
import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def 	a_		(	__lowercase       :     Any			)			->  List[Any]:
			if "model" in orig_key:
						_snake_case          =      orig_key.replace('model.'	, ''			)
			if "norm1" in orig_key:
						_snake_case          =      orig_key.replace('norm1'	, 'attention.output.LayerNorm'			)
			if "norm2" in orig_key:
						_snake_case          =      orig_key.replace('norm2'	, 'output.LayerNorm'			)
			if "norm" in orig_key:
						_snake_case          =      orig_key.replace('norm'	, 'LayerNorm'			)
			if "transformer" in orig_key:
						_snake_case          =      orig_key.split('.'			)[0].split('_'			)[-1]
						_snake_case          =      orig_key.replace(f'''transformer_{layer_num}'''	, f'''encoder.layer.{layer_num}'''			)
			if "mha.attn" in orig_key:
						_snake_case          =      orig_key.replace('mha.attn'	, 'attention.self'			)
			if "mha" in orig_key:
						_snake_case          =      orig_key.replace('mha'	, 'attention'			)
			if "W_q" in orig_key:
						_snake_case          =      orig_key.replace('W_q'	, 'self.query'			)
			if "W_k" in orig_key:
						_snake_case          =      orig_key.replace('W_k'	, 'self.key'			)
			if "W_v" in orig_key:
						_snake_case          =      orig_key.replace('W_v'	, 'self.value'			)
			if "ff1" in orig_key:
						_snake_case          =      orig_key.replace('ff1'	, 'intermediate.dense'			)
			if "ff2" in orig_key:
						_snake_case          =      orig_key.replace('ff2'	, 'output.dense'			)
			if "ff" in orig_key:
						_snake_case          =      orig_key.replace('ff'	, 'output.dense'			)
			if "mlm_class" in orig_key:
						_snake_case          =      orig_key.replace('mlm.mlm_class'	, 'cls.predictions.decoder'			)
			if "mlm" in orig_key:
						_snake_case          =      orig_key.replace('mlm'	, 'cls.predictions.transform'			)
			if "cls" not in orig_key:
						_snake_case          =      'yoso.' + orig_key

			return orig_key






def 	a_		(	__lowercase       :     Tuple	, __lowercase       :     Union[str, Any]			)			->  List[Any]:
			for key in orig_state_dict.copy().keys():
						_snake_case          =      orig_state_dict.pop(__lowercase			)

						if ("pooler" in key) or ("sen_class" in key):
									continue
						else:
									_snake_case          =      val

			_snake_case          =      orig_state_dict['cls.predictions.decoder.bias']
			_snake_case          =      torch.arange(__lowercase			).expand((1, -1)			) + 2

			return orig_state_dict






def 	a_		(	__lowercase       :     List[str]	, __lowercase       :     Tuple	, __lowercase       :     Optional[int]			)			->  Tuple:
			_snake_case          =      torch.load(__lowercase	, map_location='cpu'			)['model_state_dict']
			_snake_case          =      YosoConfig.from_json_file(__lowercase			)
			_snake_case          =      YosoForMaskedLM(__lowercase			)

			_snake_case          =      convert_checkpoint_helper(config.max_position_embeddings	, __lowercase			)

			print(model.load_state_dict(__lowercase			)			)
			model.eval()
			model.save_pretrained(__lowercase			)

			print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''			)


if __name__ == "__main__":
			_lowerCamelCase	:    List[str]          =      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help='''The json file for YOSO model config.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			_lowerCamelCase	:    List[Any]          =      parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)

def 	a_		(	__lowercase       :     List[Any]			)			->  Tuple:
			_snake_case          =      len(__lowercase			)
			for i in range(length - 1			):
						_snake_case          =      i
						for k in range(i + 1	, __lowercase			):
									if collection[k] < collection[least]:
												_snake_case          =      k
						if least != i:
									_snake_case     , _snake_case          =      (collection[i], collection[least])
			return collection


if __name__ == "__main__":
			_lowerCamelCase	:    str          =      input('''Enter numbers separated by a comma:\n''').strip()
			_lowerCamelCase	:    List[Any]          =      [int(item) for item in user_input.split(''',''')]
			print(selection_sort(unsorted))