from __future__ import annotations

import math

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						List[str]:




       '''simple docstring'''






       if len(UpperCamelCase__   ) != 2 or len(a[0]   ) != 2 or len(UpperCamelCase__   ) != 2 or len(b[0]   ) != 2:
              raise Exception('Matrices are not 2x2'   )
       _A= [
           [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_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       return [
           [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
           for row in range(len(UpperCamelCase__   )   )
       ]

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






       return [
           [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]   )   )]
           for row in range(len(UpperCamelCase__   )   )
       ]

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






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

       _A= len(UpperCamelCase__   )
       _A= matrix_length // 2

       _A= [[a[i][j] for j in range(UpperCamelCase__       ,					UpperCamelCase__   )] for i in range(UpperCamelCase__   )]
       _A= [
           [a[i][j] for j in range(UpperCamelCase__       ,					UpperCamelCase__   )] for i in range(UpperCamelCase__       ,					UpperCamelCase__   )
       ]

       _A= [[a[i][j] for j in range(UpperCamelCase__   )] for i in range(UpperCamelCase__   )]
       _A= [[a[i][j] for j in range(UpperCamelCase__   )] for i in range(UpperCamelCase__       ,					UpperCamelCase__   )]

       return top_left, top_right, bot_left, bot_right

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[int]:




       '''simple docstring'''






       return len(UpperCamelCase__   ), len(matrix[0]   )

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						str:




       '''simple docstring'''






       print('\n'.join(str(UpperCamelCase__   ) for line in matrix   )   )

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       if matrix_dimensions(UpperCamelCase__   ) == (2, 2):
              return default_matrix_multiplication(UpperCamelCase__       ,					UpperCamelCase__   )

       _A,			_A,			_A,			_A= split_matrix(UpperCamelCase__   )
       _A,			_A,			_A,			_A= split_matrix(UpperCamelCase__   )

       _A= actual_strassen(UpperCamelCase__       ,					matrix_subtraction(UpperCamelCase__       ,					UpperCamelCase__   )   )
       _A= actual_strassen(matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )
       _A= actual_strassen(matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )
       _A= actual_strassen(UpperCamelCase__       ,					matrix_subtraction(UpperCamelCase__       ,					UpperCamelCase__   )   )
       _A= actual_strassen(matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )       ,					matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )   )
       _A= actual_strassen(matrix_subtraction(UpperCamelCase__       ,					UpperCamelCase__   )       ,					matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )   )
       _A= actual_strassen(matrix_subtraction(UpperCamelCase__       ,					UpperCamelCase__   )       ,					matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )   )

       _A= matrix_addition(matrix_subtraction(matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )
       _A= matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )
       _A= matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )
       _A= matrix_subtraction(matrix_subtraction(matrix_addition(UpperCamelCase__       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )       ,					UpperCamelCase__   )

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

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_   )		->						Dict:




       '''simple docstring'''






       if matrix_dimensions(UpperCamelCase__   )[1] != matrix_dimensions(UpperCamelCase__   )[0]:
              _A= (
                  'Unable to multiply these matrices, please check the dimensions.\n'
                  F"Matrix A: {matrixa}\n"
                  F"Matrix B: {matrixa}"
              )
              raise Exception(UpperCamelCase__   )
       _A= matrix_dimensions(UpperCamelCase__   )
       _A= matrix_dimensions(UpperCamelCase__   )

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

       _A= max(*UpperCamelCase__       ,					*UpperCamelCase__   )
       _A= int(math.pow(2       ,					math.ceil(math.loga(UpperCamelCase__   )   )   )   )
       _A= matrixa
       _A= matrixa

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

       _A= actual_strassen(UpperCamelCase__       ,					UpperCamelCase__   )

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


if __name__ == "__main__":
      UpperCAmelCase_								=							[
          [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],
      ]
      UpperCAmelCase_								=							[[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
      print(strassen(matrixa, matrixa))
import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers.generation import (
          MaxLengthCriteria,
          MaxNewTokensCriteria,
          MaxTimeCriteria,
          StoppingCriteriaList,
          validate_stopping_criteria,
      )





@require_torch
class      lowerCAmelCase						(					unittest.TestCase			):



     def 			a__  (					self				,      lowerCAmelCase__       ):
            _A=	3
            _A=	250

            _A=	ids_tensor((batch_size, length)				,      lowerCAmelCase__       )
            _A=	torch.ones((batch_size, length)				,      device=lowerCAmelCase__				,      dtype=torch.float       ) / length
            return input_ids, scores



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10       ),
                    MaxTimeCriteria(max_time=0.1       ),
                ]       )

            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxLengthCriteria(max_length=10       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )



     def 			a__  (					self       ):
            _A=	MaxNewTokensCriteria(start_length=5				,      max_new_tokens=5       )

            _A,			_A=	self._get_tensors(5       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(9       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A,			_A=	self._get_tensors(10       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	StoppingCriteriaList([criteria]       )
            self.assertEqual(criteria_list.max_length				,      10       )



     def 			a__  (					self       ):
            _A,			_A=	self._get_tensors(5       )

            _A=	MaxTimeCriteria(max_time=0.1       )
            self.assertFalse(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )

            _A=	MaxTimeCriteria(max_time=0.1				,      initial_timestamp=time.time() - 0.2       )
            self.assertTrue(criteria(lowerCAmelCase__				,      lowerCAmelCase__       )       )





     def 			a__  (					self       ):
            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      10       )

            with self.assertWarns(lowerCAmelCase__       ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )				,      11       )

            _A=	validate_stopping_criteria(StoppingCriteriaList()				,      11       )

            self.assertEqual(len(lowerCAmelCase__       )				,      1       )