





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


_A					:				Dict				=			logging.get_logger(__name__)

_A					:				int				=			{
    """Salesforce/instruct-blip-flan-t5""": """https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json""",
}



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		Union[str, Any]				=	"""instructblip_vision_model"""



    def __init__(       self   ,				A_=14_08   ,				A_=61_44   ,				A_=39   ,				A_=16   ,				A_=2_24   ,				A_=14   ,				A_="gelu"   ,				A_=1E-6   ,				A_=0.0   ,				A_=1E-10   ,				A_=True   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(**A_     )

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




    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        cls._set_token_in_kwargs(A_     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cls.get_config_dict(A_   ,				**A_     )

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





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		List[Any]				=	"""instructblip_qformer"""



    def __init__(       self   ,				A_=3_05_22   ,				A_=7_68   ,				A_=12   ,				A_=12   ,				A_=30_72   ,				A_="gelu"   ,				A_=0.1   ,				A_=0.1   ,				A_=5_12   ,				A_=0.02   ,				A_=1E-12   ,				A_=0   ,				A_="absolute"   ,				A_=2   ,				A_=14_08   ,				**A_   ,				):





        '''simple docstring'''

        super().__init__(pad_token_id=A_   ,				**A_     )

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




    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				**A_     ):





        '''simple docstring'''

        cls._set_token_in_kwargs(A_     )

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cls.get_config_dict(A_   ,				**A_     )

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





class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    lowerCamelCase__    :		str				=	"""instructblip"""
    lowerCamelCase__    :		List[Any]				=	True



    def __init__(       self   ,				A_=None   ,				A_=None   ,				A_=None   ,				A_=32   ,				**A_     ):





        '''simple docstring'''

        super().__init__(**A_     )

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

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

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

        SCREAMING_SNAKE_CASE__											=			InstructBlipVisionConfig(**A_     )
        SCREAMING_SNAKE_CASE__											=			InstructBlipQFormerConfig(**A_     )
        SCREAMING_SNAKE_CASE__											=			text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
        SCREAMING_SNAKE_CASE__											=			CONFIG_MAPPING[text_model_type](**A_     )

        SCREAMING_SNAKE_CASE__											=			self.text_config.tie_word_embeddings
        SCREAMING_SNAKE_CASE__											=			self.text_config.is_encoder_decoder

        SCREAMING_SNAKE_CASE__											=			num_query_tokens
        SCREAMING_SNAKE_CASE__											=			self.vision_config.hidden_size
        SCREAMING_SNAKE_CASE__											=			self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
        SCREAMING_SNAKE_CASE__											=			1.0
        SCREAMING_SNAKE_CASE__											=			0.02



    @classmethod
    def  lowercase_					(       cls   ,				A_   ,				A_   ,				A_   ,				**A_   ,				):





        '''simple docstring'''

        return cls(
            vision_config=vision_config.to_dict()   ,				qformer_config=qformer_config.to_dict()   ,				text_config=text_config.to_dict()   ,				**A_   ,				)




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			copy.deepcopy(self.__dict__     )
        SCREAMING_SNAKE_CASE__											=			self.vision_config.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.qformer_config.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.text_config.to_dict()
        SCREAMING_SNAKE_CASE__											=			self.__class__.model_type
        return output




'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


UpperCAmelCase__  			=				{
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				[
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



