




import argparse

import torch

from diffusers import UNetaDConditionModel



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       int    ,       lowercase__					:       Optional[Any]    ,       lowercase__					:       str=None    ,       lowercase__					:       List[str]=None    ,       lowercase__					:       Optional[int]=None				):
		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():
						snake_case						:  Optional[Any]        =			old_checkpoint[path]
						snake_case						:  Tuple        =			old_tensor.shape[0] // 3

						snake_case						:  Union[str, Any]        =			(-1, channels) if len(old_tensor.shape				) == 3 else (-1)

						snake_case						:  Optional[Any]        =			old_tensor.shape[0] // config["num_head_channels"] // 3

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

						snake_case						:  Optional[Any]        =			query.reshape(snake_case__				)
						snake_case						:  Dict        =			key.reshape(snake_case__				)
						snake_case						:  Optional[int]        =			value.reshape(snake_case__				)

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

        # proj_attn.weight has to be converted from conv 1D to linear
				snake_case						:  Any        =			old_checkpoint[path["old"]]
				snake_case						:  Any        =			["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						:  List[Any]        =			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						:  Union[str, Any]        =			weight[:, :, 0]
				else:
						snake_case						:  int        =			weight



def 					UpperCamelCase__       (						lowercase__					:       Dict    ,       lowercase__					:       Union[str, Any]=0				):
		snake_case						:  str        =			[]
		for old_item in old_list:
				snake_case						:  List[str]        =			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__       (						lowercase__					:       Tuple    ,       lowercase__					:       int=1				):
		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__       (						lowercase__					:       Optional[Any]    ,       lowercase__					:       str=0				):
		snake_case						:  Optional[int]        =			[]
		for old_item in old_list:
				mapping.append({"old": old_item, "new": old_item}				)

		return mapping



def 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]    ,       lowercase__					:       Tuple=0				):
		snake_case						:  Optional[Any]        =			[]
		for old_item in old_list:
				snake_case						:  Optional[Any]        =			old_item.replace("in_layers.0"    ,       "norm1"				)
				snake_case						:  Any        =			new_item.replace("in_layers.2"    ,       "conv1"				)

				snake_case						:  List[Any]        =			new_item.replace("out_layers.0"    ,       "norm2"				)
				snake_case						:  Optional[Any]        =			new_item.replace("out_layers.3"    ,       "conv2"				)

				snake_case						:  Union[str, Any]        =			new_item.replace("emb_layers.1"    ,       "time_emb_proj"				)
				snake_case						:  Tuple        =			new_item.replace("skip_connection"    ,       "conv_shortcut"				)

				snake_case						:  str        =			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 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       int    ,       lowercase__					:       Any=None    ,       lowercase__					:       str=False				):
		snake_case						:  Union[str, Any]        =			{}
		snake_case						:  int        =			list(checkpoint.keys()				)

		snake_case						:  Optional[Any]        =			"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						:  Any        =			"model_ema." + "".join(key.split("."				)[1:]				)
								snake_case						:  str        =			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:
						snake_case						:  str        =			checkpoint.pop(snake_case__				)

		snake_case						:  Dict        =			{}

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

		snake_case						:  Tuple        =			unet_state_dict["input_blocks.0.0.weight"]
		snake_case						:  Union[str, Any]        =			unet_state_dict["input_blocks.0.0.bias"]

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

		snake_case						:  str        =			unet_state_dict["out.0.weight"]
		snake_case						:  str        =			unet_state_dict["out.0.bias"]
		snake_case						:  Union[str, Any]        =			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						:  Dict        =			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(snake_case__				)
		}

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

				snake_case						:  int        =			[
				    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						:  List[Any]        =			[key for key in input_blocks[i] if F'''input_blocks.{i}.1''' in key]
				snake_case						:  List[Any]        =			[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						:  List[str]        =			unet_state_dict.pop(
						    F'''input_blocks.{i}.op.weight'''				)
						snake_case						:  Optional[Any]        =			unet_state_dict.pop(
						    F'''input_blocks.{i}.op.bias'''				)

				snake_case						:  Optional[int]        =			renew_resnet_paths(snake_case__				)
				snake_case						:  str        =			{"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__				)

				snake_case						:  Any        =			[key for key in resnets if "temopral_conv" in key]
				snake_case						:  List[str]        =			renew_temp_conv_paths(snake_case__				)
				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(
				    snake_case__    ,       snake_case__    ,       snake_case__    ,       additional_replacements=[meta_path]    ,       config=snake_case__				)

