


import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		if "model" in orig_key:
				lowerCAmelCase__  :     int       	=   orig_key.replace('''model.'''      ,  ''''''			)
		if "norm1" in orig_key:
				lowerCAmelCase__  :     Optional[int]       	=   orig_key.replace('''norm1'''      ,  '''attention.output.LayerNorm'''			)
		if "norm2" in orig_key:
				lowerCAmelCase__  :     List[str]       	=   orig_key.replace('''norm2'''      ,  '''output.LayerNorm'''			)
		if "norm" in orig_key:
				lowerCAmelCase__  :     List[Any]       	=   orig_key.replace('''norm'''      ,  '''LayerNorm'''			)
		if "transformer" in orig_key:
				lowerCAmelCase__  :     str       	=   orig_key.split('''.'''			)[0].split('''_'''			)[-1]
				lowerCAmelCase__  :     Optional[int]       	=   orig_key.replace(f'transformer_{layer_num}'      ,  f'encoder.layer.{layer_num}'			)
		if "mha.attn" in orig_key:
				lowerCAmelCase__  :     Tuple       	=   orig_key.replace('''mha.attn'''      ,  '''attention.self'''			)
		if "mha" in orig_key:
				lowerCAmelCase__  :     Dict       	=   orig_key.replace('''mha'''      ,  '''attention'''			)
		if "W_q" in orig_key:
				lowerCAmelCase__  :     str       	=   orig_key.replace('''W_q'''      ,  '''self.query'''			)
		if "W_k" in orig_key:
				lowerCAmelCase__  :     Optional[int]       	=   orig_key.replace('''W_k'''      ,  '''self.key'''			)
		if "W_v" in orig_key:
				lowerCAmelCase__  :     Optional[Any]       	=   orig_key.replace('''W_v'''      ,  '''self.value'''			)
		if "ff.0" in orig_key:
				lowerCAmelCase__  :     Dict       	=   orig_key.replace('''ff.0'''      ,  '''intermediate.dense'''			)
		if "ff.2" in orig_key:
				lowerCAmelCase__  :     Tuple       	=   orig_key.replace('''ff.2'''      ,  '''output.dense'''			)
		if "ff" in orig_key:
				lowerCAmelCase__  :     Any       	=   orig_key.replace('''ff'''      ,  '''output.dense'''			)