



"""simple docstring"""
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel





def  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Tuple=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  lowerCamelCase_						(UpperCamelCase__		:		Optional[int]     ,       UpperCamelCase__		:		Any=0							):
    _UpperCAmelCase							:  Union[str, Any]											=    []
    for old_item in old_list:
        _UpperCAmelCase							:  int											=    old_item.replace('''in_layers.0'''     ,       '''norm1'''							)
        _UpperCAmelCase							:  Dict											=    new_item.replace('''in_layers.2'''     ,       '''conv1'''							)

        _UpperCAmelCase							:  str											=    new_item.replace('''out_layers.0'''     ,       '''norm2'''							)
        _UpperCAmelCase							:  List[Any]											=    new_item.replace('''out_layers.3'''     ,       '''conv2'''							)

        _UpperCAmelCase							:  Tuple											=    new_item.replace('''emb_layers.1'''     ,       '''time_emb_proj'''							)
        _UpperCAmelCase							:  List[str]											=    new_item.replace('''skip_connection'''     ,       '''conv_shortcut'''							)

        _UpperCAmelCase							:  Optional[Any]											=    shave_segments(UpperCamelCase__     ,       n_shave_prefix_segments=UpperCamelCase__							)

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

    return mapping





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		List[str]=0							):
    _UpperCAmelCase							:  str											=    []
    for old_item in old_list:
        _UpperCAmelCase							:  Any											=    old_item

        _UpperCAmelCase							:  str											=    new_item.replace('''norm.weight'''     ,       '''group_norm.weight'''							)
        _UpperCAmelCase							:  Tuple											=    new_item.replace('''norm.bias'''     ,       '''group_norm.bias'''							)

        _UpperCAmelCase							:  int											=    new_item.replace('''proj_out.weight'''     ,       '''proj_attn.weight'''							)
        _UpperCAmelCase							:  Dict											=    new_item.replace('''proj_out.bias'''     ,       '''proj_attn.bias'''							)

        _UpperCAmelCase							:  Any											=    shave_segments(UpperCamelCase__     ,       n_shave_prefix_segments=UpperCamelCase__							)

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

    return mapping





def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Tuple     ,       UpperCamelCase__		:		List[Any]=None     ,       UpperCamelCase__		:		List[str]=None     ,       UpperCamelCase__		:		Tuple=None							):
    assert isinstance(UpperCamelCase__     ,       UpperCamelCase__							), "Paths should be a list of dicts containing 'old' and 'new' keys."

    # Splits the attention layers into three variables.
    if attention_paths_to_split is not None:
        for path, path_map in attention_paths_to_split.items():
            _UpperCAmelCase							:  Union[str, Any]											=    old_checkpoint[path]
            _UpperCAmelCase							:  List[Any]											=    old_tensor.shape[0] // 3

            _UpperCAmelCase							:  Union[str, Any]											=    (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

            _UpperCAmelCase							:  Optional[int]											=    old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

            _UpperCAmelCase							:  Union[str, Any]											=    query.reshape(UpperCamelCase__							)
            _UpperCAmelCase							:  str											=    key.reshape(UpperCamelCase__							)
            _UpperCAmelCase							:  List[Any]											=    value.reshape(UpperCamelCase__							)

    for path in paths:
        _UpperCAmelCase							:  List[str]											=    path['''new''']

        # These have already been assigned
        if attention_paths_to_split is not None and new_path in attention_paths_to_split:
            continue

        # Global renaming happens here
        _UpperCAmelCase							:  Optional[int]											=    new_path.replace('''middle_block.0'''     ,       '''mid_block.resnets.0'''							)
        _UpperCAmelCase							:  int											=    new_path.replace('''middle_block.1'''     ,       '''mid_block.attentions.0'''							)
        _UpperCAmelCase							:  List[Any]											=    new_path.replace('''middle_block.2'''     ,       '''mid_block.resnets.1'''							)

