
"""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 					lowerCAmelCase_					(   unittest.TestCase     ):



						'''simple docstring'''


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> str:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Any	,**A_  :   Any   )  -> List[Any]:
							return AutoTokenizer.from_pretrained(self.checkpoint	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> List[str]:
							shutil.rmtree(self.tmpdirname   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Dict:
							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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict:
							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     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> str:
							A     = BarkProcessor.from_pretrained(
							    pretrained_processor_name_or_path=self.checkpoint	,speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,)

							A     = 35
							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     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[str]:
							A     = self.get_tokenizer()

							A     = BarkProcessor(tokenizer=A_   )

							A     = processor(text=self.input_string   )

							A     = 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'''

import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						a_		(     snake_case    ,   unittest.TestCase    ):
							UpperCAmelCase			:		int        =				MgpstrTokenizer
							UpperCAmelCase			:		List[Any]        =				False
							UpperCAmelCase			:		Optional[int]        =				{}
							UpperCAmelCase			:		Any        =				False


							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										super().setUp()

										# fmt: off
										snake_case:    List[Any]      							=['[GO]', '[s]', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']
										# fmt: on
										snake_case:    Union[str, Any]      							=dict(zip(a_      ,						range(len(a_			)			)			)			)

										snake_case:    List[Any]      							=os.path.join(self.tmpdirname      ,						VOCAB_FILES_NAMES['vocab_file']			)
										with open(self.vocab_file      ,						'w'      ,						encoding='utf-8'			) as fp:
													fp.write(json.dumps(a_			) + '\n'			)


							def    UpperCamelCase     (				self   :						Optional[Any]      ,						**a_   :						List[Any]			)			->					str:
										return MgpstrTokenizer.from_pretrained(self.tmpdirname      ,						**a_			)


							def    UpperCamelCase     (				self   :						int      ,						a_   :						Dict			)			->					int:
										snake_case:    Any      							='tester'
										snake_case:    List[str]      							='tester'
										return input_text, output_text


							@unittest.skip('MGP-STR always lower cases letters.'			)
							def    UpperCamelCase     (				self   :						Any			)			->					Dict:
										pass


							def    UpperCamelCase     (				self   :						Optional[int]			)			->					int:
										snake_case:    int      							=self.get_tokenizers(do_lower_case=a_			)
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case:    Optional[Any]      							='[SPECIAL_TOKEN]'

																tokenizer.add_special_tokens({'cls_token': special_token}			)
																snake_case:    List[str]      							=tokenizer.encode([special_token]      ,						add_special_tokens=a_			)
																self.assertEqual(len(a_			)      ,						1			)

																snake_case:    Union[str, Any]      							=tokenizer.decode(a_      ,						skip_special_tokens=a_			)
																self.assertTrue(special_token not in decoded			)


							def    UpperCamelCase     (				self   :						int			)			->					str:
										snake_case:    int      							=self.get_tokenizers()
										for tokenizer in tokenizers:
													with self.subTest(F'''{tokenizer.__class__.__name__}'''			):
																snake_case							,  snake_case:    Dict      							=self.get_input_output_texts(a_			)

																snake_case:    Dict      							=tokenizer.tokenize(a_			)
																snake_case:    str      							=tokenizer.convert_tokens_to_ids(a_			)
																snake_case:    List[Any]      							=tokenizer.encode(a_      ,						add_special_tokens=a_			)
																self.assertListEqual(a_      ,						a_			)

																snake_case:    str      							=tokenizer.convert_ids_to_tokens(a_			)
																self.assertNotEqual(len(a_			)      ,						0			)
																snake_case:    Tuple      							=tokenizer.decode(a_			)
																self.assertIsInstance(a_      ,						a_			)

																self.assertEqual(text_a.replace(' '      ,						''			)      ,						a_			)


							@unittest.skip('MGP-STR tokenizer only handles one sequence.'			)
							def    UpperCamelCase     (				self   :						Dict			)			->					str:
										pass




							@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer'			)
							def    UpperCamelCase     (				self   :						Optional[Any]			)			->					Optional[int]:
										pass




