
'''simple docstring'''
import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase					:    Union[str, Any]           = False
lowerCAmelCase					:    Optional[int]           = True
lowerCAmelCase					:    Union[str, Any]           = False


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, 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					:    Tuple           = 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					:    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					:    Any           = reader.read()
												lowerCAmelCase					:    Dict           = 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[Any]           = UNetaDModel(**config)
						else:
												lowerCAmelCase					:    Any           = UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
												lowerCAmelCase					:    Tuple           = class_name(**config)

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

						lowerCAmelCase					:    List[Any]           = dict(model.config)

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

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

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

												lowerCAmelCase					:    Any           = {}
												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					:    Optional[int]           = param_value
																														lowerCAmelCase					:    Tuple           = True
																		if not has_changed:
																								lowerCAmelCase					:    Dict           = param_value

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






'''simple docstring'''
import numpy as np







def 		A_(     A						:		str		,     A						:		Optional[Any]		,     A						:		Tuple		,     A						:		Optional[int]		,     A						:		str):
							UpperCamelCase   			=     int(np.ceil((x_end - xa) / h))
							UpperCamelCase   			=     np.zeros((n + 1,))
							UpperCamelCase   			=     ya
							UpperCamelCase   			=     xa

							for k in range(A):
														UpperCamelCase   			=     f(A		,     y[k])
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + 0.5 * h		,     y[k] + 0.5 * h * ka)
														UpperCamelCase   			=     f(x + h		,     y[k] + h * ka)
														UpperCamelCase   			=     y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
														x += h

							return y


if __name__ == "__main__":
						import doctest

						doctest.testmod()




