code: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import AutoencoderKL, DDPMPipeline, DDPMScheduler, UNetaDModel, VQModel
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       Optional[int]=1    ):
    if n_shave_prefix_segments >= 0:
      return ".".join(path.split("""."""    )[n_shave_prefix_segments:]    )
    else:
      return ".".join(path.split("""."""    )[:n_shave_prefix_segments]    )
  
  
  
  
  def       a__						(		snake_case__				:       int	,      snake_case__				:       List[Any]=0    ):
    _UpperCAmelCase		:       Tuple       =    []
    for old_item in old_list:
      _UpperCAmelCase		:       Tuple       =    old_item
      _UpperCAmelCase		:       str       =    new_item.replace("""block."""	,      """resnets."""    )
      _UpperCAmelCase		:       Optional[Any]       =    new_item.replace("""conv_shorcut"""	,      """conv1"""    )
      _UpperCAmelCase		:       List[str]       =    new_item.replace("""in_shortcut"""	,      """conv_shortcut"""    )
      _UpperCAmelCase		:       List[Any]       =    new_item.replace("""temb_proj"""	,      """time_emb_proj"""    )
  
      _UpperCAmelCase		:       Tuple       =    shave_segments(snake_case__	,      n_shave_prefix_segments=snake_case__    )
  
      mapping.append({"""old""": old_item, """new""": new_item}    )
  
    return mapping
  
  
  
  
  def       a__						(		snake_case__				:       Optional[int]	,      snake_case__				:       str=0	,      snake_case__				:       Optional[int]=False    ):
    _UpperCAmelCase		:       Union[str, Any]       =    []
    for old_item in old_list:
      _UpperCAmelCase		:       Tuple       =    old_item
  
      # In `model.mid`, the layer is called `attn`.
      if not in_mid:
        _UpperCAmelCase		:       Tuple       =    new_item.replace("""attn"""	,      """attentions"""    )
      _UpperCAmelCase		:       List[Any]       =    new_item.replace(""".k."""	,      """.key."""    )
      _UpperCAmelCase		:       Optional[Any]       =    new_item.replace(""".v."""	,      """.value."""    )
      _UpperCAmelCase		:       Union[str, Any]       =    new_item.replace(""".q."""	,      """.query."""    )
  
      _UpperCAmelCase		:       List[Any]       =    new_item.replace("""proj_out"""	,      """proj_attn"""    )
      _UpperCAmelCase		:       List[Any]       =    new_item.replace("""norm"""	,      """group_norm"""    )
  
      _UpperCAmelCase		:       List[str]       =    shave_segments(snake_case__	,      n_shave_prefix_segments=snake_case__    )
      mapping.append({"""old""": old_item, """new""": new_item}    )
  
    return mapping
  
  
  
  
  def       a__						(		snake_case__				:       int	,      snake_case__				:       Any	,      snake_case__				:       Optional[int]	,      snake_case__				:       Tuple=None	,      snake_case__				:       Tuple=None	,      snake_case__				:       List[Any]=None    ):
    assert isinstance(snake_case__	,      snake_case__    ), "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():
        _UpperCAmelCase		:       List[str]       =    old_checkpoint[path]
        _UpperCAmelCase		:       int       =    old_tensor.shape[0] // 3
  
        _UpperCAmelCase		:       Dict       =    (-1, channels) if len(old_tensor.shape    ) == 3 else (-1)
  
        _UpperCAmelCase		:       str       =    old_tensor.shape[0] // config.get("""num_head_channels"""	,      1    ) // 3
  
        _UpperCAmelCase		:       Tuple       =    old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]    )
        _UpperCAmelCase		:       List[Any]       =    old_tensor.split(channels // num_heads	,      dim=1    )
  
        _UpperCAmelCase		:       Dict       =    query.reshape(snake_case__    ).squeeze()
        _UpperCAmelCase		:       Optional[int]       =    key.reshape(snake_case__    ).squeeze()
        _UpperCAmelCase		:       Dict       =    value.reshape(snake_case__    ).squeeze()
  
    for path in paths:
      _UpperCAmelCase		:       Optional[int]       =    path["""new"""]
  
      if attention_paths_to_split is not None and new_path in attention_paths_to_split:
        continue
  
