


import argparse

import torch

from diffusers import UNetaDConditionModel
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:List[Any]   ,    lowercase__	:int   ,    lowercase__	:Optional[Any]=None   ,    lowercase__	:int=None   ,    lowercase__	:List[str]=None					)							->	List[str]:
						assert isinstance(snake_case_   ,    snake_case_					), "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():
																		__magic_name__					:				str				=   old_checkpoint[path]
																		__magic_name__					:				Optional[Any]				=   old_tensor.shape[0] // 3

																		__magic_name__					:				int				=   (-1, channels) if len(old_tensor.shape					) == 3 else (-1)

																		__magic_name__					:				List[str]				=   old_tensor.shape[0] // config['num_head_channels'] // 3

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

																		__magic_name__					:				List[Any]				=   query.reshape(snake_case_					)
																		__magic_name__					:				str				=   key.reshape(snake_case_					)
																		__magic_name__					:				Union[str, Any]				=   value.reshape(snake_case_					)

						for path in paths:
												__magic_name__					:				Dict				=   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:
																								__magic_name__					:				List[str]				=   new_path.replace(replacement['old']   ,    replacement['new']					)

																								    # proj_attn.weight has to be converted from conv 1D to linear
												__magic_name__					:				Union[str, Any]				=   old_checkpoint[path['old']]
												__magic_name__					:				Optional[int]				=   ['proj_attn.weight']
												__magic_name__					:				int				=   ['proj_out.weight', 'proj_in.weight']
												if any(k in new_path for k in names					):
																		__magic_name__					:				int				=   weight[:, :, 0]
												elif any(k in new_path for k in names_a					) and len(weight.shape					) > 2 and ".attentions." not in new_path:
																		__magic_name__					:				Union[str, Any]				=   weight[:, :, 0]
												else:
																		__magic_name__					:				Any				=   weight
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Any   ,    lowercase__	:Optional[int]=0					)							->	Optional[Any]:
						__magic_name__					:				Tuple				=   []
						for old_item in old_list:
												__magic_name__					:				int				=   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 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:List[str]=1					)							->	Optional[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 			__SCREAMING_SNAKE_CASE							(					lowercase__	:Union[str, Any]   ,    lowercase__	:str=0					)							->	Optional[int]:
						__magic_name__					:				List[str]				=   []
						for old_item in old_list:
												mapping.append({'old': old_item, 'new': old_item}					)

						return mapping
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:List[str]   ,    lowercase__	:Optional[Any]=0					)							->	Union[str, Any]:
						__magic_name__					:				Dict				=   []
						for old_item in old_list:
												__magic_name__					:				List[Any]				=   old_item.replace('in_layers.0'   ,    'norm1'					)
												__magic_name__					:				Dict				=   new_item.replace('in_layers.2'   ,    'conv1'					)

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

												__magic_name__					:				Dict				=   new_item.replace('emb_layers.1'   ,    'time_emb_proj'					)
												__magic_name__					:				Dict				=   new_item.replace('skip_connection'   ,    'conv_shortcut'					)

												__magic_name__					:				Dict				=   shave_segments(snake_case_   ,    n_shave_prefix_segments=snake_case_					)

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

						return mapping
def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str   ,    lowercase__	:Optional[Any]   ,    lowercase__	:Any=None   ,    lowercase__	:Optional[int]=False					)							->	Optional[int]:
						__magic_name__					:				Optional[Any]				=   {}
						__magic_name__					:				Dict				=   list(checkpoint.keys()					)

						__magic_name__					:				Tuple				=   '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'					):
																								__magic_name__					:				Union[str, Any]				=   'model_ema.' + ''.join(key.split('.'					)[1:]					)
																								__magic_name__					:				Dict				=   checkpoint.pop(snake_case_					)
						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:
																		__magic_name__					:				Optional[Any]				=   checkpoint.pop(snake_case_					)

						__magic_name__					:				Union[str, Any]				=   {}

						__magic_name__					:				Any				=   unet_state_dict['time_embed.0.weight']
						__magic_name__					:				Tuple				=   unet_state_dict['time_embed.0.bias']
						__magic_name__					:				str				=   unet_state_dict['time_embed.2.weight']
						__magic_name__					:				Union[str, Any]				=   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":
												__magic_name__					:				str				=   unet_state_dict['label_emb.0.0.weight']
												__magic_name__					:				Dict				=   unet_state_dict['label_emb.0.0.bias']
												__magic_name__					:				Dict				=   unet_state_dict['label_emb.0.2.weight']
												__magic_name__					:				Dict				=   unet_state_dict['label_emb.0.2.bias']
						else:
												raise NotImplementedError(f"Not implemented `class_embed_type`: {config['class_embed_type']}"					)

						__magic_name__					:				Any				=   unet_state_dict['input_blocks.0.0.weight']
						__magic_name__					:				List[str]				=   unet_state_dict['input_blocks.0.0.bias']