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

        # proj_attn.weight has to be converted from conv 1D to linear
        if "proj_attn.weight" in new_path:
            _UpperCAmelCase							:  List[str]											=    old_checkpoint[path['''old''']][:, :, 0]
        else:
            _UpperCAmelCase							:  Dict											=    old_checkpoint[path['''old''']]





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		Optional[int]							):
    _UpperCAmelCase							:  Dict											=    {}

    _UpperCAmelCase							:  Optional[Any]											=    checkpoint['''time_embed.0.weight''']
    _UpperCAmelCase							:  Tuple											=    checkpoint['''time_embed.0.bias''']
    _UpperCAmelCase							:  str											=    checkpoint['''time_embed.2.weight''']
    _UpperCAmelCase							:  Optional[Any]											=    checkpoint['''time_embed.2.bias''']

    _UpperCAmelCase							:  Union[str, Any]											=    checkpoint['''input_blocks.0.0.weight''']
    _UpperCAmelCase							:  str											=    checkpoint['''input_blocks.0.0.bias''']

    _UpperCAmelCase							:  Optional[int]											=    checkpoint['''out.0.weight''']
    _UpperCAmelCase							:  Union[str, Any]											=    checkpoint['''out.0.bias''']
    _UpperCAmelCase							:  int											=    checkpoint['''out.2.weight''']
    _UpperCAmelCase							:  Dict											=    checkpoint['''out.2.bias''']

    # Retrieves the keys for the input blocks only
    _UpperCAmelCase							:  Optional[Any]											=    len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''input_blocks''' in layer}							)
    _UpperCAmelCase							:  str											=    {
        layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
        for layer_id in range(UpperCamelCase__							)
    }

    # Retrieves the keys for the middle blocks only
    _UpperCAmelCase							:  Union[str, Any]											=    len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''middle_block''' in layer}							)
    _UpperCAmelCase							:  Optional[Any]											=    {
        layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
        for layer_id in range(UpperCamelCase__							)
    }

    # Retrieves the keys for the output blocks only
    _UpperCAmelCase							:  int											=    len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in checkpoint if '''output_blocks''' in layer}							)
    _UpperCAmelCase							:  Optional[Any]											=    {
        layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
        for layer_id in range(UpperCamelCase__							)
    }

    for i in range(1     ,       UpperCamelCase__							):
        _UpperCAmelCase							:  Dict											=    (i - 1) // (config['''num_res_blocks'''] + 1)
        _UpperCAmelCase							:  str											=    (i - 1) % (config['''num_res_blocks'''] + 1)

        _UpperCAmelCase							:  Dict											=    [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
        _UpperCAmelCase							:  str											=    [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

        if F'input_blocks.{i}.0.op.weight' in checkpoint:
            _UpperCAmelCase							:  Union[str, Any]											=    checkpoint[
                F'input_blocks.{i}.0.op.weight'
            ]
            _UpperCAmelCase							:  int											=    checkpoint[
                F'input_blocks.{i}.0.op.bias'
            ]
            continue

        _UpperCAmelCase							:  Optional[Any]											=    renew_resnet_paths(UpperCamelCase__							)
        _UpperCAmelCase							:  Optional[Any]											=    {'''old''': F'input_blocks.{i}.0', '''new''': F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
        _UpperCAmelCase							:  List[Any]											=    {'''old''': '''resnets.2.op''', '''new''': '''downsamplers.0.op'''}
        assign_to_checkpoint(
            UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       additional_replacements=[meta_path, resnet_op]     ,       config=UpperCamelCase__							)

        if len(UpperCamelCase__							):
            _UpperCAmelCase							:  int											=    renew_attention_paths(UpperCamelCase__							)
            _UpperCAmelCase							:  List[str]											=    {
                '''old''': F'input_blocks.{i}.1',
                '''new''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
            }
            _UpperCAmelCase							:  Tuple											=    {
                F'input_blocks.{i}.1.qkv.bias': {
                    '''key''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                    '''query''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                    '''value''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                },
                F'input_blocks.{i}.1.qkv.weight': {
                    '''key''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                    '''query''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                    '''value''': F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                },
            }
            assign_to_checkpoint(
                UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       additional_replacements=[meta_path]     ,       attention_paths_to_split=UpperCamelCase__     ,       config=UpperCamelCase__     ,       )

