




'''simple docstring'''





import argparse

import torch

from diffusers import UNetaDConditionModel


def   UpperCamelCase_(			snake_case							: List[str]							,       snake_case							: Optional[Any]							,       snake_case							: Optional[int]							,       snake_case							: Optional[Any]=None							,       snake_case							: List[Any]=None							,       snake_case							: Any=None  ):

       '''simple docstring'''






       assert isinstance(lowercase_							,       lowercase_  ), "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():
                     snake_case_			      =   old_checkpoint[path]
                     snake_case_			      =   old_tensor.shape[0] // 3

                     snake_case_			      =   (-1, channels) if len(old_tensor.shape  ) == 3 else (-1)

                     snake_case_			      =   old_tensor.shape[0] // config['''num_head_channels'''] // 3

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

                     snake_case_			      =   query.reshape(lowercase_  )
                     snake_case_			      =   key.reshape(lowercase_  )
                     snake_case_			      =   value.reshape(lowercase_  )

       for path in paths:
              snake_case_			      =   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:
                            snake_case_			      =   new_path.replace(replacement["old"]							,       replacement["new"]  )

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


def   UpperCamelCase_(			snake_case							: List[str]							,       snake_case							: Tuple=0  ):

       '''simple docstring'''






       snake_case_			      =   []
       for old_item in old_list:
              snake_case_			      =   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   UpperCamelCase_(			snake_case							: int							,       snake_case							: str=1  ):

       '''simple docstring'''






       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_(			snake_case							: List[Any]							,       snake_case							: Union[str, Any]=0  ):

       '''simple docstring'''






       snake_case_			      =   []
       for old_item in old_list:
              mapping.append({"old": old_item, "new": old_item}  )

       return mapping


def   UpperCamelCase_(			snake_case							: List[Any]							,       snake_case							: Optional[int]=0  ):

       '''simple docstring'''






       snake_case_			      =   []
       for old_item in old_list:
              snake_case_			      =   old_item.replace("in_layers.0"							,       "norm1"  )
              snake_case_			      =   new_item.replace("in_layers.2"							,       "conv1"  )

              snake_case_			      =   new_item.replace("out_layers.0"							,       "norm2"  )
              snake_case_			      =   new_item.replace("out_layers.3"							,       "conv2"  )

              snake_case_			      =   new_item.replace("emb_layers.1"							,       "time_emb_proj"  )
              snake_case_			      =   new_item.replace("skip_connection"							,       "conv_shortcut"  )

              snake_case_			      =   shave_segments(lowercase_							,       n_shave_prefix_segments=lowercase_  )

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

       return mapping


def   UpperCamelCase_(			snake_case							: Union[str, Any]							,       snake_case							: List[str]							,       snake_case							: Any=None							,       snake_case							: int=False  ):

       '''simple docstring'''






       snake_case_			      =   {}
       snake_case_			      =   list(checkpoint.keys()  )

       snake_case_			      =   '''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"  ):
                            snake_case_			      =   '''model_ema.''' + ''''''.join(key.split("."  )[1:]  )
                            snake_case_			      =   checkpoint.pop(lowercase_  )
       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:
                     snake_case_			      =   checkpoint.pop(lowercase_  )

       snake_case_			      =   {}

       snake_case_			      =   unet_state_dict['''time_embed.0.weight''']
       snake_case_			      =   unet_state_dict['''time_embed.0.bias''']
       snake_case_			      =   unet_state_dict['''time_embed.2.weight''']
       snake_case_			      =   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":
              snake_case_			      =   unet_state_dict['''label_emb.0.0.weight''']
              snake_case_			      =   unet_state_dict['''label_emb.0.0.bias''']
              snake_case_			      =   unet_state_dict['''label_emb.0.2.weight''']
              snake_case_			      =   unet_state_dict['''label_emb.0.2.bias''']
       else:
              raise NotImplementedError(f'Not implemented `class_embed_type`: {config["class_embed_type"]}'  )

       snake_case_			      =   unet_state_dict['''input_blocks.0.0.weight''']
       snake_case_			      =   unet_state_dict['''input_blocks.0.0.bias''']

       snake_case_			      =   [v for v in unet_state_dict if v.startswith("input_blocks.0.1"  )]
       snake_case_			      =   renew_attention_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''input_blocks.0.1''', '''new''': '''transformer_in'''}
       assign_to_checkpoint(lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

       snake_case_			      =   unet_state_dict['''out.0.weight''']
       snake_case_			      =   unet_state_dict['''out.0.bias''']
       snake_case_			      =   unet_state_dict['''out.2.weight''']
       snake_case_			      =   unet_state_dict['''out.2.bias''']

