






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


_UpperCAmelCase			:		Dict   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		str   				=    {
    "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  lowerCAmelCase_       (				UpperCamelCase__					):
							UpperCamelCase_		:Optional[Any]		      =    'mctct'




							def __init__(   self     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Any=8_065 ,							SCREAMING_SNAKE_CASE_     :   Any=1_536 ,							SCREAMING_SNAKE_CASE_     :   int=36 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=6_144 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=4 ,							SCREAMING_SNAKE_CASE_     :   Tuple=384 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=920 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.3 ,							SCREAMING_SNAKE_CASE_     :   str="relu" ,							SCREAMING_SNAKE_CASE_     :   Any=0.02 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.3 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.3 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=1 ,							SCREAMING_SNAKE_CASE_     :   Tuple=0 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=2 ,							SCREAMING_SNAKE_CASE_     :   Dict=1 ,							SCREAMING_SNAKE_CASE_     :   List[Any]=0.3 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=1 ,							SCREAMING_SNAKE_CASE_     :   Dict=(7,) ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=(3,) ,							SCREAMING_SNAKE_CASE_     :   List[str]=80 ,							SCREAMING_SNAKE_CASE_     :   int=1 ,							SCREAMING_SNAKE_CASE_     :   str=None ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]="sum" ,							SCREAMING_SNAKE_CASE_     :   List[Any]=False ,							**SCREAMING_SNAKE_CASE_     :   Dict ,							):
											super().__init__(**_a ,							pad_token_id=_a ,							bos_token_id=_a ,							eos_token_id=_a	)
											lowerCAmelCase__      			=						vocab_size
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						intermediate_size
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						attention_head_dim
											lowerCAmelCase__      			=						max_position_embeddings
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						layerdrop
											lowerCAmelCase__      			=						hidden_act
											lowerCAmelCase__      			=						initializer_range
											lowerCAmelCase__      			=						hidden_dropout_prob
											lowerCAmelCase__      			=						attention_probs_dropout_prob
											lowerCAmelCase__      			=						pad_token_id
											lowerCAmelCase__      			=						bos_token_id
											lowerCAmelCase__      			=						eos_token_id
											lowerCAmelCase__      			=						conv_glu_dim
											lowerCAmelCase__      			=						conv_dropout
											lowerCAmelCase__      			=						num_conv_layers
											lowerCAmelCase__      			=						input_feat_per_channel
											lowerCAmelCase__      			=						input_channels
											lowerCAmelCase__      			=						conv_channels
											lowerCAmelCase__      			=						ctc_loss_reduction
											lowerCAmelCase__      			=						ctc_zero_infinity

											# prevents config testing fail with exporting to json
											lowerCAmelCase__      			=						list(_a	)
											lowerCAmelCase__      			=						list(_a	)

											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}`.'	)







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 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						'''ylacombe/bark-small'''
											lowerCAmelCase__      			=						tempfile.mkdtemp()
											lowerCAmelCase__      			=						'''en_speaker_1'''
											lowerCAmelCase__      			=						'''This is a test string'''
											lowerCAmelCase__      			=						'''speaker_embeddings_path.json'''
											lowerCAmelCase__      			=						'''speaker_embeddings'''




							def 						__snake_case					(   self     :   Optional[Any] ,							**SCREAMING_SNAKE_CASE_     :   Optional[int]	):
											return AutoTokenizer.from_pretrained(self.checkpoint ,							**SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											shutil.rmtree(self.tmpdirname	)




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_	)

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

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




							@slow
							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						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 ,							)

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

											lowerCAmelCase__      			=						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 						__snake_case					(   self     :   Dict	):
											lowerCAmelCase__      			=						BarkProcessor.from_pretrained(
											    pretrained_processor_name_or_path=self.checkpoint ,							speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,							)

											lowerCAmelCase__      			=						35
											lowerCAmelCase__      			=						2
											lowerCAmelCase__      			=						8

											lowerCAmelCase__      			=						{
											    '''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
											lowerCAmelCase__      			=						processor(text=self.input_string ,							voice_preset=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						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
											lowerCAmelCase__      			=						os.path.join(self.tmpdirname ,							'''file.npz'''	)
											np.savez(SCREAMING_SNAKE_CASE_ ,							**SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						processor(text=self.input_string ,							voice_preset=SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						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
											lowerCAmelCase__      			=						processor(text=self.input_string ,							voice_preset=self.voice_preset	)




							def 						__snake_case					(   self     :   Optional[int]	):
											lowerCAmelCase__      			=						self.get_tokenizer()

											lowerCAmelCase__      			=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						processor(text=self.input_string	)

											lowerCAmelCase__      			=						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()	)





