from typing import Any

import numpy as np





class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    None:



									'''simple docstring'''







									if np.any(tableau[:, -1]       ,			where=tableau[:, -1] < 0						):
													raise ValueError('RHS must be > 0'						)

									snake_case__   :      Tuple   				= tableau
									snake_case__				,    snake_case__   :      Union[str, Any]   				= tableau.shape

									# Number of decision variables x1, x2, x3...
									snake_case__   :      Union[str, Any]   				= n_vars

									# Number of artificial variables to be minimised
									snake_case__   :      Tuple   				= len(np.where(tableau[self.n_vars : -1] == -1						)[0]						)

									# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
									snake_case__   :      Optional[int]   				= (self.n_art_vars > 0) + 1

									# Number of slack variables added to make inequalities into equalities
									snake_case__   :      Tuple   				= self.n_rows - self.n_stages

									# Objectives for each stage
									snake_case__   :      List[Any]   				= ['max']

									# In two stage simplex, first minimise then maximise
									if self.n_art_vars:
													self.objectives.append('min'						)

									snake_case__   :      Optional[Any]   				= ['']

									# Index of current pivot row and column
									snake_case__   :      Union[str, Any]   				= None
									snake_case__   :      Union[str, Any]   				= None

									# Does objective row only contain (non)-negative values?
									snake_case__   :      Union[str, Any]   				= False




					@staticmethod
					def      __a     (  *__UpperCamelCase						)	->    list[str]:



									'''simple docstring'''







									if len(__lowerCAmelCase						) != 3:
													raise ValueError('Must provide n_vars, n_slack, and n_art_vars'						)

									if not all(x >= 0 and isinstance(__lowerCAmelCase       ,			__lowerCAmelCase						) for x in args						):
													raise ValueError('All arguments must be non-negative integers'						)

									# decision | slack | artificial
									snake_case__   :      Any   				= ['x', 's', 'a']
									snake_case__   :      str   				= []
									for i in range(3						):
													for j in range(args[i]						):
																	titles.append(string_starts[i] + str(j + 1						)						)
									titles.append('RHS'						)
									return titles




					def      __a     (  self       ,			__UpperCamelCase						)	->    tuple[Any, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= self.objectives[-1]

									# Find entries of highest magnitude in objective rows
									snake_case__   :      Optional[Any]   				= (objective == 'min') - (objective == 'max')
									snake_case__   :      Optional[int]   				= np.argmax(sign * tableau[0, : self.n_vars]						)

									# Choice is only valid if below 0 for maximise, and above for minimise
									if sign * self.tableau[0, col_idx] <= 0:
													snake_case__   :      Dict   				= True
													return 0, 0

									# Pivot row is chosen as having the lowest quotient when elements of
									# the pivot column divide the right-hand side

									# Slice excluding the objective rows
									snake_case__   :      int   				= slice(self.n_stages       ,			self.n_rows						)

									# RHS
									snake_case__   :      Optional[Any]   				= tableau[s, -1]

									# Elements of pivot column within slice
									snake_case__   :      str   				= tableau[s, col_idx]

									# Array filled with nans
									snake_case__   :      Optional[int]   				= np.full(self.n_rows - self.n_stages       ,			np.nan						)

									# If element in pivot column is greater than zeron_stages, return
									# quotient or nan otherwise
									snake_case__   :      List[str]   				= np.divide(__lowerCAmelCase       ,			__lowerCAmelCase       ,			out=__lowerCAmelCase       ,			where=divisor > 0						)

									# Arg of minimum quotient excluding the nan values. n_stages is added
									# to compensate for earlier exclusion of objective columns
									snake_case__   :      Union[str, Any]   				= np.nanargmin(__lowerCAmelCase						) + self.n_stages
									return row_idx, col_idx




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    np.ndarray:



									'''simple docstring'''







									snake_case__   :      List[Any]   				= tableau[row_idx].copy()

									snake_case__   :      str   				= piv_row[col_idx]

									# Entry becomes 1
									piv_row *= 1 / piv_val

									# Variable in pivot column becomes basic, ie the only non-zero entry
									for idx, coeff in enumerate(tableau[:, col_idx]						):
													tableau[idx] += -coeff * piv_row
									snake_case__   :      Optional[int]   				= piv_row
									return tableau




					def      __a     (  self       ,			__UpperCamelCase						)	->    np.ndarray:



									'''simple docstring'''







									self.objectives.pop()

									if not self.objectives:
													return tableau

									# Slice containing ids for artificial columns
									snake_case__   :      Optional[int]   				= slice(-self.n_art_vars - 1       ,			-1						)

									# Delete the artificial variable columns
									snake_case__   :      Dict   				= np.delete(__lowerCAmelCase       ,			__lowerCAmelCase       ,			axis=1						)

									# Delete the objective row of the first stage
									snake_case__   :      Union[str, Any]   				= np.delete(__lowerCAmelCase       ,			0       ,			axis=0						)

									snake_case__   :      Union[str, Any]   				= 1
									self.n_rows -= 1
									snake_case__   :      str   				= 0
									snake_case__   :      List[Any]   				= False
									return tableau




					def      __a     (  self						)	->    dict[Any, Any]:



									'''simple docstring'''







									for _ in range(100						):
													# Completion of each stage removes an objective. If both stages
													# are complete, then no objectives are left
													if not self.objectives:
																	snake_case__   :      List[Any]   				= self.generate_col_titles(
																	    self.n_vars       ,			self.n_slack       ,			self.n_art_vars						)

