import argparse

import torch

from diffusers import UNetaDConditionModel
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__     ,					A__=None     ,					A__=None     ,					A__=None       )  ->   Any:
				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__   :      List[str]   				= old_checkpoint[path]
												snake_case__   :      Dict   				= old_tensor.shape[0] // 3

												snake_case__   :      Any   				= (-1, channels) if len(old_tensor.shape       ) == 3 else (-1)

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

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

												snake_case__   :      Optional[Any]   				= query.reshape(_lowercase       )
												snake_case__   :      Tuple   				= key.reshape(_lowercase       )
												snake_case__   :      Union[str, Any]   				= value.reshape(_lowercase       )

				for path in paths:
								snake_case__   :      str   				= path['new']

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

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

        # proj_attn.weight has to be converted from conv 1D to linear
								snake_case__   :      Optional[int]   				= old_checkpoint[path['old']]
								snake_case__   :      Tuple   				= ['proj_attn.weight']
								snake_case__   :      Optional[int]   				= ['proj_out.weight', 'proj_in.weight']
								if any(k in new_path for k in names       ):
												snake_case__   :      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:
												snake_case__   :      Optional[Any]   				= weight[:, :, 0]
								else:
												snake_case__   :      int   				= weight
def 					UpperCamelCase__		(							A__     ,					A__=0       )  ->   int:
				snake_case__   :      Any   				= []
				for old_item in old_list:
								snake_case__   :      Optional[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 					UpperCamelCase__		(							A__     ,					A__=1       )  ->   str:
				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__		(							A__     ,					A__=0       )  ->   Any:
				snake_case__   :      List[Any]   				= []
				for old_item in old_list:
								mapping.append({'old': old_item, 'new': old_item}       )

				return mapping
def 					UpperCamelCase__		(							A__     ,					A__=0       )  ->   Tuple:
				snake_case__   :      List[str]   				= []
				for old_item in old_list:
								snake_case__   :      Optional[int]   				= old_item.replace('in_layers.0'     ,					'norm1'       )
								snake_case__   :      Tuple   				= new_item.replace('in_layers.2'     ,					'conv1'       )

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

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

								snake_case__   :      Tuple   				= 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__		(							A__     ,					A__     ,					A__=None     ,					A__=False       )  ->   List[str]:
				snake_case__   :      Tuple   				= {}
				snake_case__   :      List[str]   				= list(checkpoint.keys()       )

				snake_case__   :      str   				= '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'       ):
																snake_case__   :      Dict   				= 'model_ema.' + ''.join(key.split('.'       )[1:]       )
																snake_case__   :      List[str]   				= checkpoint.pop(_lowercase       )
				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:
												snake_case__   :      List[Any]   				= checkpoint.pop(_lowercase       )

				snake_case__   :      int   				= {}

				snake_case__   :      Optional[int]   				= unet_state_dict['time_embed.0.weight']
				snake_case__   :      List[str]   				= unet_state_dict['time_embed.0.bias']
				snake_case__   :      Optional[int]   				= unet_state_dict['time_embed.2.weight']
				snake_case__   :      str   				= 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__   :      Optional[Any]   				= unet_state_dict['label_emb.0.0.weight']
								snake_case__   :      List[Any]   				= unet_state_dict['label_emb.0.0.bias']
								snake_case__   :      Optional[int]   				= unet_state_dict['label_emb.0.2.weight']
								snake_case__   :      int   				= unet_state_dict['label_emb.0.2.bias']
				else:
								raise NotImplementedError(F"""Not implemented `class_embed_type`: {config['class_embed_type']}"""       )

				snake_case__   :      Optional[int]   				= unet_state_dict['input_blocks.0.0.weight']
				snake_case__   :      Tuple   				= unet_state_dict['input_blocks.0.0.bias']

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

				snake_case__   :      Optional[int]   				= unet_state_dict['out.0.weight']
				snake_case__   :      Tuple   				= unet_state_dict['out.0.bias']
				snake_case__   :      int   				= unet_state_dict['out.2.weight']
				snake_case__   :      str   				= unet_state_dict['out.2.bias']

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

								snake_case__   :      str   				= [
								    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__   :      Optional[int]   				= [key for key in input_blocks[i] if F"""input_blocks.{i}.1""" in key]
								snake_case__   :      Optional[int]   				= [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__   :      int   				= unet_state_dict.pop(
												    F"""input_blocks.{i}.op.weight"""       )
												snake_case__   :      Union[str, Any]   				= unet_state_dict.pop(
												    F"""input_blocks.{i}.op.bias"""       )

								snake_case__   :      Optional[int]   				= renew_resnet_paths(_lowercase       )
								snake_case__   :      int   				= {'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__   :      str   				= [key for key in resnets if 'temopral_conv' in key]
								snake_case__   :      List[Any]   				= renew_temp_conv_paths(_lowercase       )
								snake_case__   :      Union[str, Any]   				= {
								    '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__   :      Any   				= renew_attention_paths(_lowercase       )
												snake_case__   :      int   				= {'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__   :      Tuple   				= renew_attention_paths(_lowercase       )
												snake_case__   :      str   				= {
												    '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__   :      str   				= middle_blocks[0]
				snake_case__   :      List[str]   				= [key for key in resnet_a if 'temopral_conv' in key]
				snake_case__   :      str   				= middle_blocks[1]
				snake_case__   :      List[str]   				= middle_blocks[2]
				snake_case__   :      Dict   				= middle_blocks[3]
				snake_case__   :      str   				= [key for key in resnet_a if 'temopral_conv' in key]

