from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowerCamelCase_				:		Any       						=       TypeVar("""T""")
lowerCamelCase_				:		List[Any]       						=       TypeVar("""U""")


class 						a__							(   Generic[T, U]		):



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Any:
								__a							=   key
								__a							=   val
								__a							=   0
								__a							=   None
								__a							=   None







			def __repr__(						self    ) ->    str:
								return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
								    self.key      ,						self.val      ,						self.freq      ,						self.next is not None      ,						self.prev is not None    )






class 						a__							(   Generic[T, U]		):



			def __init__(						self    ) ->    None:
								__a							=   DoubleLinkedListNode(UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   DoubleLinkedListNode(UpperCAmelCase      ,						UpperCAmelCase    )
								__a					,      __a							=   self.rear, self.head



			def __repr__(						self    ) ->    str:
								__a							=   ['DoubleLinkedList']
								__a							=   self.head
								while node.next is not None:
													rep.append(str(UpperCAmelCase    )    )
													__a							=   node.next
								rep.append(str(self.rear    )    )
								return ",\n    ".join(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    None:
								__a							=   self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								__a							=   node
								__a							=   previous
								__a							=   node
								__a							=   self.rear
								node.freq += 1
								self._position_node(UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    None:

								while node.prev is not None and node.prev.freq > node.freq:
													# swap node with previous node
													__a							=   node.prev

													__a							=   previous_node.prev
													__a							=   node.prev
													__a							=   previous_node
													__a							=   node







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    DoubleLinkedListNode[T, U] | None:

								if node.prev is None or node.next is None:
													return None

								__a							=   node.next
								__a							=   node.prev
								__a							=   None
								__a							=   None
								return node






class 						a__							(   Generic[T, U]		):
			A__				:   dict[Callable[[T], U], LFUCache[T, U]]								=			{}



			def __init__(						self      ,						UpperCAmelCase    ) ->    Optional[int]:
								__a							=   DoubleLinkedList()
								__a							=   capacity
								__a							=   0
								__a							=   0
								__a							=   0
								__a							=   {}



			def __repr__(						self    ) ->    str:
								return (
								    f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    f'''capacity={self.capacity}, current_size={self.num_keys})'''
								)



			def __contains__(						self      ,						UpperCAmelCase    ) ->    bool:
								return key in self.cache



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase    ) ->    U | None:

								if key in self.cache:
													self.hits += 1
													__a							=   self.cache[key]
													__a							=   self.list.remove(self.cache[key]    )
													assert node == value_node

													# node is guaranteed not None because it is in self.cache
													assert node is not None
													self.list.add(UpperCAmelCase    )
													return node.val
								self.miss += 1
								return None



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    None:

								if key not in self.cache:
													if self.num_keys >= self.capacity:
																		# delete first node when over capacity
																		__a							=   self.list.head.next

																		# guaranteed to have a non-None first node when num_keys > 0
																		# explain to type checker via assertions
																		assert first_node is not None
																		assert first_node.key is not None
																		assert self.list.remove(UpperCAmelCase    ) is not None
																		# first_node guaranteed to be in list

																		del self.cache[first_node.key]
																		self.num_keys -= 1
													__a							=   DoubleLinkedListNode(UpperCAmelCase      ,						UpperCAmelCase    )
													self.list.add(self.cache[key]    )
													self.num_keys += 1

								else:
													__a							=   self.list.remove(self.cache[key]    )
													assert node is not None  # node guaranteed to be in list
													__a							=   value
													self.list.add(UpperCAmelCase    )







			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls      ,						UpperCAmelCase = 1_2_8    ) ->    Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(UpperCAmelCase    ) -> Callable[..., U]:
													def cache_decorator_wrapper(*UpperCAmelCase    ) -> U:
																		if func not in cls.decorator_function_to_instance_map:
																							__a							=   LFUCache(UpperCAmelCase    )

																		__a							=   cls.decorator_function_to_instance_map[func].get(args[0]    )
																		if result is None:
																							__a							=   func(*UpperCAmelCase    )
																							cls.decorator_function_to_instance_map[func].put(args[0]      ,						UpperCAmelCase    )
																		return result

													def cache_info() -> LFUCache[T, U]:
																		return cls.decorator_function_to_instance_map[func]

													setattr(UpperCAmelCase      ,						'cache_info'      ,						UpperCAmelCase    )  # noqa: B010

													return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()











