




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


_lowerCamelCase     :		Union[str, Any]								=       logging.get_logger(__name__)

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


class     UpperCamelCase_   (			lowerCAmelCase_					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'blip_2_vision_model'


    def __init__(      self				:     Tuple ,			UpperCAmelCase__				:     Dict=1_408 ,			UpperCAmelCase__				:     Tuple=6_144 ,			UpperCAmelCase__				:     Optional[Any]=39 ,			UpperCAmelCase__				:     Any=16 ,			UpperCAmelCase__				:     int=224 ,			UpperCAmelCase__				:     Any=14 ,			UpperCAmelCase__				:     Tuple="gelu" ,			UpperCAmelCase__				:     Dict=0.00001 ,			UpperCAmelCase__				:     Optional[Any]=0.0 ,			UpperCAmelCase__				:     List[Any]=1e-10 ,			UpperCAmelCase__				:     Dict=True ,			**UpperCAmelCase__				:     int ,			)						->int:





           '''simple docstring'''

           super().__init__(**__lowerCAmelCase)

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





    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     Optional[int] ,			UpperCAmelCase__				:     Union[str, os.PathLike] ,			**UpperCAmelCase__				:     Optional[Any])						->Any:





           '''simple docstring'''

           cls._set_token_in_kwargs(__lowerCAmelCase)

           A__			,							A__							=   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":
                  A__							=   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     UpperCamelCase_   (			lowerCAmelCase_					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'blip_2_qformer'


    def __init__(      self				:     str ,			UpperCAmelCase__				:     Dict=30_522 ,			UpperCAmelCase__				:     Tuple=768 ,			UpperCAmelCase__				:     List[str]=12 ,			UpperCAmelCase__				:     Union[str, Any]=12 ,			UpperCAmelCase__				:     Union[str, Any]=3_072 ,			UpperCAmelCase__				:     int="gelu" ,			UpperCAmelCase__				:     Tuple=0.1 ,			UpperCAmelCase__				:     List[Any]=0.1 ,			UpperCAmelCase__				:     str=512 ,			UpperCAmelCase__				:     Optional[Any]=0.02 ,			UpperCAmelCase__				:     List[Any]=1e-12 ,			UpperCAmelCase__				:     List[Any]=0 ,			UpperCAmelCase__				:     Optional[Any]="absolute" ,			UpperCAmelCase__				:     Union[str, Any]=2 ,			UpperCAmelCase__				:     Tuple=1_408 ,			**UpperCAmelCase__				:     Optional[int] ,			)						->int:





           '''simple docstring'''

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

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





    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     Union[str, Any] ,			UpperCAmelCase__				:     Union[str, os.PathLike] ,			**UpperCAmelCase__				:     Tuple)						->List[Any]:





           '''simple docstring'''

           cls._set_token_in_kwargs(__lowerCAmelCase)

           A__			,							A__							=   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":
                  A__							=   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     UpperCamelCase_   (			lowerCAmelCase_					):

    '''simple docstring'''






    UpperCAmelCase__							  =				'blip-2'
    UpperCAmelCase__							  =				True


    def __init__(      self				:     Any ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Union[str, Any]=None ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Any=32 ,			**UpperCAmelCase__				:     List[Any])						->int:





           '''simple docstring'''

           super().__init__(**__lowerCAmelCase)

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

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

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

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

           A__							=   self.text_config.tie_word_embeddings
           A__							=   self.text_config.is_encoder_decoder

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


    @classmethod
    def 	SCREAMING_SNAKE_CASE							(      cls				:     Optional[int] ,			UpperCAmelCase__				:     BlipaVisionConfig ,			UpperCAmelCase__				:     BlipaQFormerConfig ,			UpperCAmelCase__				:     PretrainedConfig ,			**UpperCAmelCase__				:     Dict ,			)						->int:





           '''simple docstring'''

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





    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[Any]:





           '''simple docstring'''

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








import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor







def SCREAMING_SNAKE_CASE    (	lowercase_						)       ->	str:



       """simple docstring"""

