



"""simple docstring"""






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case							:   Dict      =    False
__snake_case							:   str      =    True
__snake_case							:   int      =    False


if __name__ == "__main__":
	__snake_case							:   Dict      =    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.')

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

	__snake_case							:   List[str]      =    {
	    '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',
	}

	__snake_case							:   Union[str, Any]      =    {
	    'time_steps': 'time_proj',
	    'mid': 'mid_block',
	    'downsample_blocks': 'down_blocks',
	    'upsample_blocks': 'up_blocks',
	}

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

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

	__snake_case							:   str      =    dict(model.config)

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

		__snake_case							:   Tuple      =    [k.replace('UNetRes', '') for k in config['down_block_types']]
		__snake_case							:   Union[str, Any]      =    [k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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




"""simple docstring"""






import gc
import threading
import time

import psutil
import torch



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       psutil.Process()
				__lowerCAmelCase      :  str												=       False

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       -1

				while True:
							__lowerCAmelCase      :  str												=       max(self.process.memory_info().rss       ,			self.cpu_memory_peak)

							# can't sleep or will not catch the peak right (this comment is here on purpose)
							if not self.peak_monitoring:
										break

	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       int:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       True
				__lowerCAmelCase      :  str												=       threading.Thread(target=self.peak_monitor)
				__lowerCAmelCase      :  Tuple												=       True
				self.thread.start()







	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       False
				self.thread.join()
				return self.cpu_memory_peak






__snake_case							:   Tuple      =    PeakCPUMemory()


def 			_lowercase				(  )						->   str:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time()}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  Optional[Any]												=       psutil.Process().memory_info().rss
			cpu_peak_tracker.start()

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       torch.cuda.memory_allocated(__snake_case	)
			torch.cuda.reset_peak_memory_stats()

			return measures


def 			_lowercase				(  __snake_case	)						->   Optional[Any]:
			# Time
			__lowerCAmelCase      :  str												=       {"time": time.time() - start_measures["time"]}

			gc.collect()
			torch.cuda.empty_cache()

			# CPU mem
			__lowerCAmelCase      :  str												=       (psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
			__lowerCAmelCase      :  List[str]												=       (cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

			# GPU mem
			for i in range(torch.cuda.device_count()	):
						__lowerCAmelCase      :  Union[str, Any]												=       (torch.cuda.memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20
						__lowerCAmelCase      :  Any												=       (torch.cuda.max_memory_allocated(__snake_case	) - start_measures[str(__snake_case	)]) / 2**20

			return measures





def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			print(F"""{description}:"""	)
			print(F"""- Time: {measures['time']:.2f}s"""	)
			for i in range(torch.cuda.device_count()	):
						print(F"""- GPU {i} allocated: {measures[str(__snake_case	)]:.2f}MiB"""	)
						__lowerCAmelCase      :  Optional[Any]												=       measures[F"""{i}-peak"""]
						print(F"""- GPU {i} peak: {peak:.2f}MiB"""	)
			print(F"""- CPU RAM allocated: {measures['cpu']:.2f}MiB"""	)
			print(F"""- CPU RAM peak: {measures['cpu-peak']:.2f}MiB"""	)