





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 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   "ylacombe/bark-small"
       UpperCamelCase	:					List[Any]       	=   tempfile.mkdtemp()
       UpperCamelCase	:					Optional[int]       	=   "en_speaker_1"
       UpperCamelCase	:					Tuple       	=   "This is a test string"
       UpperCamelCase	:					Tuple       	=   "speaker_embeddings_path.json"
       UpperCamelCase	:					List[str]       	=   "speaker_embeddings"





      def        __UpperCamelCase( self       ,							**A_   ):
       '''simple docstring'''




       return AutoTokenizer.from_pretrained(self.checkpoint       ,							**A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       shutil.rmtree(self.tmpdirname   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   self.get_tokenizer()

       UpperCamelCase	:					Optional[Any]       	=   BarkProcessor(tokenizer=A_   )

       processor.save_pretrained(self.tmpdirname   )
       UpperCamelCase	:					Tuple       	=   BarkProcessor.from_pretrained(self.tmpdirname   )

       self.assertEqual(processor.tokenizer.get_vocab()       ,							tokenizer.get_vocab()   )





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   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	:					Optional[Any]       	=   self.get_tokenizer(bos_token="(BOS)"       ,							eos_token="(EOS)"   )

       UpperCamelCase	:					List[Any]       	=   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        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint       ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,							)

       UpperCamelCase	:					int       	=   35
       UpperCamelCase	:					str       	=   2
       UpperCamelCase	:					Dict       	=   8

       UpperCamelCase	:					Tuple       	=   {
           "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	:					Any       	=   processor(text=self.input_string       ,							voice_preset=A_   )

       UpperCamelCase	:					Dict       	=   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	:					str       	=   os.path.join(self.tmpdirname       ,							"file.npz"   )
       np.savez(A_       ,							**A_   )
       UpperCamelCase	:					Union[str, Any]       	=   processor(text=self.input_string       ,							voice_preset=A_   )
       UpperCamelCase	:					List[Any]       	=   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	:					Optional[Any]       	=   processor(text=self.input_string       ,							voice_preset=self.voice_preset   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   self.get_tokenizer()

       UpperCamelCase	:					str       	=   BarkProcessor(tokenizer=A_   )

       UpperCamelCase	:					str       	=   processor(text=self.input_string   )

       UpperCamelCase	:					Tuple       	=   tokenizer(
           self.input_string       ,							padding="max_length"       ,							max_length=256       ,							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()   )







def 		A_				(     _lowerCAmelCase = 50     )						-> int:
 UpperCamelCase	:					List[Any]       	=   [[0] * 3 for _ in range(length + 1     )]

 for row_length in range(length + 1     ):
  for tile_length in range(2							,   5     ):
   for tile_start in range(row_length - tile_length + 1     ):
    different_colour_ways_number[row_length][tile_length - 2] += (
        different_colour_ways_number[row_length - tile_start - tile_length][
            tile_length - 2
        ]
        + 1
    )

 return sum(different_colour_ways_number[length]     )


if __name__ == "__main__":
    print(f"""{solution() = }""")