				snake_case__   :      Union[str, Any]   				= renew_resnet_paths(_lowercase       )
				snake_case__   :      Tuple   				= {'old': 'middle_block.0', 'new': 'mid_block.resnets.0'}
				assign_to_checkpoint(
				    _lowercase     ,					_lowercase     ,					_lowercase     ,					config=_lowercase     ,					additional_replacements=[meta_path]       )

				snake_case__   :      Optional[Any]   				= renew_temp_conv_paths(_lowercase       )
				snake_case__   :      str   				= {'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__   :      Union[str, Any]   				= renew_resnet_paths(_lowercase       )
				snake_case__   :      str   				= {'old': 'middle_block.3', 'new': 'mid_block.resnets.1'}
				assign_to_checkpoint(
				    _lowercase     ,					_lowercase     ,					_lowercase     ,					config=_lowercase     ,					additional_replacements=[meta_path]       )

				snake_case__   :      Any   				= renew_temp_conv_paths(_lowercase       )
				snake_case__   :      Union[str, Any]   				= {'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__   :      List[Any]   				= renew_attention_paths(_lowercase       )
				snake_case__   :      Optional[int]   				= {'old': 'middle_block.1', 'new': 'mid_block.attentions.0'}
				assign_to_checkpoint(
				    _lowercase     ,					_lowercase     ,					_lowercase     ,					additional_replacements=[meta_path]     ,					config=_lowercase       )

				snake_case__   :      Union[str, Any]   				= renew_attention_paths(_lowercase       )
				snake_case__   :      Union[str, Any]   				= {'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__   :      Union[str, Any]   				= i // (config['layers_per_block'] + 1)
								snake_case__   :      Dict   				= i % (config['layers_per_block'] + 1)
								snake_case__   :      Any   				= [shave_segments(_lowercase     ,					2       ) for name in output_blocks[i]]
								snake_case__   :      List[Any]   				= {}

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

								if len(_lowercase       ) > 1:
												snake_case__   :      int   				= [key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
												snake_case__   :      Union[str, Any]   				= [key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
												snake_case__   :      Optional[int]   				= [key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

												snake_case__   :      Union[str, Any]   				= renew_resnet_paths(_lowercase       )
												snake_case__   :      List[str]   				= renew_resnet_paths(_lowercase       )

												snake_case__   :      Union[str, Any]   				= {'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__   :      Optional[Any]   				= [key for key in resnets if 'temopral_conv' in key]
												snake_case__   :      Dict   				= renew_temp_conv_paths(_lowercase       )
												snake_case__   :      Union[str, Any]   				= {
												    '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__   :      str   				= {k: sorted(_lowercase       ) for k, v in output_block_list.items()}
												if ["conv.bias", "conv.weight"] in output_block_list.values():
																snake_case__   :      Optional[int]   				= list(output_block_list.values()       ).index(['conv.bias', 'conv.weight']       )
																snake_case__   :      Optional[Any]   				= unet_state_dict[
																    F"""output_blocks.{i}.{index}.conv.weight"""
																]
																snake_case__   :      Any   				= unet_state_dict[
																    F"""output_blocks.{i}.{index}.conv.bias"""
																]

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

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

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

				return new_checkpoint


if __name__ == "__main__":
				lowerCAmelCase__	:  List[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.''')
				lowerCAmelCase__	:  Union[str, Any]						=  parser.parse_args()

				lowerCAmelCase__	:  Dict						=  torch.load(args.checkpoint_path, map_location='''cpu''')
				lowerCAmelCase__	:  Optional[Any]						=  UNetaDConditionModel()

				lowerCAmelCase__	:  Tuple						=  convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				lowerCAmelCase__	:  Tuple						=  set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				lowerCAmelCase__	:  List[str]						=  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 --









import itertools
from pathlib import Path

def      a    (   _lowercase :  list[str]							)  ->int:
		UpperCamelCase__								=						[tuple(_lowercase							) for login in logins]

		UpperCamelCase__								=						{char for login in split_logins for char in login}

		for permutation in itertools.permutations(_lowercase							):
				UpperCamelCase__								=						True
				for login in logins:
						if not (
						    permutation.index(login[0]							)
						    < permutation.index(login[1]							)
						    < permutation.index(login[2]							)
						):
								UpperCamelCase__								=						False
								break

				if satisfied:
						return int("""""".join(_lowercase							)							)

		raise Exception("""Unable to find the secret passcode"""							)

def      a    (   _lowercase :  str = "keylog.txt"							)  ->int:
		UpperCamelCase__								=						Path(_lowercase							).parent.joinpath(_lowercase							).read_text().splitlines()

		return find_secret_passcode(_lowercase							)


if __name__ == "__main__":
				print(f"""{solution() = }""")





