

import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase 	=   '''pcuenq/k-upscaler'''




def      UpperCAmelCase__							(     _A :					Optional[Any]      , _A :					List[str]      , *, _A :					int      , _A :					List[Any]				):





  '''simple docstring'''







  a__    ={
      # norm1
      F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
      F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
      # conv1
      F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
      F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
      # norm2
      F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
      F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
      # conv2
      F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
      F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
  }

  if resnet.conv_shortcut is not None:
    rv.update(
        {
            F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
        }				)

  return rv




def      UpperCAmelCase__							(     _A :					str      , *, _A :					Optional[Any]      , _A :					int				):





  '''simple docstring'''







  a__, a__, a__    =checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3      , dim=0				)
  a__, a__, a__    =checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3      , dim=0				)
  a__    ={
      # norm
      F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
      F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
      # to_q
      F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1				).squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
      # to_k
      F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1				).squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
      # to_v
      F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1				).squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
      # to_out
      F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
      .squeeze(-1				)
      .squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
  }

  return rv




def      UpperCAmelCase__							(     _A :					List[str]      , *, _A :					int      , _A :					str      , _A :					List[str]				):





  '''simple docstring'''







  a__, a__    =checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2      , dim=0				)
  a__, a__    =checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2      , dim=0				)

  a__    ={
      # norm2 (ada groupnorm)
      F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
          F"""{attention_prefix}.norm_dec.mapper.weight"""
      ],
      F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
          F"""{attention_prefix}.norm_dec.mapper.bias"""
      ],
      # layernorm on encoder_hidden_state
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
          F"""{attention_prefix}.norm_enc.weight"""
      ],
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
          F"""{attention_prefix}.norm_enc.bias"""
      ],
      # to_q
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
          F"""{attention_prefix}.q_proj.weight"""
      ]
      .squeeze(-1				)
      .squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
          F"""{attention_prefix}.q_proj.bias"""
      ],
      # to_k
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1				).squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
      # to_v
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1				).squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
      # to_out
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
          F"""{attention_prefix}.out_proj.weight"""
      ]
      .squeeze(-1				)
      .squeeze(-1				),
      F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
          F"""{attention_prefix}.out_proj.bias"""
      ],
  }

  return rv




def      UpperCAmelCase__							(     _A :					Tuple      , _A :					Union[str, Any]      , _A :					Dict      , _A :					List[Any]				):





  '''simple docstring'''







  a__    ='''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
  a__    =F"""{block_prefix}.{block_idx}"""

  a__    ={}

  if not hasattr(_A      , '''attentions'''				):
    a__    =1  # resnet only
  elif not block.attentions[0].add_self_attention:
    a__    =2  # resnet -> cross-attention
  else:
    a__    =3  # resnet -> self-attention -> cross-attention)

  for resnet_idx, resnet in enumerate(block.resnets				):
    # diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
    a__    =F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
    a__    =n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
    a__    =F"""{block_prefix}.{idx}""" if block_type == '''up''' else F"""{block_prefix}.{idx}"""

    diffusers_checkpoint.update(
        resnet_to_diffusers_checkpoint(
            _A      , _A      , diffusers_resnet_prefix=_A      , resnet_prefix=_A				)				)

  if hasattr(_A      , '''attentions'''				):
    for attention_idx, attention in enumerate(block.attentions				):
      a__    =F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
      a__    =n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
      a__    =F"""{block_prefix}.{idx}"""
      a__    =F"""{block_prefix}.{idx }"""
      a__    =1 if not attention.add_self_attention else 2
      a__    =(
          n * attention_idx + cross_attention_index
          if block_type == '''up'''
          else n * attention_idx + cross_attention_index + 1
      )
      a__    =F"""{block_prefix}.{idx }"""

      diffusers_checkpoint.update(
          cross_attn_to_diffusers_checkpoint(
              _A      , diffusers_attention_prefix=_A      , diffusers_attention_index=2      , attention_prefix=_A      , )				)

      if attention.add_self_attention is True:
        diffusers_checkpoint.update(
            self_attn_to_diffusers_checkpoint(
                _A      , diffusers_attention_prefix=_A      , attention_prefix=_A      , )				)

  return diffusers_checkpoint




def      UpperCAmelCase__							(     _A :					str      , _A :					Tuple				):





  '''simple docstring'''







  a__    ={}

  # pre-processing
  diffusers_checkpoint.update(
      {
          '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
          '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
      }				)

  # timestep and class embedding
  diffusers_checkpoint.update(
      {
          '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1				),
          '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
          '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
          '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
          '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
          '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
      }				)

  # down_blocks
  for down_block_idx, down_block in enumerate(model.down_blocks				):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A      , _A      , _A      , '''down'''				)				)

  # up_blocks
  for up_block_idx, up_block in enumerate(model.up_blocks				):
    diffusers_checkpoint.update(block_to_diffusers_checkpoint(_A      , _A      , _A      , '''up'''				)				)

  # post-processing
  diffusers_checkpoint.update(
      {
          '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
          '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
      }				)

  return diffusers_checkpoint




def      UpperCAmelCase__							(     _A :					int				):