    _UpperCAmelCase							:  Optional[int]											=    middle_blocks[0]
    _UpperCAmelCase							:  Any											=    middle_blocks[1]
    _UpperCAmelCase							:  Union[str, Any]											=    middle_blocks[2]

    _UpperCAmelCase							:  Tuple											=    renew_resnet_paths(UpperCamelCase__							)
    assign_to_checkpoint(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       config=UpperCamelCase__							)

    _UpperCAmelCase							:  str											=    renew_resnet_paths(UpperCamelCase__							)
    assign_to_checkpoint(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       config=UpperCamelCase__							)

    _UpperCAmelCase							:  List[Any]											=    renew_attention_paths(UpperCamelCase__							)
    _UpperCAmelCase							:  Tuple											=    {
        '''middle_block.1.qkv.bias''': {
            '''key''': '''mid_block.attentions.0.key.bias''',
            '''query''': '''mid_block.attentions.0.query.bias''',
            '''value''': '''mid_block.attentions.0.value.bias''',
        },
        '''middle_block.1.qkv.weight''': {
            '''key''': '''mid_block.attentions.0.key.weight''',
            '''query''': '''mid_block.attentions.0.query.weight''',
            '''value''': '''mid_block.attentions.0.value.weight''',
        },
    }
    assign_to_checkpoint(
        UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       attention_paths_to_split=UpperCamelCase__     ,       config=UpperCamelCase__							)

    for i in range(UpperCamelCase__							):
        _UpperCAmelCase							:  Union[str, Any]											=    i // (config['''num_res_blocks'''] + 1)
        _UpperCAmelCase							:  str											=    i % (config['''num_res_blocks'''] + 1)
        _UpperCAmelCase							:  Optional[Any]											=    [shave_segments(UpperCamelCase__     ,       2							) for name in output_blocks[i]]
        _UpperCAmelCase							:  Optional[Any]											=    {}

        for layer in output_block_layers:
            _UpperCAmelCase							,				_UpperCAmelCase							:  Union[str, Any]											=    layer.split('''.'''							)[0], shave_segments(UpperCamelCase__     ,       1							)
            if layer_id in output_block_list:
                output_block_list[layer_id].append(UpperCamelCase__							)
            else:
                _UpperCAmelCase							:  List[Any]											=    [layer_name]

        if len(UpperCamelCase__							) > 1:
            _UpperCAmelCase							:  Dict											=    [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
            _UpperCAmelCase							:  Union[str, Any]											=    [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

            _UpperCAmelCase							:  Optional[int]											=    renew_resnet_paths(UpperCamelCase__							)
            _UpperCAmelCase							:  Optional[Any]											=    renew_resnet_paths(UpperCamelCase__							)

            _UpperCAmelCase							:  Optional[Any]											=    {'''old''': F'output_blocks.{i}.0', '''new''': F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
            assign_to_checkpoint(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       additional_replacements=[meta_path]     ,       config=UpperCamelCase__							)

            if ["conv.weight", "conv.bias"] in output_block_list.values():
                _UpperCAmelCase							:  Dict											=    list(output_block_list.values()							).index(['''conv.weight''', '''conv.bias''']							)
                _UpperCAmelCase							:  Optional[int]											=    checkpoint[
                    F'output_blocks.{i}.{index}.conv.weight'
                ]
                _UpperCAmelCase							:  int											=    checkpoint[
                    F'output_blocks.{i}.{index}.conv.bias'
                ]

