


from __future__ import annotations

import math


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if len(SCREAMING_SNAKE_CASE       ) != 2 or len(a[0]       ) != 2 or len(SCREAMING_SNAKE_CASE       ) != 2 or len(b[0]       ) != 2:
    raise Exception('''Matrices are not 2x2'''       )
  A_      :				int		     =					[
      [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   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return [
      [matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]       )       )]
      for row in range(len(SCREAMING_SNAKE_CASE       )       )
  ]


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  return [
      [matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]       )       )]
      for row in range(len(SCREAMING_SNAKE_CASE       )       )
  ]


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

  A_      :				Tuple		     =					len(SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					matrix_length // 2

  A_      :				List[str]		     =					[[a[i][j] for j in range(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )] for i in range(SCREAMING_SNAKE_CASE       )]
  A_      :				int		     =					[
      [a[i][j] for j in range(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )] for i in range(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  ]

  A_      :				Any		     =					[[a[i][j] for j in range(SCREAMING_SNAKE_CASE       )] for i in range(SCREAMING_SNAKE_CASE       )]
  A_      :				Tuple		     =					[[a[i][j] for j in range(SCREAMING_SNAKE_CASE       )] for i in range(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )]

  return top_left, top_right, bot_left, bot_right


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  return len(SCREAMING_SNAKE_CASE       ), len(matrix[0]       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE       ):
  print('''\n'''.join(str(SCREAMING_SNAKE_CASE       ) for line in matrix       )       )


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if matrix_dimensions(SCREAMING_SNAKE_CASE       ) == (2, 2):
    return default_matrix_multiplication(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  A_     ,       A_     ,       A_     ,       A_      :				Dict		     =					split_matrix(SCREAMING_SNAKE_CASE       )
  A_     ,       A_     ,       A_     ,       A_      :				str		     =					split_matrix(SCREAMING_SNAKE_CASE       )

  A_      :				int		     =					actual_strassen(SCREAMING_SNAKE_CASE ,			matrix_subtraction(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )
  A_      :				int		     =					actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )
  A_      :				Optional[Any]		     =					actual_strassen(SCREAMING_SNAKE_CASE ,			matrix_subtraction(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )
  A_      :				Dict		     =					actual_strassen(matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )
  A_      :				Dict		     =					actual_strassen(matrix_subtraction(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )
  A_      :				Tuple		     =					actual_strassen(matrix_subtraction(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )       )

  A_      :				List[Any]		     =					matrix_addition(matrix_subtraction(matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )
  A_      :				Optional[Any]		     =					matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )
  A_      :				Tuple		     =					matrix_subtraction(matrix_subtraction(matrix_addition(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       ) ,			SCREAMING_SNAKE_CASE       )

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


def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
  if matrix_dimensions(SCREAMING_SNAKE_CASE       )[1] != matrix_dimensions(SCREAMING_SNAKE_CASE       )[0]:
    A_      :				Tuple		     =					(
        '''Unable to multiply these matrices, please check the dimensions.\n'''
        f'''Matrix A: {matrixa}\n'''
        f'''Matrix B: {matrixa}'''
    )
    raise Exception(SCREAMING_SNAKE_CASE       )
  A_      :				str		     =					matrix_dimensions(SCREAMING_SNAKE_CASE       )
  A_      :				List[str]		     =					matrix_dimensions(SCREAMING_SNAKE_CASE       )

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

  A_      :				Optional[Any]		     =					max(*SCREAMING_SNAKE_CASE ,			*SCREAMING_SNAKE_CASE       )
  A_      :				List[str]		     =					int(math.pow(2 ,			math.ceil(math.loga(SCREAMING_SNAKE_CASE       )       )       )       )
  A_      :				str		     =					matrixa
  A_      :				Dict		     =					matrixa

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

  A_      :				Dict		     =					actual_strassen(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       )

  # Removing the additional zeros
  for i in range(0 ,			SCREAMING_SNAKE_CASE       ):
    if i < dimensiona[0]:
      for _ in range(dimensiona[1] ,			SCREAMING_SNAKE_CASE       ):
        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 copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test






class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @parameterized.expand([(None,), ('''foo.json''',)]		)
       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Tuple:




         '''simple docstring'''





         A_      :				Dict		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)
           A_      :				Tuple		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       config_name=_SCREAMING_SNAKE_CASE		)

         # Checks parameters that were specified
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.temperature				,       0.7		)
         self.assertEqual(loaded_config.length_penalty				,       1.0		)
         self.assertEqual(loaded_config.bad_words_ids				,       [[1, 2, 3], [4, 5]]		)

         # Checks parameters that were not specified (defaults)
         self.assertEqual(loaded_config.top_k				,       50		)
         self.assertEqual(loaded_config.max_length				,       20		)
         self.assertEqual(loaded_config.max_time				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Dict:




         '''simple docstring'''





         A_      :				str		     =					AutoConfig.from_pretrained('''gpt2'''		)
         A_      :				int		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					GenerationConfig()

         # The generation config has loaded a few non-default parameters from the model config
         self.assertNotEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # One of those parameters is eos_token_id -- check if it matches
         self.assertNotEqual(generation_config_from_model.eos_token_id				,       default_generation_config.eos_token_id		)
         self.assertEqual(generation_config_from_model.eos_token_id				,       model_config.eos_token_id		)



       def        _snake_case       (      self		)->Optional[int]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig()
         A_      :				Optional[Any]		     =					{
             '''max_new_tokens''': 1024,
             '''foo''': '''bar''',
         }
         A_      :				Dict		     =					copy.deepcopy(_SCREAMING_SNAKE_CASE		)
         A_      :				Optional[int]		     =					generation_config.update(**_SCREAMING_SNAKE_CASE		)

         # update_kwargs was not modified (no side effects)
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(generation_config.max_new_tokens				,       1024		)

         # `.update()` returns a dictionary of unused kwargs
         self.assertEqual(_SCREAMING_SNAKE_CASE				,       {'''foo''': '''bar'''}		)



       def        _snake_case       (      self		)->str:




         '''simple docstring'''





         A_      :				List[str]		     =					GenerationConfig()
         A_      :				int		     =					'''bar'''

         with tempfile.TemporaryDirectory('''test-generation-config'''		) as tmp_dir:
           generation_config.save_pretrained(_SCREAMING_SNAKE_CASE		)

           A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE		)
         # update_kwargs was used to update the config on valid attributes
         self.assertEqual(new_config.foo				,       '''bar'''		)

         A_      :				Optional[int]		     =					GenerationConfig.from_model_config(_SCREAMING_SNAKE_CASE		)
         assert not hasattr(_SCREAMING_SNAKE_CASE				,       '''foo'''		)  # no new kwargs should be initialized if from config







       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					GenerationConfig()
         self.assertEqual(default_config.temperature				,       1.0		)
         self.assertEqual(default_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(default_config.num_beams				,       1		)

         A_      :				str		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       bad_words_ids=[[1, 2, 3], [4, 5]]				,       )
         self.assertEqual(config.temperature				,       0.7		)
         self.assertEqual(config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(config.num_beams				,       1		)

         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(_SCREAMING_SNAKE_CASE		)
           A_      :				Dict		     =					GenerationConfig.from_pretrained(_SCREAMING_SNAKE_CASE				,       temperature=1.0		)

         self.assertEqual(loaded_config.temperature				,       1.0		)
         self.assertEqual(loaded_config.do_sample				,       _SCREAMING_SNAKE_CASE		)
         self.assertEqual(loaded_config.num_beams				,       1		)  # default value






@is_staging_test
class   _lowerCamelCase (					unittest.TestCase			):




       """simple docstring"""



       @classmethod
       def        _snake_case       (      cls		)->Optional[int]:




         '''simple docstring'''





         A_      :				str		     =					TOKEN
         HfFolder.save_token(_SCREAMING_SNAKE_CASE		)



       @classmethod
       def        _snake_case       (      cls		)->List[Any]:




         '''simple docstring'''





         try:
           delete_repo(token=cls._token				,       repo_id='''test-generation-config'''		)
         except HTTPError:
           pass

         try:
           delete_repo(token=cls._token				,       repo_id='''valid_org/test-generation-config-org'''		)
         except HTTPError:
           pass



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''test-generation-config'''				,       use_auth_token=self._token		)

         A_      :				Optional[int]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''test-generation-config'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''test-generation-config'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Optional[Any]		     =					GenerationConfig.from_pretrained(F'''{USER}/test-generation-config'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)







       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Tuple		     =					GenerationConfig(
             do_sample=_SCREAMING_SNAKE_CASE				,       temperature=0.7				,       length_penalty=1.0				,       )
         config.push_to_hub('''valid_org/test-generation-config-org'''				,       use_auth_token=self._token		)

         A_      :				Union[str, Any]		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)

        # Reset repo
         delete_repo(token=self._token				,       repo_id='''valid_org/test-generation-config-org'''		)

         # Push to hub via save_pretrained
         with tempfile.TemporaryDirectory() as tmp_dir:
           config.save_pretrained(
               _SCREAMING_SNAKE_CASE				,       repo_id='''valid_org/test-generation-config-org'''				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token		)

         A_      :				Dict		     =					GenerationConfig.from_pretrained('''valid_org/test-generation-config-org'''		)
         for k, v in config.to_dict().items():
           if k != "transformers_version":
             self.assertEqual(_SCREAMING_SNAKE_CASE				,       getattr(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)		)




