import argparse

import torch

from diffusers import UNetaDConditionModel


def       a__      (					a  ,   a  ,   a  ,   a=None  ,   a=None  ,   a=None							)		->	str:
       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():
                     A_							:       List[Any]	      =				old_checkpoint[path]
                     A_							:       int	      =				old_tensor.shape[0] // 3

                     A_							:       Tuple	      =				(-1, channels) if len(old_tensor.shape							) == 3 else (-1)

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

                     A_							:       Optional[int]	      =				old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
                     A_      ,							A_      ,							A_							:       Any	      =				old_tensor.split(channels // num_heads  ,   dim=1							)

                     A_							:       Dict	      =				query.reshape(__UpperCamelCase							)
                     A_							:       int	      =				key.reshape(__UpperCamelCase							)
                     A_							:       Dict	      =				value.reshape(__UpperCamelCase							)

       for path in paths:
              A_							:       List[Any]	      =				path['''new''']

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
              A_							:       Optional[Any]	      =				old_checkpoint[path['''old''']]
              A_							:       str	      =				['''proj_attn.weight''']
              A_							:       int	      =				['''proj_out.weight''', '''proj_in.weight''']
              if any(k in new_path for k in names							):
                     A_							:       Dict	      =				weight[:, :, 0]
              elif any(k in new_path for k in names_a							) and len(weight.shape							) > 2 and ".attentions." not in new_path:
                     A_							:       str	      =				weight[:, :, 0]
              else:
                     A_							:       str	      =				weight


def       a__      (					a  ,   a=0							)		->	Any:
       A_							:       Optional[int]	      =				[]
       for old_item in old_list:
              A_							:       Union[str, Any]	      =				old_item

              #         new_item = new_item.replace('norm.weight', 'group_norm.weight')
              #         new_item = new_item.replace('norm.bias', 'group_norm.bias')

              #         new_item = new_item.replace('proj_out.weight', 'proj_attn.weight')
              #         new_item = new_item.replace('proj_out.bias', 'proj_attn.bias')

              #         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

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

       return mapping


def       a__      (					a  ,   a=1							)		->	Union[str, 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       a__      (					a  ,   a=0							)		->	Dict:
       A_							:       Dict	      =				[]
       for old_item in old_list:
              mapping.append({'''old''': old_item, '''new''': old_item}							)

       return mapping


def       a__      (					a  ,   a=0							)		->	List[str]:
       A_							:       Optional[int]	      =				[]
       for old_item in old_list:
              A_							:       List[str]	      =				old_item.replace('''in_layers.0'''  ,   '''norm1'''							)
              A_							:       Optional[int]	      =				new_item.replace('''in_layers.2'''  ,   '''conv1'''							)

              A_							:       Any	      =				new_item.replace('''out_layers.0'''  ,   '''norm2'''							)
              A_							:       str	      =				new_item.replace('''out_layers.3'''  ,   '''conv2'''							)

              A_							:       Union[str, Any]	      =				new_item.replace('''emb_layers.1'''  ,   '''time_emb_proj'''							)
              A_							:       Tuple	      =				new_item.replace('''skip_connection'''  ,   '''conv_shortcut'''							)

              A_							:       Dict	      =				shave_segments(__UpperCamelCase  ,   n_shave_prefix_segments=__UpperCamelCase							)

              if "temopral_conv" not in old_item:
                     mapping.append({'''old''': old_item, '''new''': new_item}							)

       return mapping


def       a__      (					a  ,   a  ,   a=None  ,   a=False							)		->	Dict:
       A_							:       Optional[Any]	      =				{}
       A_							:       List[str]	      =				list(checkpoint.keys()							)

       A_							:       int	      =				'''model.diffusion_model.'''