      _UpperCAmelCase		:       List[str]       =    new_path.replace("""down."""	,      """down_blocks."""    )
      _UpperCAmelCase		:       List[str]       =    new_path.replace("""up."""	,      """up_blocks."""    )
  
      if additional_replacements is not None:
        for replacement in additional_replacements:
          _UpperCAmelCase		:       List[Any]       =    new_path.replace(replacement["""old"""]	,      replacement["""new"""]    )
  
      if "attentions" in new_path:
        _UpperCAmelCase		:       str       =    old_checkpoint[path["""old"""]].squeeze()
      else:
        _UpperCAmelCase		:       str       =    old_checkpoint[path["""old"""]]
  
  
  
  
  def       a__						(		snake_case__				:       List[str]	,      snake_case__				:       Optional[Any]    ):
    _UpperCAmelCase		:       int       =    {}
  
    _UpperCAmelCase		:       Union[str, Any]       =    checkpoint["""temb.dense.0.weight"""]
    _UpperCAmelCase		:       Any       =    checkpoint["""temb.dense.0.bias"""]
    _UpperCAmelCase		:       str       =    checkpoint["""temb.dense.1.weight"""]
    _UpperCAmelCase		:       int       =    checkpoint["""temb.dense.1.bias"""]
  
    _UpperCAmelCase		:       str       =    checkpoint["""norm_out.weight"""]
    _UpperCAmelCase		:       Tuple       =    checkpoint["""norm_out.bias"""]
  
    _UpperCAmelCase		:       Dict       =    checkpoint["""conv_in.weight"""]
    _UpperCAmelCase		:       Dict       =    checkpoint["""conv_in.bias"""]
    _UpperCAmelCase		:       str       =    checkpoint["""conv_out.weight"""]
    _UpperCAmelCase		:       List[str]       =    checkpoint["""conv_out.bias"""]
  
    _UpperCAmelCase		:       Optional[int]       =    len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """down""" in layer}    )
    _UpperCAmelCase		:       Tuple       =    {
        layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(snake_case__    )
    }
  
    _UpperCAmelCase		:       Optional[int]       =    len({""".""".join(layer.split("""."""    )[:2]    ) for layer in checkpoint if """up""" in layer}    )
    _UpperCAmelCase		:       List[str]       =    {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(snake_case__    )}
  
    for i in range(snake_case__    ):
      _UpperCAmelCase		:       List[str]       =    (i - 1) // (config["""layers_per_block"""] + 1)
  
      if any("""downsample""" in layer for layer in down_blocks[i]    ):
        _UpperCAmelCase		:       str       =    checkpoint[
            f'''down.{i}.downsample.op.weight'''
        ]
        _UpperCAmelCase		:       Dict       =    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]    ):
        _UpperCAmelCase		:       str       =    len(
            {""".""".join(shave_segments(snake_case__	,      2    ).split("""."""    )[:2]    ) for layer in down_blocks[i] if """block""" in layer}    )
        _UpperCAmelCase		:       Optional[Any]       =    {
            layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
            for layer_id in range(snake_case__    )
        }
  
        if num_blocks > 0:
          for j in range(config["""layers_per_block"""]    ):
            _UpperCAmelCase		:       Any       =    renew_resnet_paths(blocks[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__    )
  
      if any("""attn""" in layer for layer in down_blocks[i]    ):
        _UpperCAmelCase		:       Optional[int]       =    len(
            {""".""".join(shave_segments(snake_case__	,      2    ).split("""."""    )[:2]    ) for layer in down_blocks[i] if """attn""" in layer}    )
        _UpperCAmelCase		:       List[str]       =    {
            layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
            for layer_id in range(snake_case__    )
        }
  
        if num_attn > 0:
          for j in range(config["""layers_per_block"""]    ):
            _UpperCAmelCase		:       Tuple       =    renew_attention_paths(attns[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      config=snake_case__    )
  
    _UpperCAmelCase		:       Optional[Any]       =    [key for key in checkpoint if """mid.block_1""" in key]
    _UpperCAmelCase		:       Any       =    [key for key in checkpoint if """mid.block_2""" in key]
    _UpperCAmelCase		:       Any       =    [key for key in checkpoint if """mid.attn_1""" in key]
  
    # Mid new 2
    _UpperCAmelCase		:       Optional[int]       =    renew_resnet_paths(snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]	,      )
  
    _UpperCAmelCase		:       List[str]       =    renew_resnet_paths(snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]	,      )
  