				if len(snake_case__				):
						snake_case						:  List[Any]        =			renew_attention_paths(snake_case__				)
						snake_case						:  Any        =			{"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__				):
						snake_case						:  int        =			renew_attention_paths(snake_case__				)
						snake_case						:  List[Any]        =			{
						    "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__				)

		snake_case						:  List[str]        =			middle_blocks[0]
		snake_case						:  Optional[Any]        =			[key for key in resnet_a if "temopral_conv" in key]
		snake_case						:  Any        =			middle_blocks[1]
		snake_case						:  str        =			middle_blocks[2]
		snake_case						:  str        =			middle_blocks[3]
		snake_case						:  List[str]        =			[key for key in resnet_a if "temopral_conv" in key]

		snake_case						:  Any        =			renew_resnet_paths(snake_case__				)
		snake_case						:  Optional[Any]        =			{"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]				)

		snake_case						:  int        =			renew_temp_conv_paths(snake_case__				)
		snake_case						:  Optional[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]				)

		snake_case						:  Optional[int]        =			renew_resnet_paths(snake_case__				)
		snake_case						:  List[Any]        =			{"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]				)

		snake_case						:  str        =			renew_temp_conv_paths(snake_case__				)
		snake_case						:  Tuple        =			{"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]				)

		snake_case						:  List[Any]        =			renew_attention_paths(snake_case__				)
		snake_case						:  int        =			{"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__				)

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

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

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

						snake_case						:  Tuple        =			renew_resnet_paths(snake_case__				)
						snake_case						:  Tuple        =			renew_resnet_paths(snake_case__				)

						snake_case						:  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__				)

						snake_case						:  Optional[int]        =			[key for key in resnets if "temopral_conv" in key]
						snake_case						:  Dict        =			renew_temp_conv_paths(snake_case__				)
						snake_case						:  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__				)

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

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

						snake_case						:  int        =			[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						:  Dict        =			".".join(["output_blocks", str(snake_case__				), str(snake_case__				), "temopral_conv", pruned_path]				)
								snake_case						:  Optional[int]        =			".".join(["up_blocks", str(snake_case__				), "temp_convs", str(snake_case__				), pruned_path]				)
								snake_case						:  Optional[int]        =			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"""

import matplotlib.pyplot as plt
import numpy as np



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Any        =			len(lowercase__				)  # Number of training samples
		snake_case						:  str        =			np.eye(lowercase__				)  # Initialize weights as identity matrix
		for j in range(lowercase__				):
				snake_case						:  Any        =			point - x_train[j]
				snake_case						:  Dict        =			np.exp(diff @ diff.T / (-2.0 * tau**2)				)

		return weights



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  str        =			weight_matrix(lowercase__    ,       lowercase__    ,       lowercase__				)
		snake_case						:  str        =			np.linalg.inv(x_train.T @ weight_mat @ x_train				) @ (
		    x_train.T @ weight_mat @ y_train.T
		)

		return weight



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       float				):
		snake_case						:  Optional[int]        =			np.zeros(len(lowercase__				)				)  # Initialize array of predictions
		for i, item in enumerate(lowercase__				):
				snake_case						:  Dict        =			item @ local_weight(lowercase__    ,       lowercase__    ,       lowercase__    ,       lowercase__				)

		return y_pred



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       str    ,       lowercase__					:       str				):
		import seaborn as sns

		snake_case						:  Union[str, Any]        =			sns.load_dataset(lowercase__				)
		snake_case						:  Any        =			np.array(data[x_name]				)
		snake_case						:  str        =			np.array(data[y_name]				)

		snake_case						:  Any        =			np.ones(len(lowercase__				)				)

		# pairing elements of one and x_data
		snake_case						:  Optional[int]        =			np.column_stack((one, x_data)				)

		return x_train, x_data, y_data



def 					UpperCamelCase__       (						lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       np.ndarray    ,       lowercase__					:       str    ,       lowercase__					:       str    ,       ):
		snake_case						:  List[str]        =			np.sort(lowercase__    ,       axis=0				)
		plt.scatter(lowercase__    ,       lowercase__    ,       color="blue"				)
		plt.plot(
		    x_train_sorted[:, 1]    ,       preds[x_train[:, 1].argsort(0				)]    ,       color="yellow"    ,       linewidth=5    ,       )
		plt.title("Local Weighted Regression"				)
		plt.xlabel(lowercase__				)
		plt.ylabel(lowercase__				)
		plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Demo with a dataset from the seaborn module
							__A						,    __A						,    __A							=     load_data("tips", "total_bill", "tip")
							__A							=     local_weight_regression(training_data_x, tip, 5)
							plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")

