




'''simple docstring'''






import argparse
import json

import torch

from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=1		)						->      List[Any]:



      if n_shave_prefix_segments >= 0:
            return ".".join(path.split('.'		)[n_shave_prefix_segments:]		)
      else:
            return ".".join(path.split('.'		)[:n_shave_prefix_segments]		)






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=0		)						->      Optional[Any]:
      snake_case__					:      Dict  			=						[]
      for old_item in old_list:
            snake_case__					:      Any  			=						old_item
            snake_case__					:      int  			=						new_item.replace('block.'							,			'resnets.'		)
            snake_case__					:      Union[str, Any]  			=						new_item.replace('conv_shorcut'							,			'conv1'		)
            snake_case__					:      Optional[Any]  			=						new_item.replace('in_shortcut'							,			'conv_shortcut'		)
            snake_case__					:      Any  			=						new_item.replace('temb_proj'							,			'time_emb_proj'		)

            snake_case__					:      Any  			=						shave_segments(__A							,			n_shave_prefix_segments=__A		)

            mapping.append({'old': old_item, 'new': new_item}		)

      return mapping






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=0							,			__SCREAMING_SNAKE_CASE=False		)						->      Any:
      snake_case__					:      Tuple  			=						[]
      for old_item in old_list:
            snake_case__					:      Dict  			=						old_item

            # In `model.mid`, the layer is called `attn`.
            if not in_mid:
                  snake_case__					:      Union[str, Any]  			=						new_item.replace('attn'							,			'attentions'		)
            snake_case__					:      Optional[int]  			=						new_item.replace('.k.'							,			'.key.'		)
            snake_case__					:      Union[str, Any]  			=						new_item.replace('.v.'							,			'.value.'		)
            snake_case__					:      Union[str, Any]  			=						new_item.replace('.q.'							,			'.query.'		)

            snake_case__					:      Optional[Any]  			=						new_item.replace('proj_out'							,			'proj_attn'		)
            snake_case__					:      Any  			=						new_item.replace('norm'							,			'group_norm'		)

            snake_case__					:      int  			=						shave_segments(__A							,			n_shave_prefix_segments=__A		)
            mapping.append({'old': old_item, 'new': new_item}		)

      return mapping






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None							,			__SCREAMING_SNAKE_CASE=None		)						->      Tuple:
      assert isinstance(__A							,			__A		), "Paths should be a list of dicts containing 'old' and 'new' keys."

      if attention_paths_to_split is not None:
            if config is None:
                  raise ValueError('Please specify the config if setting \'attention_paths_to_split\' to \'True\'.'		)

            for path, path_map in attention_paths_to_split.items():
                  snake_case__					:      str  			=						old_checkpoint[path]
                  snake_case__					:      Tuple  			=						old_tensor.shape[0] // 3

                  snake_case__					:      List[str]  			=						(-1, channels) if len(old_tensor.shape		) == 3 else (-1)

                  snake_case__					:      Any  			=						old_tensor.shape[0] // config.get('num_head_channels'							,			1		) // 3

                  snake_case__					:      str  			=						old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]		)
                  snake_case__			,       snake_case__			,       snake_case__					:      str  			=						old_tensor.split(channels // num_heads							,			dim=1		)

                  snake_case__					:      List[str]  			=						query.reshape(__A		).squeeze()
                  snake_case__					:      Any  			=						key.reshape(__A		).squeeze()
                  snake_case__					:      Optional[Any]  			=						value.reshape(__A		).squeeze()

      for path in paths:
            snake_case__					:      int  			=						path['new']

            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            snake_case__					:      Union[str, Any]  			=						new_path.replace('down.'							,			'down_blocks.'		)
            snake_case__					:      Union[str, Any]  			=						new_path.replace('up.'							,			'up_blocks.'		)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        snake_case__					:      List[Any]  			=						new_path.replace(replacement['old']							,			replacement['new']		)

            if "attentions" in new_path:
                  snake_case__					:      Dict  			=						old_checkpoint[path['old']].squeeze()
            else:
                  snake_case__					:      Tuple  			=						old_checkpoint[path['old']]






