"""simple docstring"""


import argparse

import torch

from diffusers import UNetaDConditionModel





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=None    )							-> Optional[Any]:




				'''simple docstring'''



				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():
												lowerCamelCase__ =old_checkpoint[path]
												lowerCamelCase__ =old_tensor.shape[0] // 3

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

												lowerCamelCase__ =old_tensor.shape[0] // config["num_head_channels"] // 3

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

												lowerCamelCase__ =query.reshape(snake_case__    )
												lowerCamelCase__ =key.reshape(snake_case__    )
												lowerCamelCase__ =value.reshape(snake_case__    )

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

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





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=0    )							-> Optional[Any]:




				'''simple docstring'''



				lowerCamelCase__ =[]
				for old_item in old_list:
								lowerCamelCase__ =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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=1    )							-> List[str]:




				'''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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=0    )							-> Dict:




				'''simple docstring'''



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

				return mapping





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase=0    )							-> Union[str, Any]:




				'''simple docstring'''



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

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

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

								lowerCamelCase__ =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       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase=None       ,   __lowerCAmelCase=False    )							-> Tuple:




				'''simple docstring'''



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

				lowerCamelCase__ ="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"    ):
																lowerCamelCase__ ="model_ema." + "".join(key.split("."    )[1:]    )
																lowerCamelCase__ =checkpoint.pop(snake_case__    )
				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:
												lowerCamelCase__ =checkpoint.pop(snake_case__    )

				lowerCamelCase__ ={}

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

				lowerCamelCase__ =unet_state_dict["input_blocks.0.0.weight"]
				lowerCamelCase__ =unet_state_dict["input_blocks.0.0.bias"]

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

				lowerCamelCase__ =unet_state_dict["out.0.weight"]
				lowerCamelCase__ =unet_state_dict["out.0.bias"]
				lowerCamelCase__ =unet_state_dict["out.2.weight"]
				lowerCamelCase__ =unet_state_dict["out.2.bias"]

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

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

								lowerCamelCase__ =renew_resnet_paths(snake_case__    )
								lowerCamelCase__ ={"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__    )

								lowerCamelCase__ =[key for key in resnets if "temopral_conv" in key]
								lowerCamelCase__ =renew_temp_conv_paths(snake_case__    )
								lowerCamelCase__ ={
								    "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__    ):
												lowerCamelCase__ =renew_attention_paths(snake_case__    )
												lowerCamelCase__ ={"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__    ):
												lowerCamelCase__ =renew_attention_paths(snake_case__    )
												lowerCamelCase__ ={
												    "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__    )

				lowerCamelCase__ =middle_blocks[0]
				lowerCamelCase__ =[key for key in resnet_a if "temopral_conv" in key]
				lowerCamelCase__ =middle_blocks[1]
				lowerCamelCase__ =middle_blocks[2]
				lowerCamelCase__ =middle_blocks[3]
				lowerCamelCase__ =[key for key in resnet_a if "temopral_conv" in key]

				lowerCamelCase__ =renew_resnet_paths(snake_case__    )
				lowerCamelCase__ ={"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]    )

				lowerCamelCase__ =renew_temp_conv_paths(snake_case__    )
				lowerCamelCase__ ={"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]    )

				lowerCamelCase__ =renew_resnet_paths(snake_case__    )
				lowerCamelCase__ ={"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]    )

				lowerCamelCase__ =renew_temp_conv_paths(snake_case__    )
				lowerCamelCase__ ={"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]    )

				lowerCamelCase__ =renew_attention_paths(snake_case__    )
				lowerCamelCase__ ={"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__    )

				lowerCamelCase__ =renew_attention_paths(snake_case__    )
				lowerCamelCase__ ={"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__    ):
								lowerCamelCase__ =i // (config["layers_per_block"] + 1)
								lowerCamelCase__ =i % (config["layers_per_block"] + 1)
								lowerCamelCase__ =[shave_segments(snake_case__       ,   2    ) for name in output_blocks[i]]
								lowerCamelCase__ ={}

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

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

												lowerCamelCase__ =renew_resnet_paths(snake_case__    )
												lowerCamelCase__ =renew_resnet_paths(snake_case__    )

												lowerCamelCase__ ={"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__    )

												lowerCamelCase__ =[key for key in resnets if "temopral_conv" in key]
												lowerCamelCase__ =renew_temp_conv_paths(snake_case__    )
												lowerCamelCase__ ={
												    "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__    )

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

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

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

												lowerCamelCase__ =[l for l in output_block_layers if "temopral_conv" in l]
												for path in temopral_conv_paths:
																lowerCamelCase__ =path.split("temopral_conv."    )[-1]
																lowerCamelCase__ =".".join(["output_blocks", str(snake_case__    ), str(snake_case__    ), "temopral_conv", pruned_path]    )
																lowerCamelCase__ =".".join(["up_blocks", str(snake_case__    ), "temp_convs", str(snake_case__    ), pruned_path]    )
																lowerCamelCase__ =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 --



"""simple docstring"""


from __future__ import annotations

from collections.abc import Generator





def       lowerCamelCase_			( __lowerCAmelCase    )							-> Generator[int, None, None]:




						'''simple docstring'''



						if not isinstance(__lowerCAmelCase       ,   __lowerCAmelCase    ) or n < 1:
												raise Exception("Sequence only defined for positive integers"    )

						yield n
						while n != 1:
												if n % 2 == 0:
																		n //= 2
												else:
																		lowerCamelCase__ =3 * n + 1
												yield n





def       lowerCamelCase_			( )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =43
						lowerCamelCase__ =tuple(collatz_sequence(__lowerCAmelCase    )    )
						print(__lowerCAmelCase    )
						print(F'''Collatz sequence from {n} took {len(__lowerCAmelCase    )} steps.'''    )


if __name__ == "__main__":
	main()


