




"""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


a         =      logging.get_logger(__name__)

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





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     '''blip_2_vision_model'''

 def __init__(			self	:	int   ,							_UpperCAmelCase	:	Tuple=1_408   ,							_UpperCAmelCase	:	Dict=6_144   ,							_UpperCAmelCase	:	Tuple=39   ,							_UpperCAmelCase	:	Dict=16   ,							_UpperCAmelCase	:	List[str]=224   ,							_UpperCAmelCase	:	Dict=14   ,							_UpperCAmelCase	:	Optional[Any]="gelu"   ,							_UpperCAmelCase	:	Any=0.0_0001   ,							_UpperCAmelCase	:	Tuple=0.0   ,							_UpperCAmelCase	:	Union[str, Any]=1E-1_0   ,							_UpperCAmelCase	:	Optional[Any]=True   ,							**_UpperCAmelCase	:	Optional[int]   ,							):
       super().__init__(**_UpperCAmelCase		)

       _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       lowerCAmelCase_							(			cls	:	List[Any]   ,							_UpperCAmelCase	:	Union[str, os.PathLike]   ,							**_UpperCAmelCase	:	List[Any]		):
       cls._set_token_in_kwargs(_UpperCAmelCase		)

       _A       ,   _A        =       cls.get_config_dict(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       # 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(_UpperCAmelCase   ,							**_UpperCAmelCase		)





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     '''blip_2_qformer'''

 def __init__(			self	:	int   ,							_UpperCAmelCase	:	int=30_522   ,							_UpperCAmelCase	:	Optional[Any]=768   ,							_UpperCAmelCase	:	Dict=12   ,							_UpperCAmelCase	:	int=12   ,							_UpperCAmelCase	:	Optional[int]=3_072   ,							_UpperCAmelCase	:	int="gelu"   ,							_UpperCAmelCase	:	int=0.1   ,							_UpperCAmelCase	:	int=0.1   ,							_UpperCAmelCase	:	Dict=512   ,							_UpperCAmelCase	:	int=0.02   ,							_UpperCAmelCase	:	List[Any]=1E-1_2   ,							_UpperCAmelCase	:	int=0   ,							_UpperCAmelCase	:	str="absolute"   ,							_UpperCAmelCase	:	str=2   ,							_UpperCAmelCase	:	List[Any]=1_408   ,							**_UpperCAmelCase	:	Any   ,							):
       super().__init__(pad_token_id=_UpperCAmelCase   ,							**_UpperCAmelCase		)

       _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       lowerCAmelCase_							(			cls	:	Optional[Any]   ,							_UpperCAmelCase	:	Union[str, os.PathLike]   ,							**_UpperCAmelCase	:	Optional[int]		):
       cls._set_token_in_kwargs(_UpperCAmelCase		)

       _A       ,   _A        =       cls.get_config_dict(_UpperCAmelCase   ,							**_UpperCAmelCase		)

       # 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(_UpperCAmelCase   ,							**_UpperCAmelCase		)





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     '''blip-2'''
 UpperCAmelCase   :					List[str]				  =     True

 def __init__(			self	:	List[Any]   ,							_UpperCAmelCase	:	Any=None   ,							_UpperCAmelCase	:	Union[str, Any]=None   ,							_UpperCAmelCase	:	Optional[Any]=None   ,							_UpperCAmelCase	:	List[str]=32   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       super().__init__(**_UpperCAmelCase		)

       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(**_UpperCAmelCase		)
       _A        =       BlipaQFormerConfig(**_UpperCAmelCase		)
       _A        =       text_config['model_type'] if 'model_type' in text_config else 'opt'
       _A        =       CONFIG_MAPPING[text_model_type](**_UpperCAmelCase		)

       _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       lowerCAmelCase_							(			cls	:	List[str]   ,							_UpperCAmelCase	:	BlipaVisionConfig   ,							_UpperCAmelCase	:	BlipaQFormerConfig   ,							_UpperCAmelCase	:	PretrainedConfig   ,							**_UpperCAmelCase	:	Tuple   ,							):
       return cls(
           vision_config=vision_config.to_dict()   ,							qformer_config=qformer_config.to_dict()   ,							text_config=text_config.to_dict()   ,							**_UpperCAmelCase   ,							)