    _UpperCAmelCase		:       int       =    renew_attention_paths(snake_case__	,      in_mid=snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]	,      )
  
    for i in range(snake_case__    ):
      _UpperCAmelCase		:       Union[str, Any]       =    num_up_blocks - 1 - i
  
      if any("""upsample""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       Optional[int]       =    checkpoint[
            f'''up.{i}.upsample.conv.weight'''
        ]
        _UpperCAmelCase		:       Optional[int]       =    checkpoint[f'''up.{i}.upsample.conv.bias''']
  
      if any("""block""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       str       =    len(
            {""".""".join(shave_segments(snake_case__	,      2    ).split("""."""    )[:2]    ) for layer in up_blocks[i] if """block""" in layer}    )
        _UpperCAmelCase		:       Tuple       =    {
            layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(snake_case__    )
        }
  
        if num_blocks > 0:
          for j in range(config["""layers_per_block"""] + 1    ):
            _UpperCAmelCase		:       str       =    {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
            _UpperCAmelCase		:       str       =    renew_resnet_paths(blocks[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[replace_indices]    )
  
      if any("""attn""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       Optional[int]       =    len(
            {""".""".join(shave_segments(snake_case__	,      2    ).split("""."""    )[:2]    ) for layer in up_blocks[i] if """attn""" in layer}    )
        _UpperCAmelCase		:       List[str]       =    {
            layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(snake_case__    )
        }
  
        if num_attn > 0:
          for j in range(config["""layers_per_block"""] + 1    ):
            _UpperCAmelCase		:       Optional[Any]       =    {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
            _UpperCAmelCase		:       Union[str, Any]       =    renew_attention_paths(attns[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[replace_indices]    )
  
    _UpperCAmelCase		:       str       =    {k.replace("""mid_new_2"""	,      """mid_block"""    ): v for k, v in new_checkpoint.items()}
    return new_checkpoint
  
  
  
  
  def       a__						(		snake_case__				:       List[str]	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Optional[int]       =    {}
  
    _UpperCAmelCase		:       int       =    checkpoint["""encoder.norm_out.weight"""]
    _UpperCAmelCase		:       int       =    checkpoint["""encoder.norm_out.bias"""]
  
    _UpperCAmelCase		:       List[Any]       =    checkpoint["""encoder.conv_in.weight"""]
    _UpperCAmelCase		:       List[Any]       =    checkpoint["""encoder.conv_in.bias"""]
    _UpperCAmelCase		:       Dict       =    checkpoint["""encoder.conv_out.weight"""]
    _UpperCAmelCase		:       Any       =    checkpoint["""encoder.conv_out.bias"""]
  
    _UpperCAmelCase		:       int       =    checkpoint["""decoder.norm_out.weight"""]
    _UpperCAmelCase		:       Dict       =    checkpoint["""decoder.norm_out.bias"""]
  
    _UpperCAmelCase		:       Optional[int]       =    checkpoint["""decoder.conv_in.weight"""]
    _UpperCAmelCase		:       int       =    checkpoint["""decoder.conv_in.bias"""]
    _UpperCAmelCase		:       Tuple       =    checkpoint["""decoder.conv_out.weight"""]
    _UpperCAmelCase		:       Tuple       =    checkpoint["""decoder.conv_out.bias"""]
  
    _UpperCAmelCase		:       Optional[Any]       =    len({""".""".join(layer.split("""."""    )[:3]    ) for layer in checkpoint if """down""" in layer}    )
    _UpperCAmelCase		:       Union[str, Any]       =    {
        layer_id: [key for key in checkpoint if f'''down.{layer_id}''' in key] for layer_id in range(snake_case__    )
    }
  
    _UpperCAmelCase		:       List[Any]       =    len({""".""".join(layer.split("""."""    )[:3]    ) for layer in checkpoint if """up""" in layer}    )
    _UpperCAmelCase		:       Optional[Any]       =    {layer_id: [key for key in checkpoint if f'''up.{layer_id}''' in key] for layer_id in range(snake_case__    )}
  
    for i in range(snake_case__    ):
      _UpperCAmelCase		:       Optional[Any]       =    (i - 1) // (config["""layers_per_block"""] + 1)
  
      if any("""downsample""" in layer for layer in down_blocks[i]    ):
        _UpperCAmelCase		:       List[str]       =    checkpoint[
            f'''encoder.down.{i}.downsample.conv.weight'''
        ]
        _UpperCAmelCase		:       List[Any]       =    checkpoint[
            f'''encoder.down.{i}.downsample.conv.bias'''
        ]
  
