


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


_SCREAMING_SNAKE_CASE			: str			      =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			: Optional[int]			      =    {
    'salesforce/blip2-opt-2.7b': 'https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json',
}

class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		'blip_2_vision_model'
    def __init__(       self			,					__snake_case=1_4_0_8			,					__snake_case=6_1_4_4			,					__snake_case=3_9			,					__snake_case=1_6			,					__snake_case=2_2_4			,					__snake_case=1_4			,					__snake_case="gelu"			,					__snake_case=0.0_0001			,					__snake_case=0.0			,					__snake_case=1E-10			,					__snake_case=True			,					**__snake_case			,					):
         super().__init__(**__snake_case    )

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




    @classmethod
    def 		a_			(       cls			,					__snake_case			,					**__snake_case    ):
         cls._set_token_in_kwargs(__snake_case    )

         snake_case ,  snake_case		 =				cls.get_config_dict(__snake_case			,					**__snake_case    )

         # get the vision config dict if we are loading from Blip2Config
         if config_dict.get('''model_type'''    ) == "blip-2":
              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(__snake_case			,					**__snake_case    )






class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		'blip_2_qformer'
    def __init__(       self			,					__snake_case=3_0_5_2_2			,					__snake_case=7_6_8			,					__snake_case=1_2			,					__snake_case=1_2			,					__snake_case=3_0_7_2			,					__snake_case="gelu"			,					__snake_case=0.1			,					__snake_case=0.1			,					__snake_case=5_1_2			,					__snake_case=0.02			,					__snake_case=1E-12			,					__snake_case=0			,					__snake_case="absolute"			,					__snake_case=2			,					__snake_case=1_4_0_8			,					**__snake_case			,					):
         super().__init__(pad_token_id=__snake_case			,					**__snake_case    )

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




    @classmethod
    def 		a_			(       cls			,					__snake_case			,					**__snake_case    ):
         cls._set_token_in_kwargs(__snake_case    )

         snake_case ,  snake_case		 =				cls.get_config_dict(__snake_case			,					**__snake_case    )

         # get the qformer config dict if we are loading from Blip2Config
         if config_dict.get('''model_type'''    ) == "blip-2":
              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(__snake_case			,					**__snake_case    )






class   A__					(    A__	):







    """simple docstring"""

    __magic_name__			  =		'blip-2'
    __magic_name__			  =		True
    def __init__(       self			,					__snake_case=None			,					__snake_case=None			,					__snake_case=None			,					__snake_case=3_2			,					**__snake_case    ):
         super().__init__(**__snake_case    )

         if vision_config is None:
              snake_case		 =				{}
              logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.'''    )

         if qformer_config is None:
              snake_case		 =				{}
              logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.'''    )

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

         snake_case		 =				BlipaVisionConfig(**__snake_case    )
         snake_case		 =				BlipaQFormerConfig(**__snake_case    )
         snake_case		 =				text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         snake_case		 =				CONFIG_MAPPING[text_model_type](**__snake_case    )

         snake_case		 =				self.text_config.tie_word_embeddings
         snake_case		 =				self.text_config.is_encoder_decoder

         snake_case		 =				num_query_tokens
         snake_case		 =				self.vision_config.hidden_size
         snake_case		 =				self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         snake_case		 =				1.0
         snake_case		 =				0.02
    @classmethod
    def 		a_			(       cls			,					__snake_case			,					__snake_case			,					__snake_case			,					**__snake_case			,					):
         return cls(
             vision_config=vision_config.to_dict()			,					qformer_config=qformer_config.to_dict()			,					text_config=text_config.to_dict()			,					**__snake_case			,					)




    def 		a_			(       self    ):
         snake_case		 =				copy.deepcopy(self.__dict__    )
         snake_case		 =				self.vision_config.to_dict()
         snake_case		 =				self.qformer_config.to_dict()
         snake_case		 =				self.text_config.to_dict()
         snake_case		 =				self.__class__.model_type
         return output












from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




