

'''simple docstring'''



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case			:     Any										=      False
__snake_case			:     str										=      True
__snake_case			:     Union[str, Any]										=      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			:     int										=      parser.parse_args()

				__snake_case			:     Any										=      {
				    '''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			:     Tuple										=      {
				    '''time_steps''': '''time_proj''',
				    '''mid''': '''mid_block''',
				    '''downsample_blocks''': '''down_blocks''',
				    '''upsample_blocks''': '''up_blocks''',
				}

				__snake_case			:     str										=      '''''' 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			:     Any										=      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			:     str										=      UNetaDModel(**config)
				else:
								__snake_case			:     str										=      UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
								__snake_case			:     List[Any]										=      class_name(**config)

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

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

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

								__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
												__snake_case			:     Any										=      False
												for key, new_key in key_parameters_to_change.items():
																if not has_changed and param_key.split('.')[0] == key:
																				__snake_case			:     Optional[int]										=      param_value
																				__snake_case			:     Dict										=      True
												if not has_changed:
																__snake_case			:     Any										=      param_value

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




'''simple docstring'''



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			__snake_case        =	(CMStochasticIterativeScheduler,)
			__snake_case        =	10


			def lowercase__						(				self	:						List[str]		,      **lowerCAmelCase_	:						Dict			)      ->		Dict:





									'''simple docstring'''
									A__					:       int     ={
									    """num_train_timesteps""": 2_01,
									    """sigma_min""": 0.002,
									    """sigma_max""": 80.0,
									}

									config.update(**lowerCAmelCase_			)
									return config


			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Dict     =10

									A__					:       str     =self.get_scheduler_config()
									A__					:       Any     =self.scheduler_classes[0](**lowerCAmelCase_			)

									scheduler.set_timesteps(lowerCAmelCase_			)

									A__					:       List[Any]     =scheduler.timesteps[0]
									A__					:       Union[str, Any]     =scheduler.timesteps[1]

									A__					:       Optional[int]     =self.dummy_sample
									A__					:       Union[str, Any]     =0.1 * sample

									A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample
									A__					:       int     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_			).prev_sample

									self.assertEqual(output_a.shape		,      sample.shape			)
									self.assertEqual(output_a.shape		,      output_a.shape			)


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





									'''simple docstring'''
									for timesteps in [10, 50, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Tuple			)      ->		Optional[int]:





									'''simple docstring'''
									for clip_denoised in [True, False]:
															self.check_over_configs(clip_denoised=lowerCAmelCase_			)


			def lowercase__						(				self	:						List[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Any     =scheduler_class(**lowerCAmelCase_			)

									A__					:       int     =1
									scheduler.set_timesteps(lowerCAmelCase_			)
									A__					:       Any     =scheduler.timesteps

									A__					:       Optional[int]     =torch.manual_seed(0			)

									A__					:       List[Any]     =self.dummy_model()
									A__					:       Optional[int]     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for i, t in enumerate(lowerCAmelCase_			):
															# 1. scale model input
															A__					:       Optional[Any]     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       Union[str, Any]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Tuple     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Dict     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       Optional[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 192.7614			) < 1e-2
									assert abs(result_mean.item() - 0.2510			) < 1e-3


			def lowercase__						(				self	:						Dict			)      ->		Union[str, Any]:





									'''simple docstring'''
									A__					:       Dict     =self.scheduler_classes[0]
									A__					:       Dict     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[1_06, 0]
									scheduler.set_timesteps(timesteps=lowerCAmelCase_			)
									A__					:       List[Any]     =scheduler.timesteps

									A__					:       Optional[Any]     =torch.manual_seed(0			)

									A__					:       int     =self.dummy_model()
									A__					:       int     =self.dummy_sample_deter * scheduler.init_noise_sigma

									for t in timesteps:
															# 1. scale model input
															A__					:       Any     =scheduler.scale_model_input(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 2. predict noise residual
															A__					:       List[str]     =model(lowerCAmelCase_		,      lowerCAmelCase_			)

															# 3. predict previous sample x_t-1
															A__					:       Dict     =scheduler.step(lowerCAmelCase_		,      lowerCAmelCase_		,      lowerCAmelCase_		,      generator=lowerCAmelCase_			).prev_sample

															A__					:       Union[str, Any]     =pred_prev_sample

									A__					:       Union[str, Any]     =torch.sum(torch.abs(lowerCAmelCase_			)			)
									A__					:       List[Any]     =torch.mean(torch.abs(lowerCAmelCase_			)			)

									assert abs(result_sum.item() - 347.6357			) < 1e-2
									assert abs(result_mean.item() - 0.4527			) < 1e-3


			def lowercase__						(				self	:						Optional[Any]			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Union[str, Any]     =self.get_scheduler_config()
									A__					:       List[Any]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Tuple     =[39, 30, 12, 15, 0]

									with self.assertRaises(lowerCAmelCase_		,      msg="""`timesteps` must be in descending order."""			):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)


			def lowercase__						(				self	:						Union[str, Any]			)      ->		List[str]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =self.scheduler_classes[0]
									A__					:       List[str]     =self.get_scheduler_config()
									A__					:       Tuple     =scheduler_class(**lowerCAmelCase_			)

									A__					:       Dict     =[39, 30, 12, 1, 0]
									A__					:       int     =len(lowerCAmelCase_			)

									with self.assertRaises(lowerCAmelCase_		,      msg="""Can only pass one of `num_inference_steps` or `timesteps`."""			):
															scheduler.set_timesteps(num_inference_steps=lowerCAmelCase_		,      timesteps=lowerCAmelCase_			)



			def lowercase__						(				self	:						Any			)      ->		Optional[int]:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.scheduler_classes[0]
									A__					:       Any     =self.get_scheduler_config()
									A__					:       Optional[int]     =scheduler_class(**lowerCAmelCase_			)

									A__					:       List[str]     =[scheduler.config.num_train_timesteps]

									with self.assertRaises(
									    lowerCAmelCase_		,      msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""		,      ):
															scheduler.set_timesteps(timesteps=lowerCAmelCase_			)