       # at least a 100 parameters have to start with `model_ema` in order for the checkpoint to be EMA
       if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_0_0 and extract_ema:
              print(f"""Checkpoint {path} has both EMA and non-EMA weights."""							)
              print(
                  '''In this conversion only the EMA weights are extracted. If you want to instead extract the non-EMA'''
                  ''' weights (useful to continue fine-tuning), please make sure to remove the `--extract_ema` flag.'''							)
              for key in keys:
                     if key.startswith('''model.diffusion_model'''							):
                            A_							:       str	      =				'''model_ema.''' + ''''''.join(key.split('''.'''							)[1:]							)
                            A_							:       Optional[int]	      =				checkpoint.pop(__UpperCamelCase							)
       else:
              if sum(k.startswith('''model_ema'''							) for k in keys							) > 1_0_0:
                     print(
                         '''In this conversion only the non-EMA weights are extracted. If you want to instead extract the EMA'''
                         ''' weights (usually better for inference), please make sure to add the `--extract_ema` flag.'''							)

              for key in keys:
                     A_							:       int	      =				checkpoint.pop(__UpperCamelCase							)

       A_							:       Union[str, Any]	      =				{}

       A_							:       Any	      =				unet_state_dict['''time_embed.0.weight''']
       A_							:       Optional[Any]	      =				unet_state_dict['''time_embed.0.bias''']
       A_							:       Optional[Any]	      =				unet_state_dict['''time_embed.2.weight''']
       A_							:       Optional[int]	      =				unet_state_dict['''time_embed.2.bias''']

       if config["class_embed_type"] is None:
              # No parameters to port
              ...
       elif config["class_embed_type"] == "timestep" or config["class_embed_type"] == "projection":
              A_							:       List[Any]	      =				unet_state_dict['''label_emb.0.0.weight''']
              A_							:       Union[str, Any]	      =				unet_state_dict['''label_emb.0.0.bias''']
              A_							:       Union[str, Any]	      =				unet_state_dict['''label_emb.0.2.weight''']
              A_							:       List[str]	      =				unet_state_dict['''label_emb.0.2.bias''']
       else:
              raise NotImplementedError(f"""Not implemented `class_embed_type`: {config['class_embed_type']}"""							)

       A_							:       Union[str, Any]	      =				unet_state_dict['''input_blocks.0.0.weight''']
       A_							:       List[Any]	      =				unet_state_dict['''input_blocks.0.0.bias''']

       A_							:       Tuple	      =				[v for v in unet_state_dict if v.startswith('''input_blocks.0.1'''							)]
       A_							:       int	      =				renew_attention_paths(__UpperCamelCase							)
       A_							:       Any	      =				{'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
       assign_to_checkpoint(__UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

       A_							:       Any	      =				unet_state_dict['''out.0.weight''']
       A_							:       List[str]	      =				unet_state_dict['''out.0.bias''']
       A_							:       Union[str, Any]	      =				unet_state_dict['''out.2.weight''']
       A_							:       Dict	      =				unet_state_dict['''out.2.bias''']

       # Retrieves the keys for the input blocks only
       A_							:       Optional[Any]	      =				len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''input_blocks''' in layer}							)
       A_							:       List[str]	      =				{
           layer_id: [key for key in unet_state_dict if f"""input_blocks.{layer_id}""" in key]
           for layer_id in range(__UpperCamelCase							)
       }

       # Retrieves the keys for the middle blocks only
       A_							:       str	      =				len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''middle_block''' in layer}							)
       A_							:       Tuple	      =				{
           layer_id: [key for key in unet_state_dict if f"""middle_block.{layer_id}""" in key]
           for layer_id in range(__UpperCamelCase							)
       }

       # Retrieves the keys for the output blocks only
       A_							:       str	      =				len({'''.'''.join(layer.split('''.'''							)[:2]							) for layer in unet_state_dict if '''output_blocks''' in layer}							)
       A_							:       Union[str, Any]	      =				{
           layer_id: [key for key in unet_state_dict if f"""output_blocks.{layer_id}""" in key]
           for layer_id in range(__UpperCamelCase							)
       }

       for i in range(1  ,   __UpperCamelCase							):
              A_							:       str	      =				(i - 1) // (config['''layers_per_block'''] + 1)
              A_							:       int	      =				(i - 1) % (config['''layers_per_block'''] + 1)

              A_							:       Optional[int]	      =				[
                  key for key in input_blocks[i] if f"""input_blocks.{i}.0""" in key and f"""input_blocks.{i}.0.op""" not in key
              ]
              A_							:       Tuple	      =				[key for key in input_blocks[i] if f"""input_blocks.{i}.1""" in key]
              A_							:       List[Any]	      =				[key for key in input_blocks[i] if f"""input_blocks.{i}.2""" in key]

              if f"""input_blocks.{i}.op.weight""" in unet_state_dict:
                     A_							:       int	      =				unet_state_dict.pop(
                         f"""input_blocks.{i}.op.weight"""							)
                     A_							:       Optional[Any]	      =				unet_state_dict.pop(
                         f"""input_blocks.{i}.op.bias"""							)

