]		    =  matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  str		    =  matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )
 __a			:  Tuple		    =  matrix_subtraction(matrix_subtraction(matrix_addition(lowerCAmelCase__  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )  ,       lowerCAmelCase__  )

 # construct the new matrix from our 4 quadrants
 __a			:  str		    =  []
 for i in range(len(lowerCAmelCase__  )  ):
  new_matrix.append(top_left[i] + top_right[i]  )
 for i in range(len(lowerCAmelCase__  )  ):
  new_matrix.append(bot_left[i] + bot_right[i]  )
 return new_matrix




def        __UpperCamelCase					(    lowerCAmelCase__							:    list  ,       lowerCAmelCase__							:    list  ):
 if matrix_dimensions(lowerCAmelCase__  )[1] != matrix_dimensions(lowerCAmelCase__  )[0]:
  __a			:  Optional[Any]		    =  (
      '''Unable to multiply these matrices, please check the dimensions.\n'''
      f"Matrix A: {matrixa}\n"
      f"Matrix B: {matrixa}"
  )
  raise Exception(lowerCAmelCase__  )
 __a			:  Tuple		    =  matrix_dimensions(lowerCAmelCase__  )
 __a			:  Union[str, Any]		    =  matrix_dimensions(lowerCAmelCase__  )

 if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
  return [matrixa, matrixa]

 __a			:  Any		    =  max(*lowerCAmelCase__  ,       *lowerCAmelCase__  )
 __a			:  List[Any]		    =  int(math.pow(2  ,       math.ceil(math.loga(lowerCAmelCase__  )  )  )  )
 __a			:  Tuple		    =  matrixa
 __a			:  Any		    =  matrixa

 # Adding zeros to the matrices so that the arrays dimensions are the same and also
 # power of 2
 for i in range(0  ,       lowerCAmelCase__  ):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]  ,       lowerCAmelCase__  ):
    new_matrixa[i].append(0  )
  else:
   new_matrixa.append([0] * maxim  )
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]  ,       lowerCAmelCase__  ):
    new_matrixa[i].append(0  )
  else:
   new_matrixa.append([0] * maxim  )

 __a			:  str		    =  actual_strassen(lowerCAmelCase__  ,       lowerCAmelCase__  )

 # Removing the additional zeros
 for i in range(0  ,       lowerCAmelCase__  ):
  if i < dimensiona[0]:
   for _ in range(dimensiona[1]  ,       lowerCAmelCase__  ):
    final_matrix[i].pop()
  else:
   final_matrix.pop()
 return final_matrix


if __name__ == "__main__":
      lowercase__          =[
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 4, 3, 1],
          [2, 3, 6, 7],
          [3, 1, 2, 4],
          [2, 3, 4, 5],
          [6, 2, 3, 1],
      ]
      lowercase__          =[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
      print(strassen(matrixa, matrixa))


# Copyright 2023 The HuggingFace 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

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__          ={
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =['EfficientNetImageProcessor']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__          =[
          'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
          'EfficientNetForImageClassification',
          'EfficientNetModel',
          'EfficientNetPreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_efficientnet import (
          EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
          EfficientNetConfig,
          EfficientNetOnnxConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_efficientnet import EfficientNetImageProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_efficientnet import (
                EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                EfficientNetForImageClassification,
                EfficientNetModel,
                EfficientNetPreTrainedModel,
            )

else:
      import sys

      lowercase__          =_LazyModule(__name__, globals()['__file__'], _import_structure)
