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


_lowercase				:   Tuple   				=logging.get_logger(__name__)

_lowercase				:   Tuple   				={
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}



class 			SCREAMING_SNAKE_CASE_			(       _a      ):
  '''simple docstring'''


  lowercase		:  Dict							  =					"""blip_2_vision_model"""







  def __init__(					self     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       Dict=14_08   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=61_44   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=39   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=16   ,						SCREAMING_SNAKE_CASE__     :       int=2_24   ,						SCREAMING_SNAKE_CASE__     :       Dict=14   ,						SCREAMING_SNAKE_CASE__     :       str="gelu"   ,						SCREAMING_SNAKE_CASE__     :       int=0.0_0_0_0_1   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=0.0   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=1e-10   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=True   ,						**SCREAMING_SNAKE_CASE__     :       List[Any]   ,						)     ->					List[Any]:
         super().__init__(**SCREAMING_SNAKE_CASE__  )

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







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(					cls     :       str   ,						SCREAMING_SNAKE_CASE__     :       int   ,						**SCREAMING_SNAKE_CASE__     :       str  )     ->					"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__  )

         A    :  str								=cls.get_config_dict(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

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







class 			SCREAMING_SNAKE_CASE_			(       _a      ):
  '''simple docstring'''


  lowercase		:  Union[str, Any]							  =					"""blip_2_qformer"""







  def __init__(					self     :       List[str]   ,						SCREAMING_SNAKE_CASE__     :       List[str]=3_05_22   ,						SCREAMING_SNAKE_CASE__     :       Dict=7_68   ,						SCREAMING_SNAKE_CASE__     :       Tuple=12   ,						SCREAMING_SNAKE_CASE__     :       List[Any]=12   ,						SCREAMING_SNAKE_CASE__     :       str=30_72   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]="gelu"   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=0.1   ,						SCREAMING_SNAKE_CASE__     :       Dict=0.1   ,						SCREAMING_SNAKE_CASE__     :       Union[str, Any]=5_12   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=0.0_2   ,						SCREAMING_SNAKE_CASE__     :       Any=1e-12   ,						SCREAMING_SNAKE_CASE__     :       Any=0   ,						SCREAMING_SNAKE_CASE__     :       str="absolute"   ,						SCREAMING_SNAKE_CASE__     :       int=2   ,						SCREAMING_SNAKE_CASE__     :       Optional[Any]=14_08   ,						**SCREAMING_SNAKE_CASE__     :       str   ,						)     ->					Optional[Any]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

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







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(					cls     :       str   ,						SCREAMING_SNAKE_CASE__     :       Any   ,						**SCREAMING_SNAKE_CASE__     :       Union[str, Any]  )     ->					"PretrainedConfig":
         cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__  )

         A    :  List[str]								=cls.get_config_dict(SCREAMING_SNAKE_CASE__   ,						**SCREAMING_SNAKE_CASE__  )

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







class 			SCREAMING_SNAKE_CASE_			(       _a      ):
  '''simple docstring'''


  lowercase		:  Dict							  =					"""blip-2"""
  lowercase		:  Optional[int]							  =					True







  def __init__(					self     :       List[Any]   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=None   ,						SCREAMING_SNAKE_CASE__     :       Tuple=None   ,						SCREAMING_SNAKE_CASE__     :       Optional[int]=None   ,						SCREAMING_SNAKE_CASE__     :       Dict=32   ,						**SCREAMING_SNAKE_CASE__     :       Optional[int]  )     ->					Optional[int]:
         super().__init__(**SCREAMING_SNAKE_CASE__  )

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

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

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

         A    :  Dict								=BlipaVisionConfig(**SCREAMING_SNAKE_CASE__  )
         A    :  List[Any]								=BlipaQFormerConfig(**SCREAMING_SNAKE_CASE__  )
         A    :  Union[str, Any]								=text_config['model_type'] if 'model_type' in text_config else 'opt'
         A    :  List[Any]								=CONFIG_MAPPING[text_model_type](**SCREAMING_SNAKE_CASE__  )

         A    :  Optional[int]								=self.text_config.tie_word_embeddings
         A    :  Dict								=self.text_config.is_encoder_decoder

         A    :  List[str]								=num_query_tokens
         A    :  List[str]								=self.vision_config.hidden_size
         A    :  List[str]								=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         A    :  Optional[int]								=1.0
         A    :  Union[str, Any]								=0.0_2







  @classmethod
  def 		SCREAMING_SNAKE_CASE_		(					cls     :       Union[str, Any]   ,						SCREAMING_SNAKE_CASE__     :       Dict   ,						SCREAMING_SNAKE_CASE__     :       List[Any]   ,						SCREAMING_SNAKE_CASE__     :       int   ,						**SCREAMING_SNAKE_CASE__     :       Optional[Any]   ,						)     ->					Union[str, Any]:
         return cls(
             vision_config=vision_config.to_dict()   ,						qformer_config=qformer_config.to_dict()   ,						text_config=text_config.to_dict()   ,						**SCREAMING_SNAKE_CASE__   ,						)







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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE			: Optional[Any]				   =					{
    "configuration_conditional_detr": [
        "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ConditionalDetrConfig",
        "ConditionalDetrOnnxConfig",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Union[str, Any]				   =					["ConditionalDetrFeatureExtractor"]
 SCREAMING_SNAKE_CASE			: Optional[Any]				   =					["ConditionalDetrImageProcessor"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: Any				   =					[
     "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
     "ConditionalDetrForObjectDetection",
     "ConditionalDetrForSegmentation",
     "ConditionalDetrModel",
     "ConditionalDetrPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 SCREAMING_SNAKE_CASE			: str				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


