
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 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Union[str, Any]       )						->						str:
         snake_case								= "ylacombe/bark-small"
         snake_case								= tempfile.mkdtemp()
         snake_case								= "en_speaker_1"
         snake_case								= "This is a test string"
         snake_case								= "speaker_embeddings_path.json"
         snake_case								= "speaker_embeddings"





     def 		UpperCAmelCase(self				:   int    ,   **_A				:   Optional[Any]       )						->						Optional[Any]:
         return AutoTokenizer.from_pretrained(self.checkpoint    ,   **lowerCAmelCase__       )





     def 		UpperCAmelCase(self				:   List[str]       )						->						Optional[int]:
         shutil.rmtree(self.tmpdirname       )





     def 		UpperCAmelCase(self				:   Any       )						->						Optional[int]:
         snake_case								= self.get_tokenizer()

         snake_case								= BarkProcessor(tokenizer=lowerCAmelCase__       )

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

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





     @slow
     def 		UpperCAmelCase(self				:   Any       )						->						str:
         snake_case								= 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    ,   )

         snake_case								= self.get_tokenizer(bos_token="(BOS)"    ,   eos_token="(EOS)"       )

         snake_case								= 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				:   List[Any]       )						->						int:
         snake_case								= BarkProcessor.from_pretrained(
             pretrained_processor_name_or_path=self.checkpoint    ,   speaker_embeddings_dict_path=self.speaker_embeddings_dict_path    ,   )

         snake_case								= 3_5
         snake_case								= 2
         snake_case								= 8

         snake_case								= {
             "semantic_prompt": np.ones(lowerCAmelCase__       ),
             "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)       ),
             "fine_prompt": np.ones((nb_codebooks_total, seq_len)       ),
         }

         # test providing already loaded voice_preset
         snake_case								= processor(text=self.input_string    ,   voice_preset=lowerCAmelCase__       )

         snake_case								= inputs["history_prompt"]
         for key in voice_preset:
             self.assertListEqual(voice_preset[key].tolist()    ,   processed_voice_preset.get(lowerCAmelCase__    ,   np.array([]       )       ).tolist()       )

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

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

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





     def 		UpperCAmelCase(self				:   Optional[Any]       )						->						List[str]:
         snake_case								= self.get_tokenizer()

         snake_case								= BarkProcessor(tokenizer=lowerCAmelCase__       )

         snake_case								= processor(text=self.input_string       )

         snake_case								= tokenizer(
             self.input_string    ,   padding="max_length"    ,   max_length=2_5_6    ,   add_special_tokens=lowerCAmelCase__    ,   return_attention_mask=lowerCAmelCase__    ,   return_token_type_ids=lowerCAmelCase__    ,   )

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



class 					lowerCamelCase		:





     def __init__(self				:   List[Any]    ,   _A				:   str       )						->						Any:
         # we need a list not a string, so do something to change the type
         snake_case								= arr.split(","       )





     def 		UpperCAmelCase(self				:   str       )						->						str:
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         snake_case								= [int(self.array[0]       )] * len(self.array       )
         for i in range(1    ,   len(self.array       )       ):
             snake_case								= max(
                 int(self.array[i]       ) + sum_value[i - 1]    ,   int(self.array[i]       )       )
             snake_case								= max(sum_value[i]    ,   rear[i - 1]       )
         return rear[len(self.array       ) - 1]


if __name__ == "__main__":
   _A   =			input("please input some numbers:")
   _A   =			SubArray(whole_array)
   _A   =			array.solve_sub_array()
   print(("the results is:", re))

