


'''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 							__UpperCamelCase    (		unittest.TestCase ):




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  'ylacombe/bark-small'
						__a      :	List[str]    =  tempfile.mkdtemp()
						__a      :	Optional[Any]    =  'en_speaker_1'
						__a      :	Dict    =  'This is a test string'
						__a      :	Optional[Any]    =  'speaker_embeddings_path.json'
						__a      :	Union[str, Any]    =  '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'''





						__a      :	int    =  self.get_tokenizer()

						__a      :	Any    =  BarkProcessor(tokenizer=__a							)

						processor.save_pretrained(self.tmpdirname							)
						__a      :	Any    =  BarkProcessor.from_pretrained(self.tmpdirname							)

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




	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  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      :	int    =  self.get_tokenizer(bos_token='(BOS)'						,    eos_token='(EOS)'							)

						__a      :	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'''





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

						__a      :	Tuple    =  35
						__a      :	Optional[int]    =  2
						__a      :	int    =  8

						__a      :	List[Any]    =  {
						    '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      :	str    =  processor(text=self.input_string						,    voice_preset=__a							)

						__a      :	int    =  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[Any]    =  os.path.join(self.tmpdirname						,    'file.npz'							)
						np.savez(__a						,    **__a							)
						__a      :	Tuple    =  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 the hub
						__a      :	Dict    =  processor(text=self.input_string						,    voice_preset=self.voice_preset							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Any    =  self.get_tokenizer()

						__a      :	Optional[int]    =  BarkProcessor(tokenizer=__a							)

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

						__a      :	List[str]    =  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()							)







'''simple docstring'''



__lowercase     :						Optional[Any]	=     {'a': ['c', 'b'], 'b': ['d', 'e'], 'c': [], 'd': [], 'e': []}
__lowercase     :						List[str]	=     ['a', 'b', 'c', 'd', 'e']






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       Any						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					__a      :	Any    =  start
					# add current to visited
					visited.append(_SCREAMING_SNAKE_CASE       )
					__a      :	Union[str, Any]    =  edges[current]
					for neighbor in neighbors:
										# if neighbor not in visited, visit
										if neighbor not in visited:
															__a      :	Dict    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # if all neighbors visited add current to sort
					sort.append(_SCREAMING_SNAKE_CASE       )
					# if all vertices haven't been visited select a new one to visit
					if len(_SCREAMING_SNAKE_CASE       ) != len(_SCREAMING_SNAKE_CASE       ):
										for vertice in vertices:
															if vertice not in visited:
																				__a      :	List[Any]    =  topological_sort(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
    # return sort
					return sort


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     topological_sort('a', [], [])
	print(sort)