              A_							:       List[str]	      =				renew_resnet_paths(__UpperCamelCase							)
              A_							:       Dict	      =				{'''old''': f"""input_blocks.{i}.0""", '''new''': f"""down_blocks.{block_id}.resnets.{layer_in_block_id}"""}
              assign_to_checkpoint(
                  __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

              A_							:       str	      =				[key for key in resnets if '''temopral_conv''' in key]
              A_							:       Any	      =				renew_temp_conv_paths(__UpperCamelCase							)
              A_							:       Any	      =				{
                  '''old''': f"""input_blocks.{i}.0.temopral_conv""",
                  '''new''': f"""down_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
              }
              assign_to_checkpoint(
                  __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

              if len(__UpperCamelCase							):
                     A_							:       Union[str, Any]	      =				renew_attention_paths(__UpperCamelCase							)
                     A_							:       List[str]	      =				{'''old''': f"""input_blocks.{i}.1""", '''new''': f"""down_blocks.{block_id}.attentions.{layer_in_block_id}"""}
                     assign_to_checkpoint(
                         __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

              if len(__UpperCamelCase							):
                     A_							:       List[str]	      =				renew_attention_paths(__UpperCamelCase							)
                     A_							:       Optional[Any]	      =				{
                         '''old''': f"""input_blocks.{i}.2""",
                         '''new''': f"""down_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
                     }
                     assign_to_checkpoint(
                         __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

       A_							:       Dict	      =				middle_blocks[0]
       A_							:       Dict	      =				[key for key in resnet_a if '''temopral_conv''' in key]
       A_							:       int	      =				middle_blocks[1]
       A_							:       List[Any]	      =				middle_blocks[2]
       A_							:       Tuple	      =				middle_blocks[3]
       A_							:       Optional[Any]	      =				[key for key in resnet_a if '''temopral_conv''' in key]

       A_							:       str	      =				renew_resnet_paths(__UpperCamelCase							)
       A_							:       int	      =				{'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   config=__UpperCamelCase  ,   additional_replacements=[meta_path]							)

       A_							:       int	      =				renew_temp_conv_paths(__UpperCamelCase							)
       A_							:       List[Any]	      =				{'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   config=__UpperCamelCase  ,   additional_replacements=[meta_path]							)

       A_							:       Dict	      =				renew_resnet_paths(__UpperCamelCase							)
       A_							:       Union[str, Any]	      =				{'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   config=__UpperCamelCase  ,   additional_replacements=[meta_path]							)

       A_							:       Union[str, Any]	      =				renew_temp_conv_paths(__UpperCamelCase							)
       A_							:       str	      =				{'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   config=__UpperCamelCase  ,   additional_replacements=[meta_path]							)

       A_							:       List[str]	      =				renew_attention_paths(__UpperCamelCase							)
       A_							:       Union[str, Any]	      =				{'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

       A_							:       Optional[int]	      =				renew_attention_paths(__UpperCamelCase							)
       A_							:       Optional[int]	      =				{'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
       assign_to_checkpoint(
           __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

       for i in range(__UpperCamelCase							):
              A_							:       List[str]	      =				i // (config['''layers_per_block'''] + 1)
              A_							:       int	      =				i % (config['''layers_per_block'''] + 1)
              A_							:       List[Any]	      =				[shave_segments(__UpperCamelCase  ,   2							) for name in output_blocks[i]]
              A_							:       Union[str, Any]	      =				{}

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

              if len(__UpperCamelCase							) > 1:
                     A_							:       Tuple	      =				[key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
                     A_							:       str	      =				[key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
                     A_							:       Optional[Any]	      =				[key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

                     A_							:       List[str]	      =				renew_resnet_paths(__UpperCamelCase							)
                     A_							:       List[str]	      =				renew_resnet_paths(__UpperCamelCase							)

                     A_							:       str	      =				{'''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							)

