


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase	: Tuple  				=logging.get_logger(__name__)

__lowerCAmelCase	: Optional[int]  				={
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			List[str]						=		'''xmod'''


				def __init__(       self			:List[str]			,			lowerCAmelCase__			:List[str]=30_522			,			lowerCAmelCase__			:Tuple=768			,			lowerCAmelCase__			:Optional[int]=12			,			lowerCAmelCase__			:Union[str, Any]=12			,			lowerCAmelCase__			:Optional[int]=3_072			,			lowerCAmelCase__			:Dict="gelu"			,			lowerCAmelCase__			:Tuple=0.1			,			lowerCAmelCase__			:str=0.1			,			lowerCAmelCase__			:Any=512			,			lowerCAmelCase__			:int=2			,			lowerCAmelCase__			:Optional[int]=0.02			,			lowerCAmelCase__			:Tuple=1E-1_2			,			lowerCAmelCase__			:Union[str, Any]=1			,			lowerCAmelCase__			:Optional[Any]=0			,			lowerCAmelCase__			:Optional[int]=2			,			lowerCAmelCase__			:Any="absolute"			,			lowerCAmelCase__			:int=True			,			lowerCAmelCase__			:Dict=None			,			lowerCAmelCase__			:Tuple=False			,			lowerCAmelCase__			:List[Any]=2			,			lowerCAmelCase__			:Dict=False			,			lowerCAmelCase__			:Tuple=True			,			lowerCAmelCase__			:Optional[Any]=True			,			lowerCAmelCase__			:Dict=("en_XX",)			,			lowerCAmelCase__			:Dict=None			,			**lowerCAmelCase__			:Optional[int]			,			)		->			Union[str, Any]:
										super().__init__(pad_token_id=lowerCAmelCase__			,			bos_token_id=lowerCAmelCase__			,			eos_token_id=lowerCAmelCase__			,			**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Any					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     int					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     str					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     str					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     Dict					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Any					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Dict					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     List[str]					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     Dict					=		type_vocab_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     Tuple					=		position_embedding_type
										__SCREAMING_SNAKE_CASE						:     str					=		use_cache
										__SCREAMING_SNAKE_CASE						:     Any					=		classifier_dropout
										__SCREAMING_SNAKE_CASE						:     Tuple					=		pre_norm
										__SCREAMING_SNAKE_CASE						:     int					=		adapter_reduction_factor
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		adapter_layer_norm
										__SCREAMING_SNAKE_CASE						:     str					=		adapter_reuse_layer_norm
										__SCREAMING_SNAKE_CASE						:     List[str]					=		ln_before_adapter
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		list(lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		default_language




class        _lowercase      (			A__       ):






				'''simple docstring'''



				@property
				def 		__magic_name__(       self			:str	)		->			Mapping[str, Mapping[int, str]]:
										if self.task == "multiple-choice":
																__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
										else:
																__SCREAMING_SNAKE_CASE						:     List[Any]					=		{0: '''batch''', 1: '''sequence'''}
										return OrderedDict(
										    [
										        ('''input_ids''', dynamic_axis),
										        ('''attention_mask''', dynamic_axis),
										    ]	)




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()	)
