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



class     _A   (    __UpperCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						="instructblip_vision_model"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=1408   ,				SCREAMING_SNAKE_CASE_=6144   ,				SCREAMING_SNAKE_CASE_=39   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=14   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=1E-6   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=1E-10   ,				SCREAMING_SNAKE_CASE_=True   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**lowerCAmelCase_	)

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






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       cls._set_token_in_kwargs(lowerCAmelCase_	)

       UpperCamelCase__						,       UpperCamelCase__										=      cls.get_config_dict(lowerCAmelCase_   ,				**lowerCAmelCase_	)

       # get the vision config dict if we are loading from InstructBlipConfig
       if config_dict.get('''model_type'''	) == "instructblip":
            UpperCamelCase__										=      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     _A   (    __UpperCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	Optional[int]						="instructblip_qformer"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=3_0522   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=0.1   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=0   ,				SCREAMING_SNAKE_CASE_="absolute"   ,				SCREAMING_SNAKE_CASE_=2   ,				SCREAMING_SNAKE_CASE_=1408   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      List[Any]:





       '''simple docstring'''





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

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






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       cls._set_token_in_kwargs(lowerCAmelCase_	)

       UpperCamelCase__						,       UpperCamelCase__										=      cls.get_config_dict(lowerCAmelCase_   ,				**lowerCAmelCase_	)

       # get the qformer config dict if we are loading from InstructBlipConfig
       if config_dict.get('''model_type'''	) == "instructblip":
            UpperCamelCase__										=      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     _A   (    __UpperCAmelCase							):
  SCREAMING_SNAKE_CASE_	:	str						="instructblip"
  SCREAMING_SNAKE_CASE_	:	List[Any]						=True






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=32   ,				**SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       super().__init__(**lowerCAmelCase_	)

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

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

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

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

       UpperCamelCase__										=      self.text_config.tie_word_embeddings
       UpperCamelCase__										=      self.text_config.is_encoder_decoder

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






  @classmethod
  def 		_a					(cls   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Any:





       '''simple docstring'''





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






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





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




from datetime import datetime

import matplotlib.pyplot as plt
import torch







def  __UpperCamelCase    (						A							):
     for param in module.parameters():
          UpperCamelCase__										=      False







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      '''cuda''' if torch.cuda.is_available() else '''cpu'''
     if torch.backends.mps.is_available() and torch.backends.mps.is_built():
          UpperCamelCase__										=      '''mps'''
     if device == "mps":
          print(
              '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
              ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
              ''' with generations.'''							)
     return device







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      plt.imshow(A							)
     fig.axes.get_xaxis().set_visible(A							)
     fig.axes.get_yaxis().set_visible(A							)
     plt.show()







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      datetime.now()
     UpperCamelCase__										=      current_time.strftime('''%H:%M:%S'''							)
     return timestamp