                     A_							:       str	      =				[key for key in resnets if '''temopral_conv''' in key]
                     A_							:       List[str]	      =				renew_temp_conv_paths(__UpperCamelCase							)
                     A_							:       int	      =				{
                         '''old''': f"""output_blocks.{i}.0.temopral_conv""",
                         '''new''': f"""up_blocks.{block_id}.temp_convs.{layer_in_block_id}""",
                     }
                     assign_to_checkpoint(
                         __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

                     A_							:       Optional[Any]	      =				{k: sorted(__UpperCamelCase							) for k, v in output_block_list.items()}
                     if ["conv.bias", "conv.weight"] in output_block_list.values():
                            A_							:       Dict	      =				list(output_block_list.values()							).index(['''conv.bias''', '''conv.weight''']							)
                            A_							:       Tuple	      =				unet_state_dict[
                                f"""output_blocks.{i}.{index}.conv.weight"""
                            ]
                            A_							:       Any	      =				unet_state_dict[
                                f"""output_blocks.{i}.{index}.conv.bias"""
                            ]

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

                     if len(__UpperCamelCase							):
                            A_							:       Dict	      =				renew_attention_paths(__UpperCamelCase							)
                            A_							:       List[Any]	      =				{
                                '''old''': f"""output_blocks.{i}.1""",
                                '''new''': f"""up_blocks.{block_id}.attentions.{layer_in_block_id}""",
                            }
                            assign_to_checkpoint(
                                __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)

                     if len(__UpperCamelCase							):
                            A_							:       Optional[Any]	      =				renew_attention_paths(__UpperCamelCase							)
                            A_							:       int	      =				{
                                '''old''': f"""output_blocks.{i}.2""",
                                '''new''': f"""up_blocks.{block_id}.temp_attentions.{layer_in_block_id}""",
                            }
                            assign_to_checkpoint(
                                __UpperCamelCase  ,   __UpperCamelCase  ,   __UpperCamelCase  ,   additional_replacements=[meta_path]  ,   config=__UpperCamelCase							)
              else:
                     A_							:       str	      =				renew_resnet_paths(__UpperCamelCase  ,   n_shave_prefix_segments=1							)
                     for path in resnet_0_paths:
                            A_							:       List[Any]	      =				'''.'''.join(['''output_blocks''', str(__UpperCamelCase							), path['''old''']]							)
                            A_							:       Any	      =				'''.'''.join(['''up_blocks''', str(__UpperCamelCase							), '''resnets''', str(__UpperCamelCase							), path['''new''']]							)
                            A_							:       Optional[int]	      =				unet_state_dict[old_path]

                     A_							:       str	      =				[l for l in output_block_layers if '''temopral_conv''' in l]
                     for path in temopral_conv_paths:
                            A_							:       Any	      =				path.split('''temopral_conv.'''							)[-1]
                            A_							:       Union[str, Any]	      =				'''.'''.join(['''output_blocks''', str(__UpperCamelCase							), str(__UpperCamelCase							), '''temopral_conv''', pruned_path]							)
                            A_							:       List[Any]	      =				'''.'''.join(['''up_blocks''', str(__UpperCamelCase							), '''temp_convs''', str(__UpperCamelCase							), pruned_path]							)
                            A_							:       Any	      =				unet_state_dict[old_path]

       return new_checkpoint


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

    parser.add_argument(
        '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
    )
    parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
    _lowerCAmelCase        =       parser.parse_args()

    _lowerCAmelCase        =       torch.load(args.checkpoint_path, map_location='cpu')
    _lowerCAmelCase        =       UNetaDConditionModel()

    _lowerCAmelCase        =       convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

    _lowerCAmelCase        =       set(unet.state_dict().keys()) - set(converted_ckpt.keys())
    _lowerCAmelCase        =       set(converted_ckpt.keys()) - set(unet.state_dict().keys())

    assert len(diff_a) == len(diff_a) == 0, "Converted weights don't match"

    # load state_dict
    unet.load_state_dict(converted_ckpt)

    unet.save_pretrained(args.dump_path)

    # -- finish converting the unet --





"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCAmelCase				      =   {'''tokenization_phobert''': ['''PhobertTokenizer''']}


if TYPE_CHECKING:
							from .tokenization_phobert import PhobertTokenizer

else:
							import sys

							__lowerCAmelCase				      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)