'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.0.W'''].data
			__lowercase   =							checkpoint['''all_modules.1.weight'''].data
			__lowercase   =							checkpoint['''all_modules.1.bias'''].data

			__lowercase   =							checkpoint['''all_modules.2.weight'''].data
			__lowercase   =							checkpoint['''all_modules.2.bias'''].data

			__lowercase   =							checkpoint['''all_modules.3.weight'''].data
			__lowercase   =							checkpoint['''all_modules.3.bias'''].data

			__lowercase   =							checkpoint[list(checkpoint.keys() )[-4]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-3]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-2]].data
			__lowercase   =							checkpoint[list(checkpoint.keys() )[-1]].data

			__lowercase   =							4

			def set_attention_weights(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T

						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_2.b"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
						__lowercase   =							old_checkpoint[f"all_modules.{index}.NIN_3.b"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

			def set_resnet_weights(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data

						__lowercase   =							old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
						__lowercase   =							old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data

						if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
									__lowercase   =							old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data

			for i, block in enumerate(new_model_architecture.downsample_blocks ):
						__lowercase   =							hasattr(lowerCamelCase_						,				'''attentions''' )
						for j in range(len(block.resnets ) ):
									set_resnet_weights(block.resnets[j]						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1
									if has_attentions:
												set_attention_weights(block.attentions[j]						,				lowerCamelCase_						,				lowerCamelCase_ )
												module_index += 1

						if hasattr(lowerCamelCase_						,				'''downsamplers''' ) and block.downsamplers is not None:
									set_resnet_weights(block.resnet_down						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1
									__lowercase   =							checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
									module_index += 1

			set_resnet_weights(new_model_architecture.mid_block.resnets[0]						,				lowerCamelCase_						,				lowerCamelCase_ )
			module_index += 1
			set_attention_weights(new_model_architecture.mid_block.attentions[0]						,				lowerCamelCase_						,				lowerCamelCase_ )
			module_index += 1
			set_resnet_weights(new_model_architecture.mid_block.resnets[1]						,				lowerCamelCase_						,				lowerCamelCase_ )
			module_index += 1

			for i, block in enumerate(new_model_architecture.up_blocks ):
						__lowercase   =							hasattr(lowerCamelCase_						,				'''attentions''' )
						for j in range(len(block.resnets ) ):
									set_resnet_weights(block.resnets[j]						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1
						if has_attentions:
									set_attention_weights(
									    block.attentions[0]						,				lowerCamelCase_						,				lowerCamelCase_ )  # why can there only be a single attention layer for up?
									module_index += 1

						if hasattr(lowerCamelCase_						,				'''resnet_up''' ) and block.resnet_up is not None:
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
									__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
									module_index += 1
									set_resnet_weights(block.resnet_up						,				lowerCamelCase_						,				lowerCamelCase_ )
									module_index += 1

			__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
			__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data
			module_index += 1
			__lowercase   =							checkpoint[f"all_modules.{module_index}.weight"].data
			__lowercase   =							checkpoint[f"all_modules.{module_index}.bias"].data

			return new_model_architecture.state_dict()


if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE    =			argparse.ArgumentParser()

	parser.add_argument(
	    '''--checkpoint_path''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin''',
	    type=str,
	    required=False,
	    help='''Path to the checkpoint to convert.''',
	)

	parser.add_argument(
	    '''--config_file''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/config.json''',
	    type=str,
	    required=False,
	    help='''The config json file corresponding to the architecture.''',
	)

	parser.add_argument(
	    '''--dump_path''',
	    default='''/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt''',
	    type=str,
	    required=False,
	    help='''Path to the output model.''',
	)

	_SCREAMING_SNAKE_CASE    =			parser.parse_args()

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

	with open(args.config_file) as f:
		_SCREAMING_SNAKE_CASE    =			json.loads(f.read())

	_SCREAMING_SNAKE_CASE    =			convert_ncsnpp_checkpoint(
	    checkpoint,
	    config,
	)

	if "sde" in config:
		del config["sde"]

	_SCREAMING_SNAKE_CASE    =			UNetaDModel(**config)
	model.load_state_dict(converted_checkpoint)

	try:
		_SCREAMING_SNAKE_CASE    =			ScoreSdeVeScheduler.from_config('''/'''.join(args.checkpoint_path.split('''/''')[:-1]))

		_SCREAMING_SNAKE_CASE    =			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
		pipe.save_pretrained(args.dump_path)
	except:  # noqa: E722
		model.save_pretrained(args.dump_path)