																	# Find the values of each variable at optimal solution
																	return self.interpret_tableau(self.tableau       ,			self.col_titles						)

													snake_case__				,    snake_case__   :      List[Any]   				= self.find_pivot(self.tableau						)

													# If there are no more negative values in objective row
													if self.stop_iter:
																	# Delete artificial variable columns and rows. Update attributes
																	snake_case__   :      Optional[Any]   				= self.change_stage(self.tableau						)
													else:
																	snake_case__   :      Tuple   				= self.pivot(self.tableau       ,			__lowerCAmelCase       ,			__lowerCAmelCase						)
									return {}




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    dict[str, float]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= {'P': abs(tableau[0, -1]						)}

									for i in range(self.n_vars						):
													# Gives ids of nonzero entries in the ith column
													snake_case__   :      Union[str, Any]   				= np.nonzero(tableau[:, i]						)
													snake_case__   :      List[Any]   				= len(nonzero[0]						)

													# First entry in the nonzero ids
													snake_case__   :      Union[str, Any]   				= nonzero[0][0]
													snake_case__   :      Union[str, Any]   				= tableau[nonzero_rowidx, i]

													# If there is only one nonzero value in column, which is one
													if n_nonzero == nonzero_val == 1:
																	snake_case__   :      Dict   				= tableau[nonzero_rowidx, -1]
																	snake_case__   :      str   				= rhs_val

        # Check for basic variables
									for title in col_titles:
													# Don't add RHS or slack variables to output dict
													if title[0] not in "R-s-a":
																	output_dict.setdefault(__lowerCAmelCase       ,			0						)
									return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()









import argparse

import torch

from diffusers import UNetaDConditionModel

def      a    (   _lowercase :  Dict			,    _lowercase :  Dict			,    _lowercase :  Any			,    _lowercase :  Tuple=None			,    _lowercase :  List[Any]=None			,    _lowercase :  Optional[Any]=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():
						UpperCamelCase__								=						old_checkpoint[path]
						UpperCamelCase__								=						old_tensor.shape[0] // 3

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

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

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

						UpperCamelCase__								=						query.reshape(_lowercase							)
						UpperCamelCase__								=						key.reshape(_lowercase							)
						UpperCamelCase__								=						value.reshape(_lowercase							)

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

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

def      a    (   _lowercase :  Tuple			,    _lowercase :  List[Any]=0							)  ->int:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						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    (   _lowercase :  Union[str, Any]			,    _lowercase :  int=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      a    (   _lowercase :  List[str]			,    _lowercase :  Any=0							)  ->Any:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				mapping.append({"""old""": old_item, """new""": old_item}							)

		return mapping

def      a    (   _lowercase :  List[str]			,    _lowercase :  int=0							)  ->Tuple:
		UpperCamelCase__								=						[]
		for old_item in old_list:
				UpperCamelCase__								=						old_item.replace("""in_layers.0"""			,    """norm1"""							)
				UpperCamelCase__								=						new_item.replace("""in_layers.2"""			,    """conv1"""							)

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

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

				UpperCamelCase__								=						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      a    (   _lowercase :  List[Any]			,    _lowercase :  List[Any]			,    _lowercase :  int=None			,    _lowercase :  Optional[Any]=False							)  ->List[str]:
		UpperCamelCase__								=						{}
		UpperCamelCase__								=						list(checkpoint.keys()							)

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

		UpperCamelCase__								=						{}

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

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

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

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

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

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

				UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
				UpperCamelCase__								=						{"""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							)

				UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
				UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
				UpperCamelCase__								=						{
				    """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							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{"""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							):
						UpperCamelCase__								=						renew_attention_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """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							)

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

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0""", """new""": """mid_block.resnets.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.0.temopral_conv""", """new""": """mid_block.temp_convs.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3""", """new""": """mid_block.resnets.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.3.temopral_conv""", """new""": """mid_block.temp_convs.1"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    config=_lowercase			,    additional_replacements=[meta_path]							)

		UpperCamelCase__								=						renew_attention_paths(_lowercase							)
		UpperCamelCase__								=						{"""old""": """middle_block.1""", """new""": """mid_block.attentions.0"""}
		assign_to_checkpoint(
		    _lowercase			,    _lowercase			,    _lowercase			,    additional_replacements=[meta_path]			,    config=_lowercase							)

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

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

				if len(_lowercase							) > 1:
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.0""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.1""" in key]
						UpperCamelCase__								=						[key for key in output_blocks[i] if F"""output_blocks.{i}.2""" in key]

						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)
						UpperCamelCase__								=						renew_resnet_paths(_lowercase							)

						UpperCamelCase__								=						{"""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							)

						UpperCamelCase__								=						[key for key in resnets if """temopral_conv""" in key]
						UpperCamelCase__								=						renew_temp_conv_paths(_lowercase							)
						UpperCamelCase__								=						{
						    """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							)

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

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

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

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

		return new_checkpoint


if __name__ == "__main__":
				__lowerCAmelCase 	=      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 	=      parser.parse_args()

				__lowerCAmelCase 	=      torch.load(args.checkpoint_path, map_location="cpu")
				__lowerCAmelCase 	=      UNetaDConditionModel()

				__lowerCAmelCase 	=      convert_ldm_unet_checkpoint(unet_checkpoint, unet.config)

				__lowerCAmelCase 	=      set(unet.state_dict().keys()) - set(converted_ckpt.keys())
				__lowerCAmelCase 	=      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 --





