def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			int			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			list[list[int]]  ):

      def update_area_of_max_square(lowerCAmelCase_:			int			,						lowerCAmelCase_:			int  ) -> int:
            # BASE CASE
            if row >= rows or col >= cols:
                  return 0

            snake_case_  :      Optional[int]  	=							update_area_of_max_square(lowerCAmelCase_			,						col + 1  )
            snake_case_  :      int  	=							update_area_of_max_square(row + 1			,						col + 1  )
            snake_case_  :      str  	=							update_area_of_max_square(row + 1			,						lowerCAmelCase_  )

            if mat[row][col]:
                  snake_case_  :      str  	=							1 + min([right, diagonal, down]  )
                  snake_case_  :      Optional[Any]  	=							max(largest_square_area[0]			,						lowerCAmelCase_  )
                  return sub_problem_sol
            else:
                  return 0

      snake_case_  :      Tuple  	=							[0]
      update_area_of_max_square(0			,						0  )
      return largest_square_area[0]






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			int			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			list[list[int]]  ):

      def update_area_of_max_square_using_dp_array(
          lowerCAmelCase_:			int			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			list[list[int]]  ) -> int:
            if row >= rows or col >= cols:
                  return 0
            if dp_array[row][col] != -1:
                  return dp_array[row][col]

            snake_case_  :      Optional[Any]  	=							update_area_of_max_square_using_dp_array(lowerCAmelCase_			,						col + 1			,						lowerCAmelCase_  )
            snake_case_  :      List[Any]  	=							update_area_of_max_square_using_dp_array(row + 1			,						col + 1			,						lowerCAmelCase_  )
            snake_case_  :      Optional[Any]  	=							update_area_of_max_square_using_dp_array(row + 1			,						lowerCAmelCase_			,						lowerCAmelCase_  )

            if mat[row][col]:
                  snake_case_  :      Optional[int]  	=							1 + min([right, diagonal, down]  )
                  snake_case_  :      List[Any]  	=							max(largest_square_area[0]			,						lowerCAmelCase_  )
                  snake_case_  :      Optional[int]  	=							sub_problem_sol
                  return sub_problem_sol
            else:
                  return 0

      snake_case_  :      Tuple  	=							[0]
      snake_case_  :      Tuple  	=							[[-1] * cols for _ in range(lowerCAmelCase_  )]
      update_area_of_max_square_using_dp_array(0			,						0			,						lowerCAmelCase_  )

      return largest_square_area[0]






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			int			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			list[list[int]]  ):
      snake_case_  :      Optional[Any]  	=							[[0] * (cols + 1) for _ in range(rows + 1  )]
      snake_case_  :      List[Any]  	=							0
      for row in range(rows - 1			,						-1			,						-1  ):
            for col in range(cols - 1			,						-1			,						-1  ):
                  snake_case_  :      str  	=							dp_array[row][col + 1]
                  snake_case_  :      Optional[Any]  	=							dp_array[row + 1][col + 1]
                  snake_case_  :      str  	=							dp_array[row + 1][col]

                  if mat[row][col] == 1:
                        snake_case_  :      Any  	=							1 + min(lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_  )
                        snake_case_  :      Optional[Any]  	=							max(dp_array[row][col]			,						lowerCAmelCase_  )
                  else:
                        snake_case_  :      Optional[int]  	=							0

      return largest_square_area






def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			int			,						lowerCAmelCase_:			int			,						lowerCAmelCase_:			list[list[int]]  ):
      snake_case_  :      List[str]  	=							[0] * (cols + 1)
      snake_case_  :      List[Any]  	=							[0] * (cols + 1)
      snake_case_  :      int  	=							0
      for row in range(rows - 1			,						-1			,						-1  ):
            for col in range(cols - 1			,						-1			,						-1  ):
                  snake_case_  :      str  	=							current_row[col + 1]
                  snake_case_  :      str  	=							next_row[col + 1]
                  snake_case_  :      List[Any]  	=							next_row[col]

                  if mat[row][col] == 1:
                        snake_case_  :      Tuple  	=							1 + min(lowerCAmelCase_			,						lowerCAmelCase_			,						lowerCAmelCase_  )
                        snake_case_  :      Dict  	=							max(current_row[col]			,						lowerCAmelCase_  )
                  else:
                        snake_case_  :      Optional[int]  	=							0
            snake_case_  :      Dict  	=							current_row

      return largest_square_area


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))






'''simple docstring'''




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow



@require_torch
class 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)
    processor.save_pretrained(
        self.tmpdirname ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			speaker_embeddings_directory=self.speaker_embeddings_directory ,			)

    UpperCAmelCase    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        self.tmpdirname ,			self.speaker_embeddings_dict_path ,			bos_token='(BOS)' ,			eos_token='(EOS)' ,			)

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer_add_kwargs.get_vocab()  )
  def     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        'coarse_prompt': np.ones((nb_codebooks_coarse, seq_len)  ),
        'fine_prompt': np.ones((nb_codebooks_total, seq_len)  ),
    }

    # test providing already loaded voice_preset
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from the hub
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=self.voice_preset  )





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


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

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


