


import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase	: Optional[Any]  				=logging.get_logger(__name__)

__lowerCAmelCase	: List[Any]  				={
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		'''blip_2_vision_model'''


				def __init__(       self			:Dict			,			lowerCAmelCase__			:int=1_408			,			lowerCAmelCase__			:str=6_144			,			lowerCAmelCase__			:Union[str, Any]=39			,			lowerCAmelCase__			:str=16			,			lowerCAmelCase__			:Any=224			,			lowerCAmelCase__			:Optional[Any]=14			,			lowerCAmelCase__			:str="gelu"			,			lowerCAmelCase__			:Optional[int]=0.0_0001			,			lowerCAmelCase__			:Union[str, Any]=0.0			,			lowerCAmelCase__			:str=1E-1_0			,			lowerCAmelCase__			:List[str]=True			,			**lowerCAmelCase__			:int			,			)		->			Union[str, Any]:
										super().__init__(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     Any					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		patch_size
										__SCREAMING_SNAKE_CASE						:     Any					=		image_size
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		attention_dropout
										__SCREAMING_SNAKE_CASE						:     Dict					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		qkv_bias



				@classmethod
				def 		__magic_name__(       cls			:int			,			lowerCAmelCase__			:Union[str, os.PathLike]			,			**lowerCAmelCase__			:Optional[Any]	)		->			"PretrainedConfig":
										cls._set_token_in_kwargs(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Tuple					=		cls.get_config_dict(lowerCAmelCase__			,			**lowerCAmelCase__	)

										# get the vision config dict if we are loading from Blip2Config
										if config_dict.get('''model_type'''	) == "blip-2":
																__SCREAMING_SNAKE_CASE						:     int					=		config_dict['''vision_config''']

										if "model_type" in config_dict and hasattr(cls			,			'''model_type'''	) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

										return cls.from_dict(lowerCAmelCase__			,			**lowerCAmelCase__	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		'''blip_2_qformer'''


				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Optional[Any]=30_522			,			lowerCAmelCase__			:Tuple=768			,			lowerCAmelCase__			:List[Any]=12			,			lowerCAmelCase__			:str=12			,			lowerCAmelCase__			:Dict=3_072			,			lowerCAmelCase__			:Dict="gelu"			,			lowerCAmelCase__			:Any=0.1			,			lowerCAmelCase__			:Optional[int]=0.1			,			lowerCAmelCase__			:Tuple=512			,			lowerCAmelCase__			:Tuple=0.02			,			lowerCAmelCase__			:str=1E-1_2			,			lowerCAmelCase__			:Tuple=0			,			lowerCAmelCase__			:Dict="absolute"			,			lowerCAmelCase__			:Any=2			,			lowerCAmelCase__			:List[Any]=1_408			,			**lowerCAmelCase__			:int			,			)		->			Optional[Any]:
										super().__init__(pad_token_id=lowerCAmelCase__			,			**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		vocab_size
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		hidden_size
										__SCREAMING_SNAKE_CASE						:     Any					=		num_hidden_layers
										__SCREAMING_SNAKE_CASE						:     str					=		num_attention_heads
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		hidden_act
										__SCREAMING_SNAKE_CASE						:     Any					=		intermediate_size
										__SCREAMING_SNAKE_CASE						:     int					=		hidden_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		attention_probs_dropout_prob
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		max_position_embeddings
										__SCREAMING_SNAKE_CASE						:     int					=		initializer_range
										__SCREAMING_SNAKE_CASE						:     Tuple					=		layer_norm_eps
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		position_embedding_type
										__SCREAMING_SNAKE_CASE						:     Tuple					=		cross_attention_frequency
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		encoder_hidden_size



				@classmethod
				def 		__magic_name__(       cls			:Dict			,			lowerCAmelCase__			:Union[str, os.PathLike]			,			**lowerCAmelCase__			:Any	)		->			"PretrainedConfig":
										cls._set_token_in_kwargs(lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE						:     Optional[int]					=		cls.get_config_dict(lowerCAmelCase__			,			**lowerCAmelCase__	)

										# get the qformer config dict if we are loading from Blip2Config
										if config_dict.get('''model_type'''	) == "blip-2":
																__SCREAMING_SNAKE_CASE						:     Dict					=		config_dict['''qformer_config''']

										if "model_type" in config_dict and hasattr(cls			,			'''model_type'''	) and config_dict["model_type"] != cls.model_type:
																logger.warning(
																    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''	)

										return cls.from_dict(lowerCAmelCase__			,			**lowerCAmelCase__	)




class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			int						=		'''blip-2'''
				SCREAMING_SNAKE_CASE__				:			Any						=		True


				def __init__(       self			:List[str]			,			lowerCAmelCase__			:Union[str, Any]=None			,			lowerCAmelCase__			:str=None			,			lowerCAmelCase__			:int=None			,			lowerCAmelCase__			:List[str]=32			,			**lowerCAmelCase__			:Optional[int]	)		->			Optional[int]:
										super().__init__(**lowerCAmelCase__	)

										if vision_config is None:
																__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		{}
																logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.'''	)

										if qformer_config is None:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{}
																logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.'''	)

										if text_config is None:
																__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{}
																logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''	)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		BlipaVisionConfig(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     str					=		BlipaQFormerConfig(**lowerCAmelCase__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		CONFIG_MAPPING[text_model_type](**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     int					=		self.text_config.tie_word_embeddings
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.text_config.is_encoder_decoder

										__SCREAMING_SNAKE_CASE						:     Any					=		num_query_tokens
										__SCREAMING_SNAKE_CASE						:     int					=		self.vision_config.hidden_size
										__SCREAMING_SNAKE_CASE						:     str					=		self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		1.0
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		0.02


				@classmethod
				def 		__magic_name__(       cls			:Union[str, Any]			,			lowerCAmelCase__			:BlipaVisionConfig			,			lowerCAmelCase__			:BlipaQFormerConfig			,			lowerCAmelCase__			:PretrainedConfig			,			**lowerCAmelCase__			:int			,			)		->			Optional[Any]:
										return cls(
										    vision_config=vision_config.to_dict()			,			qformer_config=qformer_config.to_dict()			,			text_config=text_config.to_dict()			,			**lowerCAmelCase__			,			)



				def 		__magic_name__(       self			:int	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Dict					=		copy.deepcopy(self.__dict__	)
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.vision_config.to_dict()
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.qformer_config.to_dict()
										__SCREAMING_SNAKE_CASE						:     Dict					=		self.text_config.to_dict()
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.__class__.model_type
										return output




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()	)