                # Clear attentions as they have been attributed above.
                if len(UpperCamelCase__							) == 2:
                    _UpperCAmelCase							:  int											=    []

            if len(UpperCamelCase__							):
                _UpperCAmelCase							:  Dict											=    renew_attention_paths(UpperCamelCase__							)
                _UpperCAmelCase							:  Any											=    {
                    '''old''': F'output_blocks.{i}.1',
                    '''new''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                }
                _UpperCAmelCase							:  str											=    {
                    F'output_blocks.{i}.1.qkv.bias': {
                        '''key''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                        '''query''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                        '''value''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                    },
                    F'output_blocks.{i}.1.qkv.weight': {
                        '''key''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                        '''query''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                        '''value''': F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                    },
                }
                assign_to_checkpoint(
                    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       additional_replacements=[meta_path]     ,       attention_paths_to_split=to_split if any('''qkv''' in key for key in attentions							) else None     ,       config=UpperCamelCase__     ,       )
        else:
            _UpperCAmelCase							:  List[Any]											=    renew_resnet_paths(UpperCamelCase__     ,       n_shave_prefix_segments=1							)
            for path in resnet_0_paths:
                _UpperCAmelCase							:  str											=    '''.'''.join(['''output_blocks''', str(UpperCamelCase__							), path['''old''']]							)
                _UpperCAmelCase							:  Dict											=    '''.'''.join(['''up_blocks''', str(UpperCamelCase__							), '''resnets''', str(UpperCamelCase__							), path['''new''']]							)

                _UpperCAmelCase							:  Tuple											=    checkpoint[old_path]

    return new_checkpoint


if __name__ == "__main__":
  _lowerCAmelCase    :Optional[int]     =					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.')

  _lowerCAmelCase    :Optional[Any]     =					parser.parse_args()

  _lowerCAmelCase    :Optional[int]     =					torch.load(args.checkpoint_path)

  with open(args.config_file) as f:
    _lowerCAmelCase    :List[Any]     =					json.loads(f.read())

  _lowerCAmelCase    :Optional[Any]     =					convert_ldm_checkpoint(checkpoint, config)

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

  _lowerCAmelCase    :Union[str, Any]     =					UNetaDModel(**config)
  model.load_state_dict(converted_checkpoint)

  try:
    _lowerCAmelCase    :Dict     =					DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
    _lowerCAmelCase    :List[str]     =					VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

    _lowerCAmelCase    :Union[str, Any]     =					LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
    pipe.save_pretrained(args.dump_path)
  except:  # noqa: E722
    model.save_pretrained(args.dump_path)







"""simple docstring"""
import argparse
import os

import torch

from transformers.utils import WEIGHTS_NAME


_lowerCAmelCase    :int     =					['small', 'medium', 'large']

_lowerCAmelCase    :int     =					'lm_head.decoder.weight'
_lowerCAmelCase    :Dict     =					'lm_head.weight'





def  lowerCamelCase_						(UpperCamelCase__		:		str     ,       UpperCamelCase__		:		str							):
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[str]											=    d.pop(UpperCamelCase__							)
    os.makedirs(UpperCamelCase__     ,       exist_ok=UpperCamelCase__							)
    torch.save(UpperCamelCase__     ,       os.path.join(UpperCamelCase__     ,       UpperCamelCase__							)							)


if __name__ == "__main__":
  _lowerCAmelCase    :Dict     =					argparse.ArgumentParser()
  parser.add_argument('--dialogpt_path', default='.', type=str)
  _lowerCAmelCase    :str     =					parser.parse_args()
  for MODEL in DIALOGPT_MODELS:
    _lowerCAmelCase    :Tuple     =					os.path.join(args.dialogpt_path, f"{MODEL}_ft.pkl")
    _lowerCAmelCase    :int     =					f"./DialoGPT-{MODEL}"
    convert_dialogpt_checkpoint(
        checkpoint_path,
        pytorch_dump_folder_path,
    )


