



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     _UpperCAmelCase			(       unittest.TestCase     ):




     def 		snake_case_		(				self):
         A__		 =					'''ylacombe/bark-small'''
         A__		 =					tempfile.mkdtemp()
         A__		 =					'''en_speaker_1'''
         A__		 =					'''This is a test string'''
         A__		 =					'''speaker_embeddings_path.json'''
         A__		 =					'''speaker_embeddings'''




     def 		snake_case_		(				self ,						**a__):
         return AutoTokenizer.from_pretrained(self.checkpoint ,						**a__)




     def 		snake_case_		(				self):
         shutil.rmtree(self.tmpdirname)




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=a__)

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

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




     @slow
     def 		snake_case_		(				self):
         A__		 =					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 ,						)

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

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

         A__		 =					3_5
         A__		 =					2
         A__		 =					8

         A__		 =					{
             '''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
         A__		 =					processor(text=self.input_string ,						voice_preset=a__)

         A__		 =					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
         A__		 =					os.path.join(self.tmpdirname ,						'''file.npz''')
         np.savez(a__ ,						**a__)
         A__		 =					processor(text=self.input_string ,						voice_preset=a__)
         A__		 =					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
         A__		 =					processor(text=self.input_string ,						voice_preset=self.voice_preset)




     def 		snake_case_		(				self):
         A__		 =					self.get_tokenizer()

         A__		 =					BarkProcessor(tokenizer=a__)

         A__		 =					processor(text=self.input_string)

         A__		 =					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())





from __future__ import annotations



def 		lowerCAmelCase__    (					UpperCamelCase_				:       str ,       UpperCamelCase_				:       list[str] | None = None	)->  list[list[str]]:
    A__		 =					word_bank or []
    # create a table
    A__		 =					len(UpperCamelCase_	) + 1

    A__		 =					[]
    for _ in range(UpperCamelCase_	):
        table.append([]	)
    # seed value
    A__		 =					[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase_	):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase_	)] == word:
                    A__		 =					[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase_	)] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase_	)]:
        combination.reverse()

    return table[len(UpperCamelCase_	)]


if __name__ == "__main__":
   print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
   print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
   print(
       all_construct(
           "hexagonosaurus",
           ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
       )
   )
