






import argparse

import torch

from diffusers import UNetaDConditionModel




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase    ,	_lowercase=None    ,	_lowercase=None    ,	_lowercase=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():
															_A									=       old_checkpoint[path]
															_A									=       old_tensor.shape[0] // 3

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

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

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

															_A									=       query.reshape(_lowercase       )
															_A									=       key.reshape(_lowercase       )
															_A									=       value.reshape(_lowercase       )

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

        # proj_attn.weight has to be converted from conv 1D to linear
										_A									=       old_checkpoint[path['''old''']]
										_A									=       ['''proj_attn.weight''']
										_A									=       ['''proj_out.weight''', '''proj_in.weight''']
										if any(k in new_path for k in names       ):
															_A									=       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									=       weight[:, :, 0]
										else:
															_A									=       weight




def    __A    (   _lowercase    ,	_lowercase=0       ):

					'''simple docstring'''







					_A									=       []
					for old_item in old_list:
										_A									=       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    (   _lowercase    ,	_lowercase=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    __A    (   _lowercase    ,	_lowercase=0       ):

					'''simple docstring'''







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

					return mapping




def    __A    (   _lowercase    ,	_lowercase=0       ):

					'''simple docstring'''







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

										_A									=       new_item.replace('''out_layers.0'''    ,	'''norm2'''       )
										_A									=       new_item.replace('''out_layers.3'''    ,	'''conv2'''       )

										_A									=       new_item.replace('''emb_layers.1'''    ,	'''time_emb_proj'''       )
										_A									=       new_item.replace('''skip_connection'''    ,	'''conv_shortcut'''       )

										_A									=       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    __A    (   _lowercase    ,	_lowercase    ,	_lowercase=None    ,	_lowercase=False       ):

					'''simple docstring'''







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

					_A									=       '''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_00 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									=       '''model_ema.''' + ''''''.join(key.split('''.'''       )[1:]       )
																				_A									=       checkpoint.pop(_lowercase       )
					else:
										if sum(k.startswith('''model_ema'''       ) for k in keys       ) > 1_00:
															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									=       checkpoint.pop(_lowercase       )

					_A									=       {}

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

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

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

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

					# Retrieves the keys for the input blocks only
					_A									=       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in unet_state_dict if '''input_blocks''' in layer}       )
					_A									=       {
					    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
					_A									=       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in unet_state_dict if '''middle_block''' in layer}       )
					_A									=       {
					    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
					_A									=       len({'''.'''.join(layer.split('''.'''       )[:2]       ) for layer in unet_state_dict if '''output_blocks''' in layer}       )
					_A									=       {
					    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       ):
										_A									=       (i - 1) // (config['''layers_per_block'''] + 1)
										_A									=       (i - 1) % (config['''layers_per_block'''] + 1)

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

										_A									=       renew_resnet_paths(_lowercase       )
										_A									=       {'''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       )

										_A									=       [key for key in resnets if '''temopral_conv''' in key]
										_A									=       renew_temp_conv_paths(_lowercase       )
										_A									=       {
										    '''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       ):
															_A									=       renew_attention_paths(_lowercase       )
															_A									=       {'''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       ):
															_A									=       renew_attention_paths(_lowercase       )
															_A									=       {
															    '''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       )

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

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

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

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

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

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

					_A									=       renew_attention_paths(_lowercase       )
					_A									=       {'''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       ):
										_A									=       i // (config['''layers_per_block'''] + 1)
										_A									=       i % (config['''layers_per_block'''] + 1)
										_A									=       [shave_segments(_lowercase    ,	2       ) for name in output_blocks[i]]
										_A									=       {}

										for layer in output_block_layers:
															_A							,_A									=       layer.split('''.'''       )[0], shave_segments(_lowercase    ,	1       )
															if layer_id in output_block_list:
																				output_block_list[layer_id].append(_lowercase       )
															else:
																				_A									=       [layer_name]

										if len(_lowercase       ) > 1:
															_A									=       [key for key in output_blocks[i] if f"""output_blocks.{i}.0""" in key]
															_A									=       [key for key in output_blocks[i] if f"""output_blocks.{i}.1""" in key]
															_A									=       [key for key in output_blocks[i] if f"""output_blocks.{i}.2""" in key]

															_A									=       renew_resnet_paths(_lowercase       )
															_A									=       renew_resnet_paths(_lowercase       )

															_A									=       {'''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       )

															_A									=       [key for key in resnets if '''temopral_conv''' in key]
															_A									=       renew_temp_conv_paths(_lowercase       )
															_A									=       {
															    '''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       )

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

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

															if len(_lowercase       ):
																				_A									=       renew_attention_paths(_lowercase       )
																				_A									=       {
																				    '''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       ):
																				_A									=       renew_attention_paths(_lowercase       )
																				_A									=       {
																				    '''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:
															_A									=       renew_resnet_paths(_lowercase    ,	n_shave_prefix_segments=1       )
															for path in resnet_0_paths:
																				_A									=       '''.'''.join(['''output_blocks''', str(_lowercase       ), path['''old''']]       )
																				_A									=       '''.'''.join(['''up_blocks''', str(_lowercase       ), '''resnets''', str(_lowercase       ), path['''new''']]       )
																				_A									=       unet_state_dict[old_path]

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

					return new_checkpoint


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

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

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

				__A          = convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

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











class   SCREAMING_SNAKE_CASE :





							"""simple docstring"""

							def __init__(     self:      Any      )			-> None:
												# numbermap keys are the number and its values are lists of indexes sorted
												# in ascending order
												_A									=       {}
												# indexmap keys are an index and it's values are the number at that index
												_A									=       {}

							def 					__A       (     self:      Any						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_delete() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == item:
																						array.pop(__A      )
																						return array
																	elif array[mid] < item:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1
												raise ValueError(
												    '''Either the item is not in the array or the array was unsorted'''      )

							def 					__A       (     self:      int						,   __A:      list | str | range						,   __A:      int      )			-> list[int]:
												if isinstance(__A						,   (range, str)      ):
																	_A									=       list(__A      )
												elif not isinstance(__A						,   __A      ):
																	raise TypeError(
																	    '''binary_search_insert() only accepts either a list, range or str'''      )

												_A									=       0
												_A									=       len(__A      ) - 1

												while low <= high:
																	_A									=       (low + high) // 2
																	if array[mid] == index:
																						# If the item already exists in the array,
																						# insert it after the existing item
																						array.insert(mid + 1						,   __A      )
																						return array
																	elif array[mid] < index:
																						_A									=       mid + 1
																	else:
																						_A									=       mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
												array.insert(__A						,   __A      )
												return array

							def 					__A       (     self:      Union[str, Any]						,   __A:      int						,   __A:      int      )			-> None:
												# Remove previous index
												if index in self.indexmap:
																	_A									=       self.indexmap[index]
																	if len(self.numbermap[n]      ) == 1:
																						del self.numbermap[n]
																	else:
																						_A									=       self.binary_search_delete(self.numbermap[n]						,   __A      )

        # Set new index
												_A									=       number

												# Number not seen before or empty so insert number value
												if number not in self.numbermap:
																	_A									=       [index]

												# Here we need to perform a binary search insertion in order to insert
												# The item in the correct place
												else:
																	_A									=       self.binary_search_insert(
																	    self.numbermap[number]						,   __A      )



							def 					__A       (     self:      List[Any]						,   __A:      int      )			-> int:
												return self.numbermap.get(__A						,   [-1]      )[0]


if __name__ == "__main__":
				import doctest

				doctest.testmod()



