



import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
	from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

	from .test_feature_extraction_speechta import floats_list


__A       					=		get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')






@require_torch
class 	lowercase		(				unittest.TestCase):






	"""simple docstring"""


	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				List[Any]:
							UpperCAmelCase_=     tempfile.mkdtemp()

							UpperCAmelCase_=     SpeechTaTokenizer(__UpperCAmelCase  )
							tokenizer.save_pretrained(self.tmpdirname  )

							UpperCAmelCase_=     {
							    """feature_size""": 1,
							    """padding_value""": 0.0,
							    """sampling_rate""": 16_000,
							    """do_normalize""": False,
							    """num_mel_bins""": 80,
							    """hop_length""": 16,
							    """win_length""": 64,
							    """win_function""": """hann_window""",
							    """fmin""": 80,
							    """fmax""": 7_600,
							    """mel_floor""": 1E-10,
							    """reduction_factor""": 2,
							    """return_attention_mask""": True,
							}

							UpperCAmelCase_=     os.path.join(self.tmpdirname		,						__UpperCAmelCase  )
							with open(self.feature_extraction_file		,						"""w"""		,						encoding="""utf-8"""  ) as fp:
													fp.write(json.dumps(__UpperCAmelCase  ) + """\n"""  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		int		,						**__UpperCAmelCase			:		int  )    ->				Union[str, Any]:
							return SpeechTaTokenizer.from_pretrained(self.tmpdirname		,						**__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any		,						**__UpperCAmelCase			:		Any  )    ->				Any:
							return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname		,						**__UpperCAmelCase  )




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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]  )    ->				Optional[int]:
							UpperCAmelCase_=     self.get_tokenizer()
							UpperCAmelCase_=     self.get_feature_extractor()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							processor.save_pretrained(self.tmpdirname  )
							UpperCAmelCase_=     SpeechTaProcessor.from_pretrained(self.tmpdirname  )

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

							self.assertEqual(processor.feature_extractor.to_json_string()		,						feature_extractor.to_json_string()  )
							self.assertIsInstance(processor.feature_extractor		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				str:
							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=self.get_tokenizer()		,						feature_extractor=self.get_feature_extractor()  )
							processor.save_pretrained(self.tmpdirname  )

							UpperCAmelCase_=     self.get_tokenizer(bos_token="""(BOS)"""		,						eos_token="""(EOS)"""  )
							UpperCAmelCase_=     self.get_feature_extractor(do_normalize=__UpperCAmelCase		,						padding_value=1.0  )

							UpperCAmelCase_=     SpeechTaProcessor.from_pretrained(
							    self.tmpdirname		,						bos_token="""(BOS)"""		,						eos_token="""(EOS)"""		,						do_normalize=__UpperCAmelCase		,						padding_value=1.0  )

							self.assertEqual(processor.tokenizer.get_vocab()		,						tokenizer_add_kwargs.get_vocab()  )
							self.assertIsInstance(processor.tokenizer		,						__UpperCAmelCase  )

							self.assertEqual(processor.feature_extractor.to_json_string()		,						feature_extractor_add_kwargs.to_json_string()  )
							self.assertIsInstance(processor.feature_extractor		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[int]:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							UpperCAmelCase_=     floats_list((3, 1_000)  )

							UpperCAmelCase_=     feature_extractor(audio=__UpperCAmelCase		,						return_tensors="""np"""  )
							UpperCAmelCase_=     processor(audio=__UpperCAmelCase		,						return_tensors="""np"""  )

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()		,						input_processor[key].sum()		,						delta=1E-2  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				str:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							UpperCAmelCase_=     floats_list((3, 1_000)  )

							UpperCAmelCase_=     feature_extractor(audio_target=__UpperCAmelCase		,						return_tensors="""np"""  )
							UpperCAmelCase_=     processor(audio_target=__UpperCAmelCase		,						return_tensors="""np"""  )

							for key in input_feat_extract.keys():
													self.assertAlmostEqual(input_feat_extract[key].sum()		,						input_processor[key].sum()		,						delta=1E-2  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Tuple:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							UpperCAmelCase_=     """This is a test string"""

							UpperCAmelCase_=     processor(text=__UpperCAmelCase  )
							UpperCAmelCase_=     tokenizer(__UpperCAmelCase  )

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Tuple  )    ->				List[Any]:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							UpperCAmelCase_=     """This is a test string"""

							UpperCAmelCase_=     processor(text_target=__UpperCAmelCase  )
							UpperCAmelCase_=     tokenizer(__UpperCAmelCase  )

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




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Any  )    ->				Union[str, Any]:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							UpperCAmelCase_=     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							UpperCAmelCase_=     processor.batch_decode(__UpperCAmelCase  )
							UpperCAmelCase_=     tokenizer.batch_decode(__UpperCAmelCase  )

							self.assertListEqual(__UpperCAmelCase		,						__UpperCAmelCase  )




	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				Union[str, Any]:
							UpperCAmelCase_=     self.get_feature_extractor()
							UpperCAmelCase_=     self.get_tokenizer()

							UpperCAmelCase_=     SpeechTaProcessor(tokenizer=__UpperCAmelCase		,						feature_extractor=__UpperCAmelCase  )

							self.assertListEqual(
							    processor.model_input_names		,						feature_extractor.model_input_names		,						msg="""`processor` and `feature_extractor` model input names do not match"""		,						)









import copy
from typing import Dict

from ...generation.configuration_utils import GenerationConfig
from ...utils import logging


__A       					=		logging.get_logger(__name__)






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Optional[int]	     =							"semantic"




	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

							UpperCAmelCase_=     text_encoding_offset
							UpperCAmelCase_=     text_pad_token
							UpperCAmelCase_=     eos_token_id
							UpperCAmelCase_=     semantic_infer_token
							UpperCAmelCase_=     semantic_vocab_size
							UpperCAmelCase_=     max_input_semantic_length
							UpperCAmelCase_=     semantic_rate_hz






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"coarse_acoustics"




	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
							super().__init__(
							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)

							UpperCAmelCase_=     coarse_semantic_pad_token
							UpperCAmelCase_=     coarse_rate_hz
							UpperCAmelCase_=     n_coarse_codebooks
							UpperCAmelCase_=     coarse_infer_token
							UpperCAmelCase_=     max_coarse_input_length
							UpperCAmelCase_=     max_coarse_history
							UpperCAmelCase_=     sliding_window_len






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Tuple	     =							"fine_acoustics"




	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
							super().__init__(temperature=__UpperCAmelCase  )

							UpperCAmelCase_=     max_fine_history_length
							UpperCAmelCase_=     max_fine_input_length
							UpperCAmelCase_=     n_fine_codebooks






class 	lowercase		(				snake_case__):






	"""simple docstring"""


	a__   :      Any	     =							"bark"
	a__   :      List[str]	     =							True




	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
							if semantic_config is None:
													UpperCAmelCase_=     {}
													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )

							if coarse_acoustics_config is None:
													UpperCAmelCase_=     {}
													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )

							if fine_acoustics_config is None:
													UpperCAmelCase_=     {}
													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )

							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )

							UpperCAmelCase_=     sample_rate
							UpperCAmelCase_=     codebook_size




	@classmethod
	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
							return cls(
							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)




	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )

							UpperCAmelCase_=     self.semantic_config.to_dict()
							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()

							UpperCAmelCase_=     self.__class__.model_type
							return output




