from collections.abc import Callable


class 	lowerCamelCase_     :






    '''simple docstring'''






    def __init__(	self , __lowercase = None)       ->	Union[str, Any]:
        # Stores actual heap items.
        __UpperCamelCase     :List[Any]			  =		[]
        # Stores indexes of each item for supporting updates and deletion.
        __UpperCamelCase     :List[Any]			  =		{}
        # Stores current size of heap.
        __UpperCamelCase     :Union[str, Any]			  =		0
        # Stores function used to evaluate the score of an item on which basis ordering
        # will be done.
        __UpperCamelCase     :List[Any]			  =		key or (lambda __lowercase: x)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Any:
        return int((i - 1) / 2) if i > 0 else None







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Union[str, Any]:
        __UpperCamelCase     :Any			  =		int(2 * i + 1)
        return left if 0 < left < self.size else None







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Tuple:
        __UpperCamelCase     :Optional[int]			  =		int(2 * i + 2)
        return right if 0 < right < self.size else None







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Tuple:
        __UpperCamelCase  ,       __UpperCamelCase     :Dict			  =		(
            self.pos_map[self.arr[j][0]],
            self.pos_map[self.arr[i][0]],
        )
        # Then swap the items in the list.
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		self.arr[j], self.arr[i]







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	Any:
        return self.arr[i][1] < self.arr[j][1]







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Optional[Any]:
        __UpperCamelCase     :Optional[int]			  =		self._left(__lowercase)
        __UpperCamelCase     :Dict			  =		self._right(__lowercase)
        __UpperCamelCase     :List[Any]			  =		i

        if left is not None and not self._cmp(__lowercase , __lowercase):
            __UpperCamelCase     :Optional[int]			  =		left
        if right is not None and not self._cmp(__lowercase , __lowercase):
            __UpperCamelCase     :Dict			  =		right

        return valid_parent







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Any:
        __UpperCamelCase     :Optional[Any]			  =		self._parent(__lowercase)
        while parent is not None and not self._cmp(__lowercase , __lowercase):
            self._swap(__lowercase , __lowercase)
            __UpperCamelCase  ,       __UpperCamelCase     :int			  =		parent, self._parent(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	int:
        __UpperCamelCase     :Union[str, Any]			  =		self._get_valid_parent(__lowercase)
        while valid_parent != index:
            self._swap(__lowercase , __lowercase)
            __UpperCamelCase  ,       __UpperCamelCase     :List[Any]			  =		valid_parent, self._get_valid_parent(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	int:
        if item not in self.pos_map:
            return
        __UpperCamelCase     :Any			  =		self.pos_map[item]
        __UpperCamelCase     :Dict			  =		[item, self.key(__lowercase)]
        # Make sure heap is right in both up and down direction.
        # Ideally only one of them will make any change.
        self._heapify_up(__lowercase)
        self._heapify_down(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase)       ->	Tuple:
        if item not in self.pos_map:
            return
        __UpperCamelCase     :Dict			  =		self.pos_map[item]
        del self.pos_map[item]
        __UpperCamelCase     :Optional[Any]			  =		self.arr[self.size - 1]
        __UpperCamelCase     :Optional[Any]			  =		index
        self.size -= 1
        # Make sure heap is right in both up and down direction. Ideally only one
        # of them will make any change- so no performance loss in calling both.
        if self.size > index:
            self._heapify_up(__lowercase)
            self._heapify_down(__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase)       ->	List[Any]:
        __UpperCamelCase     :Optional[Any]			  =		len(self.arr)
        if arr_len == self.size:
            self.arr.append([item, self.key(__lowercase)])
        else:
            __UpperCamelCase     :str			  =		[item, self.key(__lowercase)]
        __UpperCamelCase     :int			  =		self.size
        self.size += 1
        self._heapify_up(self.size - 1)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        return self.arr[0] if self.size else None







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        __UpperCamelCase     :Any			  =		self.get_top()
        if top_item_tuple:
            self.delete_item(top_item_tuple[0])
        return top_item_tuple



def lowerCamelCase  (   ):
    '''simple docstring'''












if __name__ == "__main__":
    import doctest

    doctest.testmod()







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				False
SCREAMING_SNAKE_CASE							:Any							 =				True
SCREAMING_SNAKE_CASE							:Tuple							 =				False


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Tuple							 =				argparse.ArgumentParser()

					parser.add_argument(
					    '--repo_path',
					    default=None,
					    type=str,
					    required=True,
					    help='The config json file corresponding to the architecture.',
					)

					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Dict							 =				{
					    'image_size': 'sample_size',
					    'num_res_blocks': 'layers_per_block',
					    'block_channels': 'block_out_channels',
					    'down_blocks': 'down_block_types',
					    'up_blocks': 'up_block_types',
					    'downscale_freq_shift': 'freq_shift',
					    'resnet_num_groups': 'norm_num_groups',
					    'resnet_act_fn': 'act_fn',
					    'resnet_eps': 'norm_eps',
					    'num_head_channels': 'attention_head_dim',
					}

					SCREAMING_SNAKE_CASE							:Optional[int]							 =				{
					    'time_steps': 'time_proj',
					    'mid': 'mid_block',
					    'downsample_blocks': 'down_blocks',
					    'upsample_blocks': 'up_blocks',
					}

					SCREAMING_SNAKE_CASE							:int							 =				'' if has_file(args.repo_path, 'config.json') else 'unet'

					with open(os.path.join(args.repo_path, subfolder, 'config.json'), 'r', encoding='utf-8') as reader:
										SCREAMING_SNAKE_CASE							:Dict							 =				reader.read()
										SCREAMING_SNAKE_CASE							:List[str]							 =				json.loads(text)

					if do_only_config:
										for key in config_parameters_to_change.keys():
															config.pop(key, None)

					if has_file(args.repo_path, 'config.json'):
										SCREAMING_SNAKE_CASE							:Optional[int]							 =				UNetaDModel(**config)
					else:
										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
										SCREAMING_SNAKE_CASE							:List[str]							 =				class_name(**config)

					if do_only_config:
										model.save_config(os.path.join(args.repo_path, subfolder))

					SCREAMING_SNAKE_CASE							:List[str]							 =				dict(model.config)

					if do_only_renaming:
										for key, value in config_parameters_to_change.items():
															if key in config:
																				SCREAMING_SNAKE_CASE							:Optional[Any]							 =				config[key]
																				del config[key]

										SCREAMING_SNAKE_CASE							:Optional[Any]							 =				[k.replace('UNetRes', '') for k in config['down_block_types']]
										SCREAMING_SNAKE_CASE							:List[Any]							 =				[k.replace('UNetRes', '') for k in config['up_block_types']]

					if do_only_weights:
										SCREAMING_SNAKE_CASE							:Tuple							 =				torch.load(os.path.join(args.repo_path, subfolder, 'diffusion_pytorch_model.bin'))

										SCREAMING_SNAKE_CASE							:Any							 =				{}
										for param_key, param_value in state_dict.items():
															if param_key.endswith('.op.bias') or param_key.endswith('.op.weight'):
																				continue
															SCREAMING_SNAKE_CASE							:List[str]							 =				False
															for key, new_key in key_parameters_to_change.items():
																				if not has_changed and param_key.split('.')[0] == key:
																									SCREAMING_SNAKE_CASE							:List[Any]							 =				param_value
																									SCREAMING_SNAKE_CASE							:str							 =				True
															if not has_changed:
																				SCREAMING_SNAKE_CASE							:List[str]							 =				param_value

										model.load_state_dict(new_state_dict)
										model.save_pretrained(os.path.join(args.repo_path, subfolder))