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

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

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

       for i in range(1							,       lowercase_  ):
              snake_case_			      =   (i - 1) // (config['''layers_per_block'''] + 1)
              snake_case_			      =   (i - 1) % (config['''layers_per_block'''] + 1)

              snake_case_			      =   [
                  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
              ]
              snake_case_			      =   [key for key in input_blocks[i] if f'input_blocks.{i}.1' in key]
              snake_case_			      =   [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:
                     snake_case_			      =   unet_state_dict.pop(
                         f'input_blocks.{i}.op.weight'  )
                     snake_case_			      =   unet_state_dict.pop(
                         f'input_blocks.{i}.op.bias'  )

              snake_case_			      =   renew_resnet_paths(lowercase_  )
              snake_case_			      =   {'''old''': f'input_blocks.{i}.0', '''new''': f'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
              assign_to_checkpoint(
                  lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

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

              if len(lowercase_  ):
                     snake_case_			      =   renew_attention_paths(lowercase_  )
                     snake_case_			      =   {'''old''': f'input_blocks.{i}.1', '''new''': f'down_blocks.{block_id}.attentions.{layer_in_block_id}'}
                     assign_to_checkpoint(
                         lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

              if len(lowercase_  ):
                     snake_case_			      =   renew_attention_paths(lowercase_  )
                     snake_case_			      =   {
                         '''old''': f'input_blocks.{i}.2',
                         '''new''': f'down_blocks.{block_id}.temp_attentions.{layer_in_block_id}',
                     }
                     assign_to_checkpoint(
                         lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

       snake_case_			      =   middle_blocks[0]
       snake_case_			      =   [key for key in resnet_a if '''temopral_conv''' in key]
       snake_case_			      =   middle_blocks[1]
       snake_case_			      =   middle_blocks[2]
       snake_case_			      =   middle_blocks[3]
       snake_case_			      =   [key for key in resnet_a if '''temopral_conv''' in key]

       snake_case_			      =   renew_resnet_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.0''', '''new''': '''mid_block.resnets.0'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       config=lowercase_							,       additional_replacements=[meta_path]  )

       snake_case_			      =   renew_temp_conv_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.0.temopral_conv''', '''new''': '''mid_block.temp_convs.0'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       config=lowercase_							,       additional_replacements=[meta_path]  )

       snake_case_			      =   renew_resnet_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.3''', '''new''': '''mid_block.resnets.1'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       config=lowercase_							,       additional_replacements=[meta_path]  )

       snake_case_			      =   renew_temp_conv_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.3.temopral_conv''', '''new''': '''mid_block.temp_convs.1'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       config=lowercase_							,       additional_replacements=[meta_path]  )

       snake_case_			      =   renew_attention_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.1''', '''new''': '''mid_block.attentions.0'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

       snake_case_			      =   renew_attention_paths(lowercase_  )
       snake_case_			      =   {'''old''': '''middle_block.2''', '''new''': '''mid_block.temp_attentions.0'''}
       assign_to_checkpoint(
           lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

       for i in range(lowercase_  ):
              snake_case_			      =   i // (config['''layers_per_block'''] + 1)
              snake_case_			      =   i % (config['''layers_per_block'''] + 1)
              snake_case_			      =   [shave_segments(lowercase_							,       2  ) for name in output_blocks[i]]
              snake_case_			      =   {}

              for layer in output_block_layers:
                     snake_case_			      =   layer.split("."  )[0], shave_segments(lowercase_							,       1  )
                     if layer_id in output_block_list:
                            output_block_list[layer_id].append(lowercase_  )
                     else:
                            snake_case_			      =   [layer_name]

              if len(lowercase_  ) > 1:
                     snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.0' in key]
                     snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.1' in key]
                     snake_case_			      =   [key for key in output_blocks[i] if f'output_blocks.{i}.2' in key]

                     snake_case_			      =   renew_resnet_paths(lowercase_  )
                     snake_case_			      =   renew_resnet_paths(lowercase_  )

                     snake_case_			      =   {'''old''': f'output_blocks.{i}.0', '''new''': f'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
                     assign_to_checkpoint(
                         lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