  '''simple docstring'''







  a__    =original_config['''input_channels'''] + original_config['''unet_cond_dim''']
  a__    =original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

  a__    =original_config['''channels''']

  assert (
      len(set(original_config['''depths''']				)				) == 1
  ), "UNet2DConditionModel currently do not support blocks with different number of layers"
  a__    =original_config['''depths'''][0]

  a__    =original_config['''mapping_cond_dim''']
  a__    =original_config['''cross_cond_dim''']

  a__    =[]
  a__    =[]
  for s, c in zip(original_config['''self_attn_depths''']      , original_config['''cross_attn_depths''']				):
    if s:
      a__    ='''self'''
      a__    ='''cross''' if c else None
    elif c:
      a__    ='''cross'''
      a__    =None
    else:
      a__    =None
      a__    =None
    attna_types.append(_A				)
    attna_types.append(_A				)

  a__    =UNetaDConditionModel(
      in_channels=_A      , out_channels=_A      , down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')      , mid_block_type=_A      , up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')      , block_out_channels=_A      , layers_per_block=_A      , act_fn='''gelu'''      , norm_num_groups=_A      , cross_attention_dim=_A      , attention_head_dim=64      , time_cond_proj_dim=_A      , resnet_time_scale_shift='''scale_shift'''      , time_embedding_type='''fourier'''      , timestep_post_act='''gelu'''      , conv_in_kernel=1      , conv_out_kernel=1      , )

  return unet




def      UpperCAmelCase__							(     _A :					List[Any]				):





  '''simple docstring'''







  a__    =torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''				)

  a__    =huggingface_hub.hf_hub_download(_A      , '''config_laion_text_cond_latent_upscaler_2.json'''				)
  a__    =huggingface_hub.hf_hub_download(
      _A      , '''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''				)
  print(F"""loading original model configuration from {orig_config_path}"""				)
  print(F"""loading original model checkpoint from {orig_weights_path}"""				)

  print('''converting to diffusers unet'''				)
  a__    =K.config.load_config(open(_A				)				)['''model''']
  a__    =unet_model_from_original_config(_A				)

  a__    =torch.load(_A      , map_location=_A				)['''model_ema''']
  a__    =unet_to_diffusers_checkpoint(_A      , _A				)

  model.load_state_dict(_A      , strict=_A				)
  model.save_pretrained(args.dump_path				)
  print(F"""saving converted unet model in {args.dump_path}"""				)


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

       parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
       lowerCamelCase 	=   parser.parse_args()

       main(args)



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase 	=   logging.get_logger(__name__)

lowerCamelCase 	=   {
    '''facebook/sam-vit-huge''': '''https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json''',
    '''facebook/sam-vit-large''': '''https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json''',
    '''facebook/sam-vit-base''': '''https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json''',
}







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_=1024,       lowercase_=16,       lowercase_=16,       lowercase_=4,       lowercase_="gelu",       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =image_size
     a__    =patch_size
     a__    =image_size // patch_size
     a__    =mask_input_channels
     a__    =num_point_embeddings
     a__    =hidden_act
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=256,       lowercase_="relu",       lowercase_=2048,       lowercase_=2,       lowercase_=8,       lowercase_=2,       lowercase_=3,       lowercase_=3,       lowercase_=256,       lowercase_=1E-6,       **lowercase_,       )				->       int:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =hidden_size
     a__    =hidden_act
     a__    =mlp_dim
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =attention_downsample_rate
     a__    =num_multimask_outputs
     a__    =iou_head_depth
     a__    =iou_head_hidden_dim
     a__    =layer_norm_eps







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   def __init__(							self,       lowercase_=768,       lowercase_=256,       lowercase_=12,       lowercase_=12,       lowercase_=3,       lowercase_=1024,       lowercase_=16,       lowercase_="gelu",       lowercase_=1E-06,       lowercase_=0.0,       lowercase_=1E-10,       lowercase_=True,       lowercase_=4.0,       lowercase_=True,       lowercase_=True,       lowercase_=14,       lowercase_=[2, 5, 8, 11],       lowercase_=128,       lowercase_=None,       **lowercase_,       )				->       Union[str, Any]:






     """simple docstring"""






     super().__init__(**lowercase_			)

     a__    =hidden_size
     a__    =output_channels
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =num_channels
     a__    =image_size
     a__    =patch_size
     a__    =hidden_act
     a__    =layer_norm_eps
     a__    =attention_dropout
     a__    =initializer_range
     a__    =qkv_bias
     a__    =mlp_ratio
     a__    =use_abs_pos
     a__    =use_rel_pos
     a__    =window_size
     a__    =global_attn_indexes
     a__    =num_pos_feats
     a__    =int(hidden_size * mlp_ratio			) if mlp_dim is None else mlp_dim







class 		__magic_name__     (				lowerCamelCase__				):

   '''simple docstring'''




   lowerCamelCase__					:      Optional[int]        =  'sam'
   lowerCamelCase__					:      str        =  True



   def __init__(							self,       lowercase_=None,       lowercase_=None,       lowercase_=None,       lowercase_=0.02,       **lowercase_,       )				->       Tuple:






     """simple docstring"""






     super().__init__(**lowercase_			)
     a__    =vision_config if vision_config is not None else {}
     a__    =prompt_encoder_config if prompt_encoder_config is not None else {}
     a__    =mask_decoder_config if mask_decoder_config is not None else {}

     if isinstance(lowercase_,       lowercase_			):
       a__    =vision_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =prompt_encoder_config.to_dict()
     if isinstance(lowercase_,       lowercase_			):
       a__    =mask_decoder_config.to_dict()

     a__    =SamVisionConfig(**lowercase_			)
     a__    =SamPromptEncoderConfig(**lowercase_			)
     a__    =SamMaskDecoderConfig(**lowercase_			)
     a__    =initializer_range



   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =copy.deepcopy(self.__dict__			)
     a__    =self.vision_config.to_dict()
     a__    =self.prompt_encoder_config.to_dict()
     a__    =self.mask_decoder_config.to_dict()
     a__    =self.__class__.model_type
     return output