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      Optional[Any]:
      snake_case__					:      int  			=						{}

      snake_case__					:      Union[str, Any]  			=						checkpoint['temb.dense.0.weight']
      snake_case__					:      List[Any]  			=						checkpoint['temb.dense.0.bias']
      snake_case__					:      Tuple  			=						checkpoint['temb.dense.1.weight']
      snake_case__					:      int  			=						checkpoint['temb.dense.1.bias']

      snake_case__					:      List[Any]  			=						checkpoint['norm_out.weight']
      snake_case__					:      Dict  			=						checkpoint['norm_out.bias']

      snake_case__					:      Union[str, Any]  			=						checkpoint['conv_in.weight']
      snake_case__					:      Optional[Any]  			=						checkpoint['conv_in.bias']
      snake_case__					:      List[str]  			=						checkpoint['conv_out.weight']
      snake_case__					:      Dict  			=						checkpoint['conv_out.bias']

      snake_case__					:      Optional[Any]  			=						len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'down' in layer}		)
      snake_case__					:      Dict  			=						{
          layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A		)
      }

      snake_case__					:      Tuple  			=						len({'.'.join(layer.split('.'		)[:2]		) for layer in checkpoint if 'up' in layer}		)
      snake_case__					:      Optional[int]  			=						{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A		)}

      for i in range(__A		):
            snake_case__					:      List[Any]  			=						(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]		):
                  snake_case__					:      List[str]  			=						checkpoint[
                      f"down.{i}.downsample.op.weight"
                  ]
                  snake_case__					:      Any  			=						checkpoint[f"down.{i}.downsample.op.bias"]
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.weight'] = checkpoint[f'down.{i}.downsample.conv.weight']
            #            new_checkpoint[f'down_blocks.{i}.downsamplers.0.op.bias'] = checkpoint[f'down.{i}.downsample.conv.bias']

            if any('block' in layer for layer in down_blocks[i]		):
                  snake_case__					:      List[Any]  			=						len(
                      {'.'.join(shave_segments(__A							,			2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'block' in layer}		)
                  snake_case__					:      Union[str, Any]  			=						{
                      layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                      for layer_id in range(__A		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']		):
                              snake_case__					:      Any  			=						renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A		)

            if any('attn' in layer for layer in down_blocks[i]		):
                  snake_case__					:      str  			=						len(
                      {'.'.join(shave_segments(__A							,			2		).split('.'		)[:2]		) for layer in down_blocks[i] if 'attn' in layer}		)
                  snake_case__					:      Optional[int]  			=						{
                      layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                      for layer_id in range(__A		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']		):
                              snake_case__					:      Optional[Any]  			=						renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			config=__A		)

      snake_case__					:      Union[str, Any]  			=						[key for key in checkpoint if 'mid.block_1' in key]
      snake_case__					:      Any  			=						[key for key in checkpoint if 'mid.block_2' in key]
      snake_case__					:      List[str]  			=						[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      snake_case__					:      int  			=						renew_resnet_paths(__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,			)

      snake_case__					:      Any  			=						renew_resnet_paths(__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,			)

      snake_case__					:      Tuple  			=						renew_attention_paths(__A							,			in_mid=__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,			)

      for i in range(__A		):
            snake_case__					:      List[Any]  			=						num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Optional[int]  			=						checkpoint[
                      f"up.{i}.upsample.conv.weight"
                  ]
                  snake_case__					:      Dict  			=						checkpoint[f"up.{i}.upsample.conv.bias"]

            if any('block' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Optional[Any]  			=						len(
                      {'.'.join(shave_segments(__A							,			2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'block' in layer}		)
                  snake_case__					:      int  			=						{
                      layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              snake_case__					:      Optional[Any]  			=						{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                              snake_case__					:      Optional[int]  			=						renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			additional_replacements=[replace_indices]		)

            if any('attn' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Optional[Any]  			=						len(
                      {'.'.join(shave_segments(__A							,			2		).split('.'		)[:2]		) for layer in up_blocks[i] if 'attn' in layer}		)
                  snake_case__					:      Optional[Any]  			=						{
                      layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              snake_case__					:      Any  			=						{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                              snake_case__					:      Optional[Any]  			=						renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			additional_replacements=[replace_indices]		)

      snake_case__					:      List[Any]  			=						{k.replace('mid_new_2'							,			'mid_block'		): v for k, v in new_checkpoint.items()}
      return new_checkpoint






def 		UpperCamelCase__			(		__SCREAMING_SNAKE_CASE							,			__SCREAMING_SNAKE_CASE		)						->      List[Any]:
      snake_case__					:      List[str]  			=						{}

      snake_case__					:      List[Any]  			=						checkpoint['encoder.norm_out.weight']
      snake_case__					:      Tuple  			=						checkpoint['encoder.norm_out.bias']

      snake_case__					:      Any  			=						checkpoint['encoder.conv_in.weight']
      snake_case__					:      Dict  			=						checkpoint['encoder.conv_in.bias']
      snake_case__					:      List[Any]  			=						checkpoint['encoder.conv_out.weight']
      snake_case__					:      Tuple  			=						checkpoint['encoder.conv_out.bias']

      snake_case__					:      List[Any]  			=						checkpoint['decoder.norm_out.weight']
      snake_case__					:      Tuple  			=						checkpoint['decoder.norm_out.bias']

      snake_case__					:      Optional[Any]  			=						checkpoint['decoder.conv_in.weight']
      snake_case__					:      List[Any]  			=						checkpoint['decoder.conv_in.bias']
      snake_case__					:      int  			=						checkpoint['decoder.conv_out.weight']
      snake_case__					:      Tuple  			=						checkpoint['decoder.conv_out.bias']

      snake_case__					:      str  			=						len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'down' in layer}		)
      snake_case__					:      List[str]  			=						{
          layer_id: [key for key in checkpoint if f"down.{layer_id}" in key] for layer_id in range(__A		)
      }

      snake_case__					:      Any  			=						len({'.'.join(layer.split('.'		)[:3]		) for layer in checkpoint if 'up' in layer}		)
      snake_case__					:      int  			=						{layer_id: [key for key in checkpoint if f"up.{layer_id}" in key] for layer_id in range(__A		)}

      for i in range(__A		):
            snake_case__					:      Any  			=						(i - 1) // (config['layers_per_block'] + 1)

            if any('downsample' in layer for layer in down_blocks[i]		):
                  snake_case__					:      int  			=						checkpoint[
                      f"encoder.down.{i}.downsample.conv.weight"
                  ]
                  snake_case__					:      List[str]  			=						checkpoint[
                      f"encoder.down.{i}.downsample.conv.bias"
                  ]

            if any('block' in layer for layer in down_blocks[i]		):
                  snake_case__					:      Union[str, Any]  			=						len(
                      {'.'.join(shave_segments(__A							,			3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'block' in layer}		)
                  snake_case__					:      List[str]  			=						{
                      layer_id: [key for key in down_blocks[i] if f"block.{layer_id}" in key]
                      for layer_id in range(__A		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block']		):
                              snake_case__					:      Optional[int]  			=						renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A		)

            if any('attn' in layer for layer in down_blocks[i]		):
                  snake_case__					:      Optional[int]  			=						len(
                      {'.'.join(shave_segments(__A							,			3		).split('.'		)[:3]		) for layer in down_blocks[i] if 'attn' in layer}		)
                  snake_case__					:      str  			=						{
                      layer_id: [key for key in down_blocks[i] if f"attn.{layer_id}" in key]
                      for layer_id in range(__A		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block']		):
                              snake_case__					:      int  			=						renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			config=__A		)

      snake_case__					:      Any  			=						[key for key in checkpoint if 'mid.block_1' in key]
      snake_case__					:      Dict  			=						[key for key in checkpoint if 'mid.block_2' in key]
      snake_case__					:      Tuple  			=						[key for key in checkpoint if 'mid.attn_1' in key]

      # Mid new 2
      snake_case__					:      List[Any]  			=						renew_resnet_paths(__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_1', 'new': 'resnets.0'}]							,			)

      snake_case__					:      str  			=						renew_resnet_paths(__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'block_2', 'new': 'resnets.1'}]							,			)

      snake_case__					:      Optional[Any]  			=						renew_attention_paths(__A							,			in_mid=__A		)
      assign_to_checkpoint(
          __A							,			__A							,			__A							,			additional_replacements=[{'old': 'mid.', 'new': 'mid_new_2.'}, {'old': 'attn_1', 'new': 'attentions.0'}]							,			)

      for i in range(__A		):
            snake_case__					:      int  			=						num_up_blocks - 1 - i

            if any('upsample' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Union[str, Any]  			=						checkpoint[
                      f"decoder.up.{i}.upsample.conv.weight"
                  ]
                  snake_case__					:      Any  			=						checkpoint[
                      f"decoder.up.{i}.upsample.conv.bias"
                  ]

            if any('block' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Optional[Any]  			=						len(
                      {'.'.join(shave_segments(__A							,			3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'block' in layer}		)
                  snake_case__					:      List[Any]  			=						{
                      layer_id: [key for key in up_blocks[i] if f"block.{layer_id}" in key] for layer_id in range(__A		)
                  }

                  if num_blocks > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              snake_case__					:      Optional[int]  			=						{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                              snake_case__					:      List[Any]  			=						renew_resnet_paths(blocks[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			additional_replacements=[replace_indices]		)

            if any('attn' in layer for layer in up_blocks[i]		):
                  snake_case__					:      Dict  			=						len(
                      {'.'.join(shave_segments(__A							,			3		).split('.'		)[:3]		) for layer in up_blocks[i] if 'attn' in layer}		)
                  snake_case__					:      List[str]  			=						{
                      layer_id: [key for key in up_blocks[i] if f"attn.{layer_id}" in key] for layer_id in range(__A		)
                  }

                  if num_attn > 0:
                        for j in range(config['layers_per_block'] + 1		):
                              snake_case__					:      Optional[Any]  			=						{'old': f"up_blocks.{i}", 'new': f"up_blocks.{block_id}"}
                              snake_case__					:      List[Any]  			=						renew_attention_paths(attns[j]		)
                              assign_to_checkpoint(__A							,			__A							,			__A							,			additional_replacements=[replace_indices]		)

      snake_case__					:      Optional[Any]  			=						{k.replace('mid_new_2'							,			'mid_block'		): v for k, v in new_checkpoint.items()}
      snake_case__					:      Any  			=						checkpoint['quant_conv.weight']
      snake_case__					:      Optional[int]  			=						checkpoint['quant_conv.bias']
      if "quantize.embedding.weight" in checkpoint:
            snake_case__					:      Any  			=						checkpoint['quantize.embedding.weight']
      snake_case__					:      int  			=						checkpoint['post_quant_conv.weight']
      snake_case__					:      Tuple  			=						checkpoint['post_quant_conv.bias']

      return new_checkpoint


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

      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
      )

      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the architecture.",
      )

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

      A_ 			=		parser.parse_args()
      A_ 			=		torch.load(args.checkpoint_path)

      with open(args.config_file) as f:
            A_ 			=		json.loads(f.read())

      # unet case
      A_ 			=		{key.split(".")[0] for key in checkpoint.keys()}
      if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
            A_ 			=		convert_vq_autoenc_checkpoint(checkpoint, config)
      else:
            A_ 			=		convert_ddpm_checkpoint(checkpoint, config)

      if "ddpm" in config:
            del config["ddpm"]

      if config["_class_name"] == "VQModel":
            A_ 			=		VQModel(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      elif config["_class_name"] == "AutoencoderKL":
            A_ 			=		AutoencoderKL(**config)
            model.load_state_dict(converted_checkpoint)
            model.save_pretrained(args.dump_path)
      else:
            A_ 			=		UNetaDModel(**config)
            model.load_state_dict(converted_checkpoint)

            A_ 			=		DDPMScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

            A_ 			=		DDPMPipeline(unet=model, scheduler=scheduler)
            pipe.save_pretrained(args.dump_path)






'''simple docstring'''






import bsa
import requests






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


if __name__ == "__main__":
      import json

      A_ 			=		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)
          )
      )