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

                     snake_case_			      =   {k: sorted(lowercase_  ) for k, v in output_block_list.items()}
                     if ["conv.bias", "conv.weight"] in output_block_list.values():
                            snake_case_			      =   list(output_block_list.values()  ).index(["conv.bias", "conv.weight"]  )
                            snake_case_			      =   unet_state_dict[
                                f'output_blocks.{i}.{index}.conv.weight'
                            ]
                            snake_case_			      =   unet_state_dict[
                                f'output_blocks.{i}.{index}.conv.bias'
                            ]

                            # Clear attentions as they have been attributed above.
                            if len(lowercase_  ) == 2:
                                   snake_case_			      =   []

                     if len(lowercase_  ):
                            snake_case_			      =   renew_attention_paths(lowercase_  )
                            snake_case_			      =   {
                                '''old''': f'output_blocks.{i}.1',
                                '''new''': f'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                            }
                            assign_to_checkpoint(
                                lowercase_							,       lowercase_							,       lowercase_							,       additional_replacements=[meta_path]							,       config=lowercase_  )

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

                     snake_case_			      =   [l for l in output_block_layers if '''temopral_conv''' in l]
                     for path in temopral_conv_paths:
                            snake_case_			      =   path.split("temopral_conv."  )[-1]
                            snake_case_			      =   '''.'''.join(["output_blocks", str(lowercase_  ), str(lowercase_  ), "temopral_conv", pruned_path]  )
                            snake_case_			      =   '''.'''.join(["up_blocks", str(lowercase_  ), "temp_convs", str(lowercase_  ), pruned_path]  )
                            snake_case_			      =   unet_state_dict[old_path]

       return new_checkpoint


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE							:     Any   	=				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.")
  _SCREAMING_SNAKE_CASE							:     int   	=				parser.parse_args()

  _SCREAMING_SNAKE_CASE							:     Tuple   	=				torch.load(args.checkpoint_path, map_location="cpu")
  _SCREAMING_SNAKE_CASE							:     Dict   	=				UNetaDConditionModel()

  _SCREAMING_SNAKE_CASE							:     Optional[int]   	=				convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

  _SCREAMING_SNAKE_CASE							:     Union[str, Any]   	=				set(unet.state_dict().keys()) - set(converted_ckpt.keys())
  _SCREAMING_SNAKE_CASE							:     Tuple   	=				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'''





import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from unittest.mock import Mock, patch

import diffusers.utils.hub_utils




class 				_snake_case	(			unittest.TestCase							):




			@patch("diffusers.utils.hub_utils.get_full_repo_name"							)
			def      lowerCAmelCase__       (      self       ,							a__							)       ->       None:

										'''simple docstring'''




										snake_case_			      =   "full_repo_name"
										with TemporaryDirectory() as tmpdir:
																	# Dummy args values
																	snake_case_			      =   Mock()
																	snake_case_			      =   tmpdir
																	snake_case_			      =   0
																	snake_case_			      =   "hub_token"
																	snake_case_			      =   "dataset_name"
																	snake_case_			      =   0.0_1
																	snake_case_			      =   100_000
																	snake_case_			      =   10_000
																	snake_case_			      =   0.0_1
																	snake_case_			      =   0.0_2
																	snake_case_			      =   0.0_3
																	snake_case_			      =   0.0_0_0_5
																	snake_case_			      =   0.0_0_0_0_0_1
																	snake_case_			      =   1
																	snake_case_			      =   10
																	snake_case_			      =   0.0_0_1
																	snake_case_			      =   0.1
																	snake_case_			      =   0.2
																	snake_case_			      =   True

																	# Model card mush be rendered and saved
																	diffusers.utils.hub_utils.create_model_card(a__       ,							model_name="model_name"							)
																	self.assertTrue((Path(a__							) / "README.md").is_file()							)




