




import argparse

import torch

from diffusers import UNetaDConditionModel


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=None						,	lowerCAmelCase=None						,	lowerCAmelCase=None   )->  Tuple:
		assert isinstance(lowerCAmelCase						,	lowerCAmelCase   ), "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():
						SCREAMING_SNAKE_CASE_							=       old_checkpoint[path]
						SCREAMING_SNAKE_CASE_							=       old_tensor.shape[0] // 3

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

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

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

						SCREAMING_SNAKE_CASE_							=       query.reshape(lowerCAmelCase   )
						SCREAMING_SNAKE_CASE_							=       key.reshape(lowerCAmelCase   )
						SCREAMING_SNAKE_CASE_							=       value.reshape(lowerCAmelCase   )

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

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


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=0   )->  Optional[Any]:
		SCREAMING_SNAKE_CASE_							=       []
		for old_item in old_list:
				SCREAMING_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 				_a       (				lowerCAmelCase						,	lowerCAmelCase=1   )->  int:
		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       (				lowerCAmelCase						,	lowerCAmelCase=0   )->  Tuple:
		SCREAMING_SNAKE_CASE_							=       []
		for old_item in old_list:
				mapping.append({'old': old_item, 'new': old_item}   )

		return mapping


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=0   )->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       []
		for old_item in old_list:
				SCREAMING_SNAKE_CASE_							=       old_item.replace('in_layers.0'						,	'norm1'   )
				SCREAMING_SNAKE_CASE_							=       new_item.replace('in_layers.2'						,	'conv1'   )

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

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

				SCREAMING_SNAKE_CASE_							=       shave_segments(lowerCAmelCase						,	n_shave_prefix_segments=lowerCAmelCase   )

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

		return mapping


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=None						,	lowerCAmelCase=False   )->  Any:
		SCREAMING_SNAKE_CASE_							=       {}
		SCREAMING_SNAKE_CASE_							=       list(checkpoint.keys()   )

		SCREAMING_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   ) > 100 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'   ):
								SCREAMING_SNAKE_CASE_							=       'model_ema.' + ''.join(key.split('.'   )[1:]   )
								SCREAMING_SNAKE_CASE_							=       checkpoint.pop(lowerCAmelCase   )
		else:
				if sum(k.startswith('model_ema'   ) for k in keys   ) > 100:
						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:
						SCREAMING_SNAKE_CASE_							=       checkpoint.pop(lowerCAmelCase   )

		SCREAMING_SNAKE_CASE_							=       {}

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

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

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

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

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

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

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

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

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

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

				SCREAMING_SNAKE_CASE_							=       [key for key in resnets if 'temopral_conv' in key]
				SCREAMING_SNAKE_CASE_							=       renew_temp_conv_paths(lowerCAmelCase   )
				SCREAMING_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(
				    lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	additional_replacements=[meta_path]						,	config=lowerCAmelCase   )

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

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

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

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

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

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

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

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

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

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

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

				if len(lowerCAmelCase   ) > 1:
						SCREAMING_SNAKE_CASE_							=       [key for key in output_blocks[i] if F'''output_blocks.{i}.0''' in key]
						SCREAMING_SNAKE_CASE_							=       [key for key in output_blocks[i] if F'''output_blocks.{i}.1''' in key]
						SCREAMING_SNAKE_CASE_							=       [key for key in output_blocks[i] if F'''output_blocks.{i}.2''' in key]

						SCREAMING_SNAKE_CASE_							=       renew_resnet_paths(lowerCAmelCase   )
						SCREAMING_SNAKE_CASE_							=       renew_resnet_paths(lowerCAmelCase   )

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

						SCREAMING_SNAKE_CASE_							=       [key for key in resnets if 'temopral_conv' in key]
						SCREAMING_SNAKE_CASE_							=       renew_temp_conv_paths(lowerCAmelCase   )
						SCREAMING_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(
						    lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	additional_replacements=[meta_path]						,	config=lowerCAmelCase   )

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

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

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

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

						SCREAMING_SNAKE_CASE_							=       [l for l in output_block_layers if 'temopral_conv' in l]
						for path in temopral_conv_paths:
								SCREAMING_SNAKE_CASE_							=       path.split('temopral_conv.'   )[-1]
								SCREAMING_SNAKE_CASE_							=       '.'.join(['output_blocks', str(lowerCAmelCase   ), str(lowerCAmelCase   ), 'temopral_conv', pruned_path]   )
								SCREAMING_SNAKE_CASE_							=       '.'.join(['up_blocks', str(lowerCAmelCase   ), 'temp_convs', str(lowerCAmelCase   ), pruned_path]   )
								SCREAMING_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:    List[str]											=   parser.parse_args()

						SCREAMING_SNAKE_CASE:    Optional[int]											=   torch.load(args.checkpoint_path, map_location='''cpu''')
						SCREAMING_SNAKE_CASE:    List[str]											=   UNetaDConditionModel()

						SCREAMING_SNAKE_CASE:    int											=   convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

						SCREAMING_SNAKE_CASE:    int											=   set(unet.state_dict().keys()) - set(converted_ckpt.keys())
						SCREAMING_SNAKE_CASE:    List[Any]											=   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 --





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       0, len(lowerCAmelCase   ) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
						print(double_linear_search(list(range(1_0_0)), 4_0))