
'''simple docstring'''





import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


a__		: Any  							=							"pcuenq/k-upscaler"





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	*, UpperCAmelCase   ,	UpperCAmelCase						)->      int:







      """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 							snake_case				(		UpperCAmelCase   ,	*, UpperCAmelCase   ,	UpperCAmelCase						)->      Optional[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 							snake_case				(		UpperCAmelCase   ,	*, UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """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 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      List[str]:







      """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(UpperCAmelCase   ,	'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(
                    UpperCAmelCase   ,	UpperCAmelCase   ,	diffusers_resnet_prefix=UpperCAmelCase   ,	resnet_prefix=UpperCAmelCase						)						)

      if hasattr(UpperCAmelCase   ,	'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(
                          UpperCAmelCase   ,	diffusers_attention_prefix=UpperCAmelCase   ,	diffusers_attention_index=2   ,	attention_prefix=UpperCAmelCase   ,	)						)

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

      return diffusers_checkpoint





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      Any:







      """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(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	'down'						)						)

      # up_blocks
      for up_block_idx, up_block in enumerate(model.up_blocks						):
            diffusers_checkpoint.update(block_to_diffusers_checkpoint(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase   ,	'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 							snake_case				(		UpperCAmelCase						)->      Optional[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(UpperCAmelCase						)
            attna_types.append(UpperCAmelCase						)

      __A													=      UNetaDConditionModel(
          in_channels=UpperCAmelCase   ,	out_channels=UpperCAmelCase   ,	down_block_types=('KDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D', 'KCrossAttnDownBlock2D')   ,	mid_block_type=UpperCAmelCase   ,	up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')   ,	block_out_channels=UpperCAmelCase   ,	layers_per_block=UpperCAmelCase   ,	act_fn='gelu'   ,	norm_num_groups=UpperCAmelCase   ,	cross_attention_dim=UpperCAmelCase   ,	attention_head_dim=6_4   ,	time_cond_proj_dim=UpperCAmelCase   ,	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 							snake_case				(		UpperCAmelCase						)->      Optional[int]:







      """simple docstring"""
      __A													=      torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)

      __A													=      huggingface_hub.hf_hub_download(UpperCAmelCase   ,	'config_laion_text_cond_latent_upscaler_2.json'						)
      __A													=      huggingface_hub.hf_hub_download(
          UpperCAmelCase   ,	'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(UpperCAmelCase						)						)['model']
      __A													=      unet_model_from_original_config(UpperCAmelCase						)

      __A													=      torch.load(UpperCAmelCase   ,	map_location=UpperCAmelCase						)['model_ema']
      __A													=      unet_to_diffusers_checkpoint(UpperCAmelCase   ,	UpperCAmelCase						)

      model.load_state_dict(UpperCAmelCase   ,	strict=UpperCAmelCase						)
      model.save_pretrained(args.dump_path						)
      print(f'saving converted unet model in {args.dump_path}'						)


if __name__ == "__main__":
 a__		: str  							=							argparse.ArgumentParser()

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

 main(args)











from __future__ import annotations


def 						UpperCAmelCase__   (   UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       UpperCAmelCase__			:float | int			,       ):






 '''simple docstring'''

 if not all((nth_term, start_term_a, common_ratio_r)       ):
  return []
 a							=		[]
 a							=		1
 a							=		common_ratio_r
 for _ in range(int(UpperCAmelCase__       )       ):
  if not series:
   series.append(UpperCAmelCase__       )
  else:
   power += 1
   series.append(float(start_term_a * multiple       )       )
   a							=		pow(float(UpperCAmelCase__       )			,       UpperCAmelCase__       )
 return series


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     A_		:  Optional[Any]										=  float(input('''Enter the last number (n term) of the Geometric Series'''))
     A_		:  Optional[Any]										=  float(input('''Enter the starting term (a) of the Geometric Series'''))
     A_		:  Optional[int]										=  float(
         input('''Enter the common ratio between two terms (r) of the Geometric Series''')
     )
     print('''Formula of Geometric Series => a + ar + ar^2 ... +ar^n''')
     print(geometric_series(nth_term, start_term_a, common_ratio_r))

