






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase       :       Dict        =				False
lowerCamelCase       :       List[str]        =				True
lowerCamelCase       :       Tuple        =				False


if __name__ == "__main__":
	lowerCamelCase       :       List[Any]        =				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.''')

	lowerCamelCase       :       Optional[Any]        =				parser.parse_args()

	lowerCamelCase       :       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''',
	}

	lowerCamelCase       :       List[str]        =				{
	    '''time_steps''': '''time_proj''',
	    '''mid''': '''mid_block''',
	    '''downsample_blocks''': '''down_blocks''',
	    '''upsample_blocks''': '''up_blocks''',
	}

	lowerCamelCase       :       Optional[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:
		lowerCamelCase       :       Union[str, Any]        =				reader.read()
		lowerCamelCase       :       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'''):
		lowerCamelCase       :       List[str]        =				UNetaDModel(**config)
	else:
		lowerCamelCase       :       str        =				UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
		lowerCamelCase       :       Optional[Any]        =				class_name(**config)

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

	lowerCamelCase       :       Any        =				dict(model.config)

	if do_only_renaming:
		for key, value in config_parameters_to_change.items():
			if key in config:
				lowerCamelCase       :       List[str]        =				config[key]
				del config[key]

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

	if do_only_weights:
		lowerCamelCase       :       List[Any]        =				torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

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

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







import math

class 						lowerCAmelCase   :

					'''simple docstring'''

					def __init__(     self :		Tuple						,   __a :		int=0							)	->     Optional[Any]:  # a graph with Node 0,1,...,N-1


												"""simple docstring"""

												__lowercase					:							Any			=   n
												__lowercase					:							Optional[int]			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # adjacency matrix for weight
												__lowercase					:							Dict			=   [
												    [math.inf for j in range(0						,   __a							)] for i in range(0						,   __a							)
												]  # dp[i][j] stores minimum distance from i to j



					def 					lowerCAmelCase (     self :		int						,   __a :		Optional[int]						,   __a :		Tuple						,   __a :		Dict							)	->     Dict:


												"""simple docstring"""

												__lowercase					:							Any			=   w



					def 					lowerCAmelCase (     self :		List[str]							)	->     Tuple:


												"""simple docstring"""

												for k in range(0						,   self.n							):
																			for i in range(0						,   self.n							):
																										for j in range(0						,   self.n							):
																																	__lowercase					:							List[str]			=   min(self.dp[i][j]						,   self.dp[i][k] + self.dp[k][j]							)



					def 					lowerCAmelCase (     self :		Any						,   __a :		Any						,   __a :		str							)	->     int:


												"""simple docstring"""

												return self.dp[u][v]


if __name__ == "__main__":
	lowerCamelCase       :       int        =				Graph(5)
	graph.add_edge(0, 2, 9)
	graph.add_edge(0, 4, 10)
	graph.add_edge(1, 3, 5)
	graph.add_edge(2, 3, 7)
	graph.add_edge(3, 0, 10)
	graph.add_edge(3, 1, 2)
	graph.add_edge(3, 2, 1)
	graph.add_edge(3, 4, 6)
	graph.add_edge(4, 1, 3)
	graph.add_edge(4, 2, 4)
	graph.add_edge(4, 3, 9)
	graph.floyd_warshall()
	graph.show_min(1, 4)
	graph.show_min(0, 3)