      if any("""block""" in layer for layer in down_blocks[i]    ):
        _UpperCAmelCase		:       List[Any]       =    len(
            {""".""".join(shave_segments(snake_case__	,      3    ).split("""."""    )[:3]    ) for layer in down_blocks[i] if """block""" in layer}    )
        _UpperCAmelCase		:       int       =    {
            layer_id: [key for key in down_blocks[i] if f'''block.{layer_id}''' in key]
            for layer_id in range(snake_case__    )
        }
  
        if num_blocks > 0:
          for j in range(config["""layers_per_block"""]    ):
            _UpperCAmelCase		:       Any       =    renew_resnet_paths(blocks[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__    )
  
      if any("""attn""" in layer for layer in down_blocks[i]    ):
        _UpperCAmelCase		:       int       =    len(
            {""".""".join(shave_segments(snake_case__	,      3    ).split("""."""    )[:3]    ) for layer in down_blocks[i] if """attn""" in layer}    )
        _UpperCAmelCase		:       Dict       =    {
            layer_id: [key for key in down_blocks[i] if f'''attn.{layer_id}''' in key]
            for layer_id in range(snake_case__    )
        }
  
        if num_attn > 0:
          for j in range(config["""layers_per_block"""]    ):
            _UpperCAmelCase		:       List[str]       =    renew_attention_paths(attns[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      config=snake_case__    )
  
    _UpperCAmelCase		:       Dict       =    [key for key in checkpoint if """mid.block_1""" in key]
    _UpperCAmelCase		:       Any       =    [key for key in checkpoint if """mid.block_2""" in key]
    _UpperCAmelCase		:       Optional[Any]       =    [key for key in checkpoint if """mid.attn_1""" in key]
  
    # Mid new 2
    _UpperCAmelCase		:       int       =    renew_resnet_paths(snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_1""", """new""": """resnets.0"""}]	,      )
  
    _UpperCAmelCase		:       Union[str, Any]       =    renew_resnet_paths(snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """block_2""", """new""": """resnets.1"""}]	,      )
  
    _UpperCAmelCase		:       Optional[int]       =    renew_attention_paths(snake_case__	,      in_mid=snake_case__    )
    assign_to_checkpoint(
        snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[{"""old""": """mid.""", """new""": """mid_new_2."""}, {"""old""": """attn_1""", """new""": """attentions.0"""}]	,      )
  
    for i in range(snake_case__    ):
      _UpperCAmelCase		:       int       =    num_up_blocks - 1 - i
  
      if any("""upsample""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       Union[str, Any]       =    checkpoint[
            f'''decoder.up.{i}.upsample.conv.weight'''
        ]
        _UpperCAmelCase		:       List[Any]       =    checkpoint[
            f'''decoder.up.{i}.upsample.conv.bias'''
        ]
  
      if any("""block""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       List[str]       =    len(
            {""".""".join(shave_segments(snake_case__	,      3    ).split("""."""    )[:3]    ) for layer in up_blocks[i] if """block""" in layer}    )
        _UpperCAmelCase		:       Union[str, Any]       =    {
            layer_id: [key for key in up_blocks[i] if f'''block.{layer_id}''' in key] for layer_id in range(snake_case__    )
        }
  
        if num_blocks > 0:
          for j in range(config["""layers_per_block"""] + 1    ):
            _UpperCAmelCase		:       List[Any]       =    {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
            _UpperCAmelCase		:       str       =    renew_resnet_paths(blocks[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[replace_indices]    )
  
      if any("""attn""" in layer for layer in up_blocks[i]    ):
        _UpperCAmelCase		:       Union[str, Any]       =    len(
            {""".""".join(shave_segments(snake_case__	,      3    ).split("""."""    )[:3]    ) for layer in up_blocks[i] if """attn""" in layer}    )
        _UpperCAmelCase		:       Tuple       =    {
            layer_id: [key for key in up_blocks[i] if f'''attn.{layer_id}''' in key] for layer_id in range(snake_case__    )
        }
  
        if num_attn > 0:
          for j in range(config["""layers_per_block"""] + 1    ):
            _UpperCAmelCase		:       Tuple       =    {"""old""": f'''up_blocks.{i}''', """new""": f'''up_blocks.{block_id}'''}
            _UpperCAmelCase		:       List[str]       =    renew_attention_paths(attns[j]    )
            assign_to_checkpoint(snake_case__	,      snake_case__	,      snake_case__	,      additional_replacements=[replace_indices]    )
  
