


'''simple docstring'''






import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__UpperCAmelCase       							=				False
__UpperCAmelCase       							=				True
__UpperCAmelCase       							=				False


if __name__ == "__main__":
		__UpperCAmelCase       							=				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.''')

		__UpperCAmelCase       							=				parser.parse_args()

		__UpperCAmelCase       							=				{
		    '''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''',
		}

		__UpperCAmelCase       							=				{
		    '''time_steps''': '''time_proj''',
		    '''mid''': '''mid_block''',
		    '''downsample_blocks''': '''down_blocks''',
		    '''upsample_blocks''': '''up_blocks''',
		}

		__UpperCAmelCase       							=				'''''' 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:
				__UpperCAmelCase       							=				reader.read()
				__UpperCAmelCase       							=				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'''):
				__UpperCAmelCase       							=				UNetaDModel(**config)
		else:
				__UpperCAmelCase       							=				UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
				__UpperCAmelCase       							=				class_name(**config)

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

		__UpperCAmelCase       							=				dict(model.config)

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

				__UpperCAmelCase       							=				[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
				__UpperCAmelCase       							=				[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

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

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



'''simple docstring'''






import re


def    _snake_case  (	A			)		->       bool:
							lowerCAmelCase__			  =      re.compile(R'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$'''			)
							if match := re.search(A					,       A			):
														return match.string == phone
							return False


if __name__ == "__main__":
		print(indian_phone_validator('''+918827897895'''))