
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        SCREAMING_SNAKE_CASE__    (							unittest.TestCase    ):

							"""simple docstring"""






							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			'''ylacombe/bark-small'''
													__UpperCamelCase       =			tempfile.mkdtemp()
													__UpperCamelCase       =			'''en_speaker_1'''
													__UpperCamelCase       =			'''This is a test string'''
													__UpperCamelCase       =			'''speaker_embeddings_path.json'''
													__UpperCamelCase       =			'''speaker_embeddings'''



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Union[str, Any]:





													'''simple docstring'''






													return AutoTokenizer.from_pretrained(self.checkpoint     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self			)->   Union[str, Any]:





													'''simple docstring'''






													shutil.rmtree(self.tmpdirname			)



							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_			)

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

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



							@slow
							def     A__		(  self			)->   List[Any]:





													'''simple docstring'''






													__UpperCamelCase       =			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     ,	)

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

													__UpperCamelCase       =			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     A__		(  self			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			BarkProcessor.from_pretrained(
													    pretrained_processor_name_or_path=self.checkpoint     ,	speaker_embeddings_dict_path=self.speaker_embeddings_dict_path     ,	)

													__UpperCamelCase       =			35
													__UpperCamelCase       =			2
													__UpperCamelCase       =			8

													__UpperCamelCase       =			{
													    '''semantic_prompt''': np.ones(SCREAMING_SNAKE_CASE_			),
													    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)			),
													    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)			),
													}

													# test providing already loaded voice_preset
													__UpperCamelCase       =			processor(text=self.input_string     ,	voice_preset=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			inputs['''history_prompt''']
													for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist()     ,	processed_voice_preset.get(SCREAMING_SNAKE_CASE_     ,	np.array([]			)			).tolist()			)

													# test loading voice preset from npz file
													__UpperCamelCase       =			os.path.join(self.tmpdirname     ,	'''file.npz'''			)
													np.savez(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			processor(text=self.input_string     ,	voice_preset=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			inputs['''history_prompt''']

													for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist()     ,	processed_voice_preset.get(SCREAMING_SNAKE_CASE_     ,	np.array([]			)			).tolist()			)

													# test loading voice preset from the hub
													__UpperCamelCase       =			processor(text=self.input_string     ,	voice_preset=self.voice_preset			)



							def     A__		(  self			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.get_tokenizer()

													__UpperCamelCase       =			BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			processor(text=self.input_string			)

													__UpperCamelCase       =			tokenizer(
													    self.input_string     ,	padding='''max_length'''     ,	max_length=256     ,	add_special_tokens=SCREAMING_SNAKE_CASE_     ,	return_attention_mask=SCREAMING_SNAKE_CASE_     ,	return_token_type_ids=SCREAMING_SNAKE_CASE_     ,	)

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






from ....configuration_utils import PretrainedConfig
from ....utils import logging


lowercase__							:							Tuple									=			logging.get_logger(__name__)

lowercase__							:							Union[str, Any]									=			{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  'mctct'



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=8065     ,	SCREAMING_SNAKE_CASE_=1536     ,	SCREAMING_SNAKE_CASE_=36     ,	SCREAMING_SNAKE_CASE_=6144     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=384     ,	SCREAMING_SNAKE_CASE_=920     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_="relu"     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=0.3     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=(7,)     ,	SCREAMING_SNAKE_CASE_=(3,)     ,	SCREAMING_SNAKE_CASE_=80     ,	SCREAMING_SNAKE_CASE_=1     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="sum"     ,	SCREAMING_SNAKE_CASE_=False     ,	**SCREAMING_SNAKE_CASE_     ,	)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_     ,	pad_token_id=SCREAMING_SNAKE_CASE_     ,	bos_token_id=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			vocab_size
													__UpperCamelCase       =			hidden_size
													__UpperCamelCase       =			num_hidden_layers
													__UpperCamelCase       =			intermediate_size
													__UpperCamelCase       =			num_attention_heads
													__UpperCamelCase       =			attention_head_dim
													__UpperCamelCase       =			max_position_embeddings
													__UpperCamelCase       =			layer_norm_eps
													__UpperCamelCase       =			layerdrop
													__UpperCamelCase       =			hidden_act
													__UpperCamelCase       =			initializer_range
													__UpperCamelCase       =			hidden_dropout_prob
													__UpperCamelCase       =			attention_probs_dropout_prob
													__UpperCamelCase       =			pad_token_id
													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id
													__UpperCamelCase       =			conv_glu_dim
													__UpperCamelCase       =			conv_dropout
													__UpperCamelCase       =			num_conv_layers
													__UpperCamelCase       =			input_feat_per_channel
													__UpperCamelCase       =			input_channels
													__UpperCamelCase       =			conv_channels
													__UpperCamelCase       =			ctc_loss_reduction
													__UpperCamelCase       =			ctc_zero_infinity

													# prevents config testing fail with exporting to json
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			list(SCREAMING_SNAKE_CASE_			)

													if len(self.conv_kernel			) != self.num_conv_layers:
																			raise ValueError(
																			    '''Configuration for convolutional module is incorrect. '''
																			    '''It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '''
																			    F"but is `len(config.conv_kernel) = {len(self.conv_kernel			)}`, "
																			    F"`config.num_conv_layers = {self.num_conv_layers}`."			)




