




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


lowerCamelCase__				:     Any			       =							'''pcuenq/k-upscaler'''

def       UpperCAmelCase_							(   __UpperCAmelCase		:			Any    ,    __UpperCAmelCase		:			Dict    ,    *, __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			Tuple							)      ->				Any:
      SCREAMING_SNAKE_CASE_					=    {
          # 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_							(   __UpperCAmelCase		:			str    ,    *, __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Dict							)      ->				Optional[int]:
      SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.qkv_proj.weight"].chunk(3    ,    dim=0							)
      SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.qkv_proj.bias"].chunk(3    ,    dim=0							)
      SCREAMING_SNAKE_CASE_					=    {
          # 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_							(   __UpperCAmelCase		:			List[Any]    ,    *, __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			str    ,    __UpperCAmelCase		:			int							)      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.kv_proj.weight"].chunk(2    ,    dim=0							)
      SCREAMING_SNAKE_CASE_					=    checkpoint[f"{attention_prefix}.kv_proj.bias"].chunk(2    ,    dim=0							)

      SCREAMING_SNAKE_CASE_					=    {
          # 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_							(   __UpperCAmelCase		:			List[str]    ,    __UpperCAmelCase		:			Dict    ,    __UpperCAmelCase		:			int    ,    __UpperCAmelCase		:			Any							)      ->				List[Any]:
      SCREAMING_SNAKE_CASE_					=    'inner_model.u_net.u_blocks' if block_type == 'up' else 'inner_model.u_net.d_blocks'
      SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{block_idx}"

      SCREAMING_SNAKE_CASE_					=    {}

      if not hasattr(__UpperCAmelCase    ,    'attentions'							):
            SCREAMING_SNAKE_CASE_					=    1  # resnet only
      elif not block.attentions[0].add_self_attention:
            SCREAMING_SNAKE_CASE_					=    2  # resnet -> cross-attention
      else:
            SCREAMING_SNAKE_CASE_					=    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}"
            SCREAMING_SNAKE_CASE_					=    f"{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"
            SCREAMING_SNAKE_CASE_					=    n * resnet_idx if block_type == 'up' else n * resnet_idx + 1
            SCREAMING_SNAKE_CASE_					=    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							):
                  SCREAMING_SNAKE_CASE_					=    f"{block_type}_blocks.{block_idx}.attentions.{attention_idx}"
                  SCREAMING_SNAKE_CASE_					=    n * attention_idx + 1 if block_type == 'up' else n * attention_idx + 2
                  SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{idx}"
                  SCREAMING_SNAKE_CASE_					=    f"{block_prefix}.{idx }"
                  SCREAMING_SNAKE_CASE_					=    1 if not attention.add_self_attention else 2
                  SCREAMING_SNAKE_CASE_					=    (
                      n * attention_idx + cross_attention_index
                      if block_type == 'up'
                      else n * attention_idx + cross_attention_index + 1
                  )
                  SCREAMING_SNAKE_CASE_					=    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       UpperCAmelCase_							(   __UpperCAmelCase		:			Optional[Any]    ,    __UpperCAmelCase		:			Any							)      ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE_					=    {}

      # 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       UpperCAmelCase_							(   __UpperCAmelCase		:			Union[str, Any]							)      ->				Tuple:
      SCREAMING_SNAKE_CASE_					=    original_config['input_channels'] + original_config['unet_cond_dim']
      SCREAMING_SNAKE_CASE_					=    original_config['input_channels'] + (1 if original_config['has_variance'] else 0)

      SCREAMING_SNAKE_CASE_					=    original_config['channels']

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

      SCREAMING_SNAKE_CASE_					=    original_config['mapping_cond_dim']
      SCREAMING_SNAKE_CASE_					=    original_config['cross_cond_dim']

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

      SCREAMING_SNAKE_CASE_					=    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=64    ,    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       UpperCAmelCase_							(   __UpperCAmelCase		:			List[str]							)      ->				str:
      SCREAMING_SNAKE_CASE_					=    torch.device('cuda' if torch.cuda.is_available() else 'cpu'							)

      SCREAMING_SNAKE_CASE_					=    huggingface_hub.hf_hub_download(__UpperCAmelCase    ,    'config_laion_text_cond_latent_upscaler_2.json'							)
      SCREAMING_SNAKE_CASE_					=    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'							)
      SCREAMING_SNAKE_CASE_					=    K.config.load_config(open(__UpperCAmelCase							)							)['model']
      SCREAMING_SNAKE_CASE_					=    unet_model_from_original_config(__UpperCAmelCase							)

      SCREAMING_SNAKE_CASE_					=    torch.load(__UpperCAmelCase    ,    map_location=__UpperCAmelCase							)['model_ema']
      SCREAMING_SNAKE_CASE_					=    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__":
   lowerCamelCase__				:     Any			       =							argparse.ArgumentParser()

   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   lowerCamelCase__				:     List[Any]			       =							parser.parse_args()

   main(args)












import bsa
import requests

def       UpperCAmelCase_							(   __UpperCAmelCase		:			bsa.element.ResultSet							)      ->				dict[str, str]:
      return {
          "name": soup.ha.a.text,
          "genre": soup.find('span'    ,    class_='genre'							).text.strip(),
          "rating": soup.strong.text,
          "page_link": f"https://www.imdb.com{soup.a.get('href'							)}",
      }

def       UpperCAmelCase_							(   __UpperCAmelCase		:			int = 5							)      ->				tuple:
      SCREAMING_SNAKE_CASE_					=    int(float(__UpperCAmelCase							)							)
      if num_movies < 1:
            return ()
      SCREAMING_SNAKE_CASE_					=    (
          'https://www.imdb.com/search/title?title_type='
          f"feature&sort=num_votes,desc&count={num_movies}"
      )
      SCREAMING_SNAKE_CASE_					=    bsa.BeautifulSoup(requests.get(__UpperCAmelCase							).content    ,    'html.parser'							)
      return tuple(
          get_movie_data_from_soup(__UpperCAmelCase							)
          for movie in source.find_all('div'    ,    class_='lister-item mode-advanced'							)							)


if __name__ == "__main__":
   import json

   lowerCamelCase__				:     int			       =							int(input('How many movies would you like to see? '))
   print(
       ', '.join(
           json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
       )
   )