    _UpperCAmelCase		:       List[Any]       =    {k.replace("""mid_new_2"""	,      """mid_block"""    ): v for k, v in new_checkpoint.items()}
    _UpperCAmelCase		:       Optional[int]       =    checkpoint["""quant_conv.weight"""]
    _UpperCAmelCase		:       Optional[Any]       =    checkpoint["""quant_conv.bias"""]
    if "quantize.embedding.weight" in checkpoint:
      _UpperCAmelCase		:       Union[str, Any]       =    checkpoint["""quantize.embedding.weight"""]
    _UpperCAmelCase		:       Dict       =    checkpoint["""post_quant_conv.weight"""]
    _UpperCAmelCase		:       str       =    checkpoint["""post_quant_conv.bias"""]
  
    return new_checkpoint
  
  
  if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__				:    List[str]         =    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.')
  
     SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    parser.parse_args()
     SCREAMING_SNAKE_CASE__				:    Tuple         =    torch.load(args.checkpoint_path)
  
     with open(args.config_file) as f:
        SCREAMING_SNAKE_CASE__				:    str         =    json.loads(f.read())
  
     # unet case
     SCREAMING_SNAKE_CASE__				:    Dict         =    {key.split('.')[0] for key in checkpoint.keys()}
     if "encoder" in key_prefix_set and "decoder" in key_prefix_set:
        SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    convert_vq_autoenc_checkpoint(checkpoint, config)
     else:
        SCREAMING_SNAKE_CASE__				:    str         =    convert_ddpm_checkpoint(checkpoint, config)
  
     if "ddpm" in config:
        del config["ddpm"]
  
     if config["_class_name"] == "VQModel":
        SCREAMING_SNAKE_CASE__				:    Dict         =    VQModel(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
     elif config["_class_name"] == "AutoencoderKL":
        SCREAMING_SNAKE_CASE__				:    Union[str, Any]         =    AutoencoderKL(**config)
        model.load_state_dict(converted_checkpoint)
        model.save_pretrained(args.dump_path)
     else:
        SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    UNetaDModel(**config)
        model.load_state_dict(converted_checkpoint)
  
        SCREAMING_SNAKE_CASE__				:    Optional[Any]         =    DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
        SCREAMING_SNAKE_CASE__				:    List[str]         =    DDPMPipeline(unet=model, scheduler=scheduler)
        pipe.save_pretrained(args.dump_path)
  
  
  
  
  
  
style_context: 
  
  
  import os
  
  import requests
  from bsa import BeautifulSoup
  from fake_useragent import UserAgent
  
  SCREAMING_SNAKE_CASE__				:    int         =    {'UserAgent': UserAgent().random}
  SCREAMING_SNAKE_CASE__				:    Tuple         =    'https://www.mywaifulist.moe/random'
  
  
  
  
  def       a__						(		snake_case__				:       str	,      snake_case__				:       str    ):
    _UpperCAmelCase		:       Any       =    requests.get(snake_case__	,      headers=snake_case__    )
    with open(snake_case__	,      """wb"""    ) as file:
      file.write(image.content    )
  
  
  
  
  def       a__						(		):
    _UpperCAmelCase		:       List[Any]       =    BeautifulSoup(requests.get(snake_case__	,      headers=snake_case__    ).text	,      """html.parser"""    )
    _UpperCAmelCase		:       Optional[int]       =    soup.find("""meta"""	,      attrs={"""property""": """og:title"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       int       =    soup.find("""meta"""	,      attrs={"""property""": """og:image"""}    ).attrs["""content"""]
    _UpperCAmelCase		:       Any       =    soup.find("""p"""	,      id="""description"""    ).get_text()
    _UpperCAmelCase,_UpperCAmelCase		:       Dict       =    os.path.splitext(os.path.basename(snake_case__    )    )
    _UpperCAmelCase		:       Tuple       =    title.strip().replace(""" """	,      """_"""    )
    _UpperCAmelCase		:       Optional[Any]       =    f'''{image_title}{image_extension}'''
    save_image(snake_case__	,      snake_case__    )
    return (title, description, image_title)
  
  
  if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__     ,				SCREAMING_SNAKE_CASE__				:    Optional[int]         =    random_anime_character()
   print(F'{title}\n\n{desc}\n\nImage saved : {image_title}')
  
  
  
  
  
  