       if "cls_token" in name:
              A__							=   name.replace('''cls_token'''			,    '''vit.embeddings.cls_token'''						)
       if "mask_token" in name:
              A__							=   name.replace('''mask_token'''			,    '''decoder.mask_token'''						)
       if "decoder_pos_embed" in name:
              A__							=   name.replace('''decoder_pos_embed'''			,    '''decoder.decoder_pos_embed'''						)
       if "pos_embed" in name and "decoder" not in name:
              A__							=   name.replace('''pos_embed'''			,    '''vit.embeddings.position_embeddings'''						)
       if "patch_embed.proj" in name:
              A__							=   name.replace('''patch_embed.proj'''			,    '''vit.embeddings.patch_embeddings.projection'''						)
       if "patch_embed.norm" in name:
              A__							=   name.replace('''patch_embed.norm'''			,    '''vit.embeddings.norm'''						)
       if "decoder_blocks" in name:
              A__							=   name.replace('''decoder_blocks'''			,    '''decoder.decoder_layers'''						)
       if "blocks" in name:
              A__							=   name.replace('''blocks'''			,    '''vit.encoder.layer'''						)
       if "attn.proj" in name:
              A__							=   name.replace('''attn.proj'''			,    '''attention.output.dense'''						)
       if "attn" in name:
              A__							=   name.replace('''attn'''			,    '''attention.self'''						)
       if "norm1" in name:
              A__							=   name.replace('''norm1'''			,    '''layernorm_before'''						)
       if "norm2" in name:
              A__							=   name.replace('''norm2'''			,    '''layernorm_after'''						)
       if "mlp.fc1" in name:
              A__							=   name.replace('''mlp.fc1'''			,    '''intermediate.dense'''						)
       if "mlp.fc2" in name:
              A__							=   name.replace('''mlp.fc2'''			,    '''output.dense'''						)
       if "decoder_embed" in name:
              A__							=   name.replace('''decoder_embed'''			,    '''decoder.decoder_embed'''						)
       if "decoder_norm" in name:
              A__							=   name.replace('''decoder_norm'''			,    '''decoder.decoder_norm'''						)
       if "decoder_pred" in name:
              A__							=   name.replace('''decoder_pred'''			,    '''decoder.decoder_pred'''						)
       if "norm.weight" in name and "decoder" not in name:
              A__							=   name.replace('''norm.weight'''			,    '''vit.layernorm.weight'''						)
       if "norm.bias" in name and "decoder" not in name:
              A__							=   name.replace('''norm.bias'''			,    '''vit.layernorm.bias'''						)

       return name







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	str:



       """simple docstring"""

       for key in orig_state_dict.copy().keys():
              A__							=   orig_state_dict.pop(lowercase_						)

              if "qkv" in key:
                     A__							=   key.split('''.'''						)
                     A__							=   int(key_split[1]						)
                     if "decoder_blocks" in key:
                            A__							=   config.decoder_hidden_size
                            A__							=   '''decoder.decoder_layers.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]
                     else:
                            A__							=   config.hidden_size
                            A__							=   '''vit.encoder.layer.'''
                            if "weight" in key:
                                   A__							=   val[:dim, :]
                                   A__							=   val[dim : dim * 2, :]
                                   A__							=   val[-dim:, :]
                            elif "bias" in key:
                                   A__							=   val[:dim]
                                   A__							=   val[dim : dim * 2]
                                   A__							=   val[-dim:]

              else:
                     A__							=   val

       return orig_state_dict







def SCREAMING_SNAKE_CASE    (	lowercase_			,    lowercase_						)       ->	Tuple:



       """simple docstring"""

       A__							=   ViTMAEConfig()
       if "large" in checkpoint_url:
              A__							=   1_024
              A__							=   4_096
              A__							=   24
              A__							=   16
       elif "huge" in checkpoint_url:
              A__							=   14
              A__							=   1_280
              A__							=   5_120
              A__							=   32
              A__							=   16

       A__							=   ViTMAEForPreTraining(lowercase_						)

       A__							=   torch.hub.load_state_dict_from_url(lowercase_			,    map_location='''cpu'''						)['''model''']

       A__							=   ViTMAEImageProcessor(size=config.image_size						)

       A__							=   convert_state_dict(lowercase_			,    lowercase_						)

       model.load_state_dict(lowercase_						)
       model.eval()

       A__							=   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

       A__							=   Image.open(requests.get(lowercase_			,    stream=lowercase_						).raw						)
       A__							=   ViTMAEImageProcessor(size=config.image_size						)
       A__							=   image_processor(images=lowercase_			,    return_tensors='''pt'''						)

       # forward pass
       torch.manual_seed(2						)
       A__							=   model(**lowercase_						)
       A__							=   outputs.logits

       if "large" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]						)
       elif "huge" in checkpoint_url:
              A__							=   torch.tensor(
                  [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]						)
       else:
              A__							=   torch.tensor(
                  [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]						)

       # verify logits
       assert torch.allclose(logits[0, :3, :3]			,    lowercase_			,    atol=1E-4						)

       print(f"""Saving model to {pytorch_dump_folder_path}"""						)
       model.save_pretrained(lowercase_						)

       print(f"""Saving image processor to {pytorch_dump_folder_path}"""						)
       image_processor.save_pretrained(lowercase_						)


if __name__ == "__main__":
  _lowerCamelCase     :		Any								=       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--checkpoint_url""",
      default="""https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth""",
      type=str,
      help="""URL of the checkpoint you'd like to convert.""",
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
  )

  _lowerCamelCase     :		Optional[int]								=       parser.parse_args()
  convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


