


from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput
class        _lowercase      (			nn.Module       ):






				'''simple docstring'''



				def __init__(       self			:Optional[int]			,			lowerCAmelCase__			:int = 16			,			lowerCAmelCase__			:int = 88			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:int = 1			,			lowerCAmelCase__			:float = 0.0			,			lowerCAmelCase__			:int = 32			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:bool = False			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:Optional[int] = None			,			lowerCAmelCase__			:str = "geglu"			,			lowerCAmelCase__			:Optional[int] = None			,			)		->			int:
										super().__init__()
										__SCREAMING_SNAKE_CASE						:     int					=		nn.ModuleList(
										    [
										        TransformeraDModel(
										            num_attention_heads=lowerCAmelCase__			,			attention_head_dim=lowerCAmelCase__			,			in_channels=lowerCAmelCase__			,			num_layers=lowerCAmelCase__			,			dropout=lowerCAmelCase__			,			norm_num_groups=lowerCAmelCase__			,			cross_attention_dim=lowerCAmelCase__			,			attention_bias=lowerCAmelCase__			,			sample_size=lowerCAmelCase__			,			num_vector_embeds=lowerCAmelCase__			,			activation_fn=lowerCAmelCase__			,			num_embeds_ada_norm=lowerCAmelCase__			,			)
										        for _ in range(2	)
										    ]	)

										# Variables that can be set by a pipeline:

										# The ratio of transformer1 to transformer2's output states to be combined during inference
										__SCREAMING_SNAKE_CASE						:     List[str]					=		0.5

										# The shape of `encoder_hidden_states` is expected to be
										# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		[77, 257]

										# Which transformer to use to encode which condition.
										# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
										__SCREAMING_SNAKE_CASE						:     List[str]					=		[1, 0]



				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:List[Any]			,			lowerCAmelCase__			:Tuple=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:bool = True			,			)		->			Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		hidden_states

										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		0
										# attention_mask is not used yet
										for i in range(2	):
																# for each of the two transformers, pass the corresponding condition tokens
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
																__SCREAMING_SNAKE_CASE						:     str					=		self.transformer_index_for_condition[i]
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.transformers[transformer_index](
																    lowerCAmelCase__			,			encoder_hidden_states=lowerCAmelCase__			,			timestep=lowerCAmelCase__			,			cross_attention_kwargs=lowerCAmelCase__			,			return_dict=lowerCAmelCase__			,			)[0]
																encoded_states.append(encoded_state - input_states	)
																tokens_start += self.condition_lengths[i]

										__SCREAMING_SNAKE_CASE						:     Tuple					=		encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
										__SCREAMING_SNAKE_CASE						:     List[str]					=		output_states + input_states

										if not return_dict:
																return (output_states,)

										return TransformeraDModelOutput(sample=lowerCAmelCase__	)




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        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def 		__magic_name__(       self			:Union[str, Any]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		'''ylacombe/bark-small'''
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		tempfile.mkdtemp()
										__SCREAMING_SNAKE_CASE						:     str					=		'''en_speaker_1'''
										__SCREAMING_SNAKE_CASE						:     Any					=		'''This is a test string'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings_path.json'''
										__SCREAMING_SNAKE_CASE						:     int					=		'''speaker_embeddings'''


				def 		__magic_name__(       self			:List[str]			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										return AutoTokenizer.from_pretrained(self.checkpoint			,			**lowerCAmelCase__	)


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


				def 		__magic_name__(       self			:Dict	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Tuple					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										processor.save_pretrained(self.tmpdirname	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BarkProcessor.from_pretrained(self.tmpdirname	)

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


				@slow
				def 		__magic_name__(       self			:Tuple	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		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			,			)

										__SCREAMING_SNAKE_CASE						:     Dict					=		self.get_tokenizer(bos_token='''(BOS)'''			,			eos_token='''(EOS)'''	)

										__SCREAMING_SNAKE_CASE						:     Union[str, 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 		__magic_name__(       self			:List[str]	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		BarkProcessor.from_pretrained(
										    pretrained_processor_name_or_path=self.checkpoint			,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path			,			)

										__SCREAMING_SNAKE_CASE						:     str					=		35
										__SCREAMING_SNAKE_CASE						:     str					=		2
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		8

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
										    '''semantic_prompt''': np.ones(lowerCAmelCase__	),
										    '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)	),
										    '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)	),
										}

										# test providing already loaded voice_preset
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[str]					=		inputs['''history_prompt''']
										for key in voice_preset:
																self.assertListEqual(voice_preset[key].tolist()			,			processed_voice_preset.get(lowerCAmelCase__			,			np.array([]	)	).tolist()	)

										# test loading voice preset from npz file
										__SCREAMING_SNAKE_CASE						:     str					=		os.path.join(self.tmpdirname			,			'''file.npz'''	)
										np.savez(lowerCAmelCase__			,			**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string			,			voice_preset=lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		inputs['''history_prompt''']

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

										# test loading voice preset from the hub
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		processor(text=self.input_string			,			voice_preset=self.voice_preset	)



				def 		__magic_name__(       self			:Tuple	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.get_tokenizer()

										__SCREAMING_SNAKE_CASE						:     Any					=		BarkProcessor(tokenizer=lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		processor(text=self.input_string	)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		tokenizer(
										    self.input_string			,			padding='''max_length'''			,			max_length=256			,			add_special_tokens=lowerCAmelCase__			,			return_attention_mask=lowerCAmelCase__			,			return_token_type_ids=lowerCAmelCase__			,			)

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