






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 							_UpperCamelCase			(unittest.TestCase			):


      def  __UpperCAmelCase		(	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  __UpperCAmelCase		(	self					,						**__UpperCamelCase	)->	Optional[int]:
           return AutoTokenizer.from_pretrained(self.checkpoint					,						**__UpperCamelCase	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           shutil.rmtree(self.tmpdirname	)


      def  __UpperCAmelCase		(	self	)->	Tuple:
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BarkProcessor(tokenizer=__UpperCamelCase	)

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

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


      @slow
      def  __UpperCAmelCase		(	self	)->	Dict:
           __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  __UpperCAmelCase		(	self	)->	int:
           __lowerCAmelCase								=					BarkProcessor.from_pretrained(
               pretrained_processor_name_or_path=self.checkpoint					,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path					,						)

           __lowerCAmelCase								=					3_5
           __lowerCAmelCase								=					2
           __lowerCAmelCase								=					8

           __lowerCAmelCase								=					{
               "semantic_prompt": np.ones(__UpperCamelCase	),
               "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=__UpperCamelCase	)

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

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

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

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


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					self.get_tokenizer()

           __lowerCAmelCase								=					BarkProcessor(tokenizer=__UpperCamelCase	)

           __lowerCAmelCase								=					processor(text=self.input_string	)

           __lowerCAmelCase								=					tokenizer(
               self.input_string					,						padding="max_length"					,						max_length=2_5_6					,						add_special_tokens=__UpperCamelCase					,						return_attention_mask=__UpperCamelCase					,						return_token_type_ids=__UpperCamelCase					,						)

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














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			:					Union[str, Any]         =	logging.get_logger(__name__)

lowerCamelCase			:					Any         =	{
    '''Salesforce/instruct-blip-flan-t5''': '''https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json''',
}



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""instructblip_vision_model"""


      def __init__(	self					,						__UpperCamelCase=1_4_0_8					,						__UpperCamelCase=6_1_4_4					,						__UpperCamelCase=3_9					,						__UpperCamelCase=1_6					,						__UpperCamelCase=2_2_4					,						__UpperCamelCase=1_4					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=1e-6					,						__UpperCamelCase=0.0					,						__UpperCamelCase=1e-10					,						__UpperCamelCase=True					,						**__UpperCamelCase					,						)->	Union[str, Any]:
           super().__init__(**__UpperCamelCase	)

           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					patch_size
           __lowerCAmelCase								=					image_size
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					attention_dropout
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					qkv_bias


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						**__UpperCamelCase	)->	"PretrainedConfig":
           cls._set_token_in_kwargs(__UpperCamelCase	)

           __lowerCAmelCase			, __lowerCAmelCase								=					cls.get_config_dict(__UpperCamelCase					,						**__UpperCamelCase	)

           # get the vision config dict if we are loading from InstructBlipConfig
           if config_dict.get("model_type"	) == "instructblip":
                __lowerCAmelCase								=					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(__UpperCamelCase					,						**__UpperCamelCase	)



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""instructblip_qformer"""


      def __init__(	self					,						__UpperCamelCase=3_0_5_2_2					,						__UpperCamelCase=7_6_8					,						__UpperCamelCase=1_2					,						__UpperCamelCase=1_2					,						__UpperCamelCase=3_0_7_2					,						__UpperCamelCase="gelu"					,						__UpperCamelCase=0.1					,						__UpperCamelCase=0.1					,						__UpperCamelCase=5_1_2					,						__UpperCamelCase=0.0_2					,						__UpperCamelCase=1e-12					,						__UpperCamelCase=0					,						__UpperCamelCase="absolute"					,						__UpperCamelCase=2					,						__UpperCamelCase=1_4_0_8					,						**__UpperCamelCase					,						)->	Any:
           super().__init__(pad_token_id=__UpperCamelCase					,						**__UpperCamelCase	)

           __lowerCAmelCase								=					vocab_size
           __lowerCAmelCase								=					hidden_size
           __lowerCAmelCase								=					num_hidden_layers
           __lowerCAmelCase								=					num_attention_heads
           __lowerCAmelCase								=					hidden_act
           __lowerCAmelCase								=					intermediate_size
           __lowerCAmelCase								=					hidden_dropout_prob
           __lowerCAmelCase								=					attention_probs_dropout_prob
           __lowerCAmelCase								=					max_position_embeddings
           __lowerCAmelCase								=					initializer_range
           __lowerCAmelCase								=					layer_norm_eps
           __lowerCAmelCase								=					position_embedding_type
           __lowerCAmelCase								=					cross_attention_frequency
           __lowerCAmelCase								=					encoder_hidden_size


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						**__UpperCamelCase	)->	"PretrainedConfig":
           cls._set_token_in_kwargs(__UpperCamelCase	)

           __lowerCAmelCase			, __lowerCAmelCase								=					cls.get_config_dict(__UpperCamelCase					,						**__UpperCamelCase	)

           # get the qformer config dict if we are loading from InstructBlipConfig
           if config_dict.get("model_type"	) == "instructblip":
                __lowerCAmelCase								=					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(__UpperCamelCase					,						**__UpperCamelCase	)



class 							_UpperCamelCase			(a_			):
      snake_case_  =							"""instructblip"""
      snake_case_  =							True


      def __init__(	self					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=None					,						__UpperCamelCase=3_2					,						**__UpperCamelCase	)->	Tuple:
           super().__init__(**__UpperCamelCase	)

           if vision_config is None:
                __lowerCAmelCase								=					{}
                logger.info("vision_config is None. initializing the InstructBlipVisionConfig with default values."	)

           if qformer_config is None:
                __lowerCAmelCase								=					{}
                logger.info("qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."	)

           if text_config is None:
                __lowerCAmelCase								=					{}
                logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)."	)

           __lowerCAmelCase								=					InstructBlipVisionConfig(**__UpperCamelCase	)
           __lowerCAmelCase								=					InstructBlipQFormerConfig(**__UpperCamelCase	)
           __lowerCAmelCase								=					text_config["model_type"] if "model_type" in text_config else "opt"
           __lowerCAmelCase								=					CONFIG_MAPPING[text_model_type](**__UpperCamelCase	)

           __lowerCAmelCase								=					self.text_config.tie_word_embeddings
           __lowerCAmelCase								=					self.text_config.is_encoder_decoder

           __lowerCAmelCase								=					num_query_tokens
           __lowerCAmelCase								=					self.vision_config.hidden_size
           __lowerCAmelCase								=					self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
           __lowerCAmelCase								=					1.0
           __lowerCAmelCase								=					0.0_2


      @classmethod
      def  __UpperCAmelCase		(	cls					,						__UpperCamelCase					,						__UpperCamelCase					,						__UpperCamelCase					,						**__UpperCamelCase					,						)->	int:
           return cls(
               vision_config=vision_config.to_dict()					,						qformer_config=qformer_config.to_dict()					,						text_config=text_config.to_dict()					,						**__UpperCamelCase					,						)


      def  __UpperCAmelCase		(	self	)->	Dict:
           __lowerCAmelCase								=					copy.deepcopy(self.__dict__	)
           __lowerCAmelCase								=					self.vision_config.to_dict()
           __lowerCAmelCase								=					self.qformer_config.to_dict()
           __lowerCAmelCase								=					self.text_config.to_dict()
           __lowerCAmelCase								=					self.__class__.model_type
           return output






