

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




	def        lowerCAmelCase_       (     self						:     Union[str, Any]					,      **__A						:     Any							):
		return AutoTokenizer.from_pretrained(self.checkpoint					,      **__A							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		shutil.rmtree(self.tmpdirname							)




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			str		    =			self.get_tokenizer()

		__A :			Optional[Any]		    =			BarkProcessor(tokenizer=__A							)

		processor.save_pretrained(self.tmpdirname							)
		__A :			List[str]		    =			BarkProcessor.from_pretrained(self.tmpdirname							)

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




	@slow
	def        lowerCAmelCase_       (     self						:     Union[str, Any]							):
		__A :			Optional[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					,      )

		__A :			str		    =			self.get_tokenizer(bos_token="""(BOS)"""					,      eos_token="""(EOS)"""							)

		__A :			Optional[int]		    =			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        lowerCAmelCase_       (     self						:     Optional[Any]							):
		__A :			List[str]		    =			BarkProcessor.from_pretrained(
		    pretrained_processor_name_or_path=self.checkpoint					,      speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,      )

		__A :			int		    =			35
		__A :			Union[str, Any]		    =			2
		__A :			Optional[Any]		    =			8

		__A :			List[str]		    =			{
		    """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 :			Union[str, Any]		    =			processor(text=self.input_string					,      voice_preset=__A							)

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




	def        lowerCAmelCase_       (     self						:     Tuple							):
		__A :			str		    =			self.get_tokenizer()

		__A :			str		    =			BarkProcessor(tokenizer=__A							)

		__A :			int		    =			processor(text=self.input_string							)

		__A :			int		    =			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()							)










from __future__ import annotations

def 	_lowerCAmelCase (    A__    ,	A__ = None						):
      lowercase__  =	word_bank or []
      # create a table
      lowercase__  =	len(A__						) + 1

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

      # iterate through the indices
      for i in range(A__						):
            # condition
            if table[i] != []:
                  for word in word_bank:
                        # slice condition
                        if target[i : i + len(A__						)] == word:
                              lowercase__  =	[
                                  [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(A__						)] += new_combinations

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

      return table[len(A__						)]


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"],
           )
       )