 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _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







"""simple docstring"""

import argparse
import os

import torch

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)


a         =      {
    '''sample_size''': 32,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [32, 64],
    '''attention_head_dim''': 8,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 64,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 3,
    '''num_class_embeds''': 1_000,
    '''block_out_channels''': [192, 192 * 2, 192 * 3, 192 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''scale_shift''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''sample_size''': 256,
    '''in_channels''': 3,
    '''out_channels''': 3,
    '''layers_per_block''': 2,
    '''num_class_embeds''': None,
    '''block_out_channels''': [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
    '''attention_head_dim''': 64,
    '''down_block_types''': [
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''ResnetDownsampleBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
        '''AttnDownBlock2D''',
    ],
    '''up_block_types''': [
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''AttnUpBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
        '''ResnetUpsampleBlock2D''',
    ],
    '''resnet_time_scale_shift''': '''default''',
    '''upsample_type''': '''resnet''',
    '''downsample_type''': '''resnet''',
}

a         =      {
    '''num_train_timesteps''': 40,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 201,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}

a         =      {
    '''num_train_timesteps''': 151,
    '''sigma_min''': 0.0_0_2,
    '''sigma_max''': 8_0.0,
}






def 						_snake_case					( _snake_case							: Dict							)   -> int:






      '''simple docstring'''


      if isinstance(_snake_case    ,  _snake_case							):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise argparse.ArgumentTypeError('boolean value expected'							)






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: Tuple    ,  _snake_case							: Dict    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]=False							)   -> List[str]:






      '''simple docstring'''


      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.weight''']
      _A        =       checkpoint[F'''{old_prefix}.in_layers.2.bias''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.weight''']
      _A        =       checkpoint[F'''{old_prefix}.emb_layers.1.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.0.bias''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.weight''']
      _A        =       checkpoint[F'''{old_prefix}.out_layers.3.bias''']

      if has_skip:
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.weight''']
            _A        =       checkpoint[F'''{old_prefix}.skip_connection.bias''']

      return new_checkpoint






def 						_snake_case					( _snake_case							: List[Any]    ,  _snake_case							: int    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: int=None							)   -> Optional[int]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.weight'''].chunk(3    ,  dim=0							)
      _A       ,   _A       ,   _A        =       checkpoint[F'''{old_prefix}.qkv.bias'''].chunk(3    ,  dim=0							)

      _A        =       checkpoint[F'''{old_prefix}.norm.weight''']
      _A        =       checkpoint[F'''{old_prefix}.norm.bias''']

      _A        =       weight_q.squeeze(-1							).squeeze(-1							)
      _A        =       bias_q.squeeze(-1							).squeeze(-1							)
      _A        =       weight_k.squeeze(-1							).squeeze(-1							)
      _A        =       bias_k.squeeze(-1							).squeeze(-1							)
      _A        =       weight_v.squeeze(-1							).squeeze(-1							)
      _A        =       bias_v.squeeze(-1							).squeeze(-1							)

      _A        =       (
          checkpoint[F'''{old_prefix}.proj_out.weight'''].squeeze(-1							).squeeze(-1							)
      )
      _A        =       checkpoint[F'''{old_prefix}.proj_out.bias'''].squeeze(-1							).squeeze(-1							)

      return new_checkpoint






def 						_snake_case					( _snake_case							: str    ,  _snake_case							: Any							)   -> str:






      '''simple docstring'''


      _A        =       torch.load(_snake_case    ,  map_location='cpu'							)
      _A        =       {}

      _A        =       checkpoint['time_embed.0.weight']
      _A        =       checkpoint['time_embed.0.bias']
      _A        =       checkpoint['time_embed.2.weight']
      _A        =       checkpoint['time_embed.2.bias']

      if unet_config["num_class_embeds"] is not None:
            _A        =       checkpoint['label_emb.weight']

