



from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	_snake_case							(			__a      ):






   '''simple docstring'''






   A__	:      Optional[Any]    =		['''image_processor''', '''tokenizer''']
   A__	:      int    =		'''BlipImageProcessor'''
   A__	:      Any    =		'''AutoTokenizer'''


   def __init__(  self:			List[str]   ,lowerCamelCase_:			int   ,lowerCamelCase_:			Optional[int]				)     ->	Dict:
       UpperCAmelCase_			:    List[str]   						= False
       super().__init__(lowerCAmelCase__   ,lowerCAmelCase__				)
       UpperCAmelCase_			:    List[str]   						= self.image_processor


   def __call__(  self:			Tuple   ,lowerCamelCase_:			Any = None   ,lowerCamelCase_:			Any = None   ,lowerCamelCase_:			Optional[Any] = True   ,lowerCamelCase_:			str = False   ,lowerCamelCase_:			Optional[Any] = None   ,lowerCamelCase_:			List[Any] = None   ,lowerCamelCase_:			List[str] = 0   ,lowerCamelCase_:			List[str] = None   ,lowerCamelCase_:			List[str] = None   ,lowerCamelCase_:			Tuple = False   ,lowerCamelCase_:			Dict = False   ,lowerCamelCase_:			Optional[int] = False   ,lowerCamelCase_:			Optional[Any] = False   ,lowerCamelCase_:			Tuple = False   ,lowerCamelCase_:			Dict = True   ,lowerCamelCase_:			Union[str, Any] = None   ,**lowerCamelCase_:			List[Any]   ,)     ->	Optional[int]:
       if images is None and text is None:
           raise ValueError("""You have to specify either images or text."""				)

       # Get only text
       if images is None:
           UpperCAmelCase_			:    List[str]   						= self.tokenizer
           UpperCAmelCase_			:    List[str]   						= self.tokenizer(
               text=lowerCAmelCase__   ,add_special_tokens=lowerCAmelCase__   ,padding=lowerCAmelCase__   ,truncation=lowerCAmelCase__   ,max_length=lowerCAmelCase__   ,stride=lowerCAmelCase__   ,pad_to_multiple_of=lowerCAmelCase__   ,return_attention_mask=lowerCAmelCase__   ,return_overflowing_tokens=lowerCAmelCase__   ,return_special_tokens_mask=lowerCAmelCase__   ,return_offsets_mapping=lowerCAmelCase__   ,return_token_type_ids=lowerCAmelCase__   ,return_length=lowerCAmelCase__   ,verbose=lowerCAmelCase__   ,return_tensors=lowerCAmelCase__   ,**lowerCAmelCase__   ,)
           return text_encoding

       # add pixel_values
       UpperCAmelCase_			:    str   						= self.image_processor(lowerCAmelCase__   ,return_tensors=lowerCAmelCase__				)

       if text is not None:
           UpperCAmelCase_			:    List[Any]   						= self.tokenizer(
               text=lowerCAmelCase__   ,add_special_tokens=lowerCAmelCase__   ,padding=lowerCAmelCase__   ,truncation=lowerCAmelCase__   ,max_length=lowerCAmelCase__   ,stride=lowerCAmelCase__   ,pad_to_multiple_of=lowerCAmelCase__   ,return_attention_mask=lowerCAmelCase__   ,return_overflowing_tokens=lowerCAmelCase__   ,return_special_tokens_mask=lowerCAmelCase__   ,return_offsets_mapping=lowerCAmelCase__   ,return_token_type_ids=lowerCAmelCase__   ,return_length=lowerCAmelCase__   ,verbose=lowerCAmelCase__   ,return_tensors=lowerCAmelCase__   ,**lowerCAmelCase__   ,)
       else:
           UpperCAmelCase_			:    Optional[Any]   						= None

       if text_encoding is not None:
           encoding_image_processor.update(lowerCAmelCase__				)

       return encoding_image_processor


   def 	A__  (  self:			Optional[Any]   ,*lowerCamelCase_:			List[str]   ,**lowerCamelCase_:			List[str]				)     ->	Optional[Any]:
       return self.tokenizer.batch_decode(*lowerCAmelCase__   ,**lowerCAmelCase__				)


   def 	A__  (  self:			str   ,*lowerCamelCase_:			int   ,**lowerCamelCase_:			Union[str, Any]				)     ->	Tuple:
       return self.tokenizer.decode(*lowerCAmelCase__   ,**lowerCAmelCase__				)






   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 	A__  (  self:			int				)     ->	Union[str, Any]:
       UpperCAmelCase_			:    List[Any]   						= self.tokenizer.model_input_names
       UpperCAmelCase_			:    List[str]   						= self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)


"""simple docstring"""





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


__magic_name__       =			logging.get_logger(__name__)

__magic_name__       =			{
    "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							:  Optional[Any]						   =		'''blip_2_vision_model'''




		def __init__(     self							,  lowerCAmelCase__=1_4_0_8							,  lowerCAmelCase__=6_1_4_4							,  lowerCAmelCase__=3_9							,  lowerCAmelCase__=1_6							,  lowerCAmelCase__=2_2_4							,  lowerCAmelCase__=1_4							,  lowerCAmelCase__="gelu"							,  lowerCAmelCase__=0.0_00_01							,  lowerCAmelCase__=0.0							,  lowerCAmelCase__=1E-10							,  lowerCAmelCase__=True							,  **lowerCAmelCase__							,  ):
				super().__init__(**lowerCAmelCase__)

				__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 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = 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            = 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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Tuple						   =		'''blip_2_qformer'''




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

				__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 	snake_case_     (     cls							,  lowerCAmelCase__							,  **lowerCAmelCase__):
				cls._set_token_in_kwargs(lowerCAmelCase__)

				__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE            = 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            = 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 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  Optional[Any]						   =		'''blip-2'''
		__lowercase							:  Any						   =		True




		def __init__(     self							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=None							,  lowerCAmelCase__=3_2							,  **lowerCAmelCase__):
				super().__init__(**lowerCAmelCase__)

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

				if qformer_config is None:
						__SCREAMING_SNAKE_CASE            = {}
						logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig 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            = BlipaVisionConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = BlipaQFormerConfig(**lowerCAmelCase__)
				__SCREAMING_SNAKE_CASE            = text_config["""model_type"""] if """model_type""" in text_config else """opt"""
				__SCREAMING_SNAKE_CASE            = CONFIG_MAPPING[text_model_type](**lowerCAmelCase__)

				__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 	snake_case_     (     cls							,  lowerCAmelCase__							,  lowerCAmelCase__							,  lowerCAmelCase__							,  **lowerCAmelCase__							,  ):
				return cls(
				    vision_config=vision_config.to_dict()							,  qformer_config=qformer_config.to_dict()							,  text_config=text_config.to_dict()							,  **lowerCAmelCase__							,  )




		def 	snake_case_     (     self):
				__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