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

						__magic_name__					:				str				=   unet_state_dict['out.0.weight']
						__magic_name__					:				str				=   unet_state_dict['out.0.bias']
						__magic_name__					:				Union[str, Any]				=   unet_state_dict['out.2.weight']
						__magic_name__					:				Tuple				=   unet_state_dict['out.2.bias']

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

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

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

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

												__magic_name__					:				Tuple				=   [
												    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
												]
												__magic_name__					:				List[Any]				=   [key for key in input_blocks[i] if f"input_blocks.{i}.1" in key]
												__magic_name__					:				Dict				=   [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:
																		__magic_name__					:				Any				=   unet_state_dict.pop(
																		    f"input_blocks.{i}.op.weight"					)
																		__magic_name__					:				Optional[Any]				=   unet_state_dict.pop(
																		    f"input_blocks.{i}.op.bias"					)

												__magic_name__					:				Tuple				=   renew_resnet_paths(snake_case_					)
												__magic_name__					:				Union[str, Any]				=   {'old': f"input_blocks.{i}.0", 'new': f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
												assign_to_checkpoint(
												    snake_case_   ,    snake_case_   ,    snake_case_   ,    additional_replacements=[meta_path]   ,    config=snake_case_					)

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

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

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

						__magic_name__					:				str				=   middle_blocks[0]
						__magic_name__					:				Union[str, Any]				=   [key for key in resnet_a if 'temopral_conv' in key]
						__magic_name__					:				Any				=   middle_blocks[1]
						__magic_name__					:				Optional[int]				=   middle_blocks[2]
						__magic_name__					:				List[Any]				=   middle_blocks[3]
						__magic_name__					:				List[Any]				=   [key for key in resnet_a if 'temopral_conv' in key]

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

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

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

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

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

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

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

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

												if len(snake_case_					) > 1:
																		__magic_name__					:				List[str]				=   [key for key in output_blocks[i] if f"output_blocks.{i}.0" in key]
																		__magic_name__					:				List[str]				=   [key for key in output_blocks[i] if f"output_blocks.{i}.1" in key]
																		__magic_name__					:				Any				=   [key for key in output_blocks[i] if f"output_blocks.{i}.2" in key]

																		__magic_name__					:				Optional[int]				=   renew_resnet_paths(snake_case_					)
																		__magic_name__					:				int				=   renew_resnet_paths(snake_case_					)

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

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

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

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

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

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

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

						return new_checkpoint


if __name__ == "__main__":
			snake_case__  				=			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.')
			snake_case__  				=			parser.parse_args()

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

			snake_case__  				=			convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

			snake_case__  				=			set(unet.state_dict().keys()) - set(converted_ckpt.keys())
			snake_case__  				=			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 random
import sys

__UpperCAmelCase												=   'ABCDEFGHIJKLMNOPQRSTUVWXYZ'



def        SCREAMING_SNAKE_CASE_  (	)       ->	None:
 SCREAMING_SNAKE_CASE  :  Dict								=		input('Enter message: '							)
 SCREAMING_SNAKE_CASE  :  Dict								=		'LFWOAYUISVKMNXPBDCRJTQEGHZ'
 SCREAMING_SNAKE_CASE  :  Tuple								=		input('Encrypt/Decrypt [e/d]: '							)

 check_valid_key(snake_case_							)

 if resp.lower().startswith('e'							):
  SCREAMING_SNAKE_CASE  :  str								=		'encrypt'
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		encrypt_message(snake_case_   ,     snake_case_							)
 elif resp.lower().startswith('d'							):
  SCREAMING_SNAKE_CASE  :  List[Any]								=		'decrypt'
  SCREAMING_SNAKE_CASE  :  List[str]								=		decrypt_message(snake_case_   ,     snake_case_							)

 print(f"""\n{mode.title()}ion: \n{translated}"""							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str							)       ->	None:
 SCREAMING_SNAKE_CASE  :  int								=		list(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		list(snake_case_							)
 key_list.sort()
 letters_list.sort()

 if key_list != letters_list:
  sys.exit('Error in the key or symbol set.'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'encrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 return translate_message(snake_case_   ,     snake_case_   ,     'decrypt'							)



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	str   ,     snake_case_				:	str							)       ->	str:
 SCREAMING_SNAKE_CASE  :  Any								=		''
 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		LETTERS
 SCREAMING_SNAKE_CASE  :  List[Any]								=		key

 if mode == "decrypt":
  SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[Any]								=		chars_b, chars_a

 for symbol in message:
  if symbol.upper() in chars_a:
   SCREAMING_SNAKE_CASE  :  Optional[Any]								=		chars_a.find(symbol.upper()							)
   if symbol.isupper():
    translated += chars_b[sym_index].upper()
   else:
    translated += chars_b[sym_index].lower()
  else:
   translated += symbol

 return translated



def        SCREAMING_SNAKE_CASE_  (	)       ->	str:
 SCREAMING_SNAKE_CASE  :  Dict								=		list(snake_case_							)
 random.shuffle(snake_case_							)
 return "".join(snake_case_							)


if __name__ == "__main__":
   main()