      _A        =       checkpoint['input_blocks.0.0.weight']
      _A        =       checkpoint['input_blocks.0.0.bias']

      _A        =       unet_config['down_block_types']
      _A        =       unet_config['layers_per_block']
      _A        =       unet_config['attention_head_dim']
      _A        =       unet_config['block_out_channels']
      _A        =       1
      _A        =       channels_list[0]

      for i, layer_type in enumerate(_snake_case							):
            _A        =       channels_list[i]
            _A        =       current_channels != prev_channels
            if layer_type == "ResnetDownsampleBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

            elif layer_type == "AttnDownBlock2D":
                  for j in range(_snake_case							):
                        _A        =       F'''down_blocks.{i}.resnets.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.0'''
                        _A        =       True if j == 0 and downsample_block_has_skip else False
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''down_blocks.{i}.attentions.{j}'''
                        _A        =       F'''input_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

            if i != len(_snake_case							) - 1:
                  _A        =       F'''down_blocks.{i}.downsamplers.0'''
                  _A        =       F'''input_blocks.{current_layer}.0'''
                  _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                  current_layer += 1

            _A        =       current_channels

      # hardcoded the mid-block for now
      _A        =       'mid_block.resnets.0'
      _A        =       'middle_block.0'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.attentions.0'
      _A        =       'middle_block.1'
      _A        =       convert_attention(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
      _A        =       'mid_block.resnets.1'
      _A        =       'middle_block.2'
      _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       0
      _A        =       unet_config['up_block_types']

      for i, layer_type in enumerate(_snake_case							):
            if layer_type == "ResnetUpsampleBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.1'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
            elif layer_type == "AttnUpBlock2D":
                  for j in range(layers_per_block + 1							):
                        _A        =       F'''up_blocks.{i}.resnets.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.0'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  has_skip=_snake_case							)
                        _A        =       F'''up_blocks.{i}.attentions.{j}'''
                        _A        =       F'''output_blocks.{current_layer}.1'''
                        _A        =       convert_attention(
                            _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)
                        current_layer += 1

                  if i != len(_snake_case							) - 1:
                        _A        =       F'''up_blocks.{i}.upsamplers.0'''
                        _A        =       F'''output_blocks.{current_layer-1}.2'''
                        _A        =       convert_resnet(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)

      _A        =       checkpoint['out.0.weight']
      _A        =       checkpoint['out.0.bias']
      _A        =       checkpoint['out.2.weight']
      _A        =       checkpoint['out.2.bias']

      return new_checkpoint


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()

       parser.add_argument('''--unet_path''', default=None, type=str, required=True, help='''Path to the unet.pt to convert.''')
       parser.add_argument(
           '''--dump_path''', default=None, type=str, required=True, help='''Path to output the converted UNet model.'''
       )
       parser.add_argument('''--class_cond''', default=True, type=str, help='''Whether the model is class-conditional.''')

       a         =      parser.parse_args()
       a         =      strabool(args.class_cond)

       a         =      os.path.basename(args.unet_path)
       print(F'''Checkpoint: {ckpt_name}''')

       # Get U-Net config
       if "imagenet64" in ckpt_name:
              a         =      IMAGENET_64_UNET_CONFIG
       elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      LSUN_256_UNET_CONFIG
       elif "test" in ckpt_name:
              a         =      TEST_UNET_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       if not args.class_cond:
              a         =      None

       a         =      con_pt_to_diffuser(args.unet_path, unet_config)

       a         =      UNetaDModel(**unet_config)
       image_unet.load_state_dict(converted_unet_ckpt)

       # Get scheduler config
       if "cd" in ckpt_name or "test" in ckpt_name:
              a         =      CD_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
              a         =      CT_IMAGENET_64_SCHEDULER_CONFIG
       elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
              a         =      CT_LSUN_256_SCHEDULER_CONFIG
       else:
              raise ValueError(F'''Checkpoint type {ckpt_name} is not currently supported.''')

       a         =      CMStochasticIterativeScheduler(**scheduler_config)

       a         =      ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
       consistency_model.save_pretrained(args.dump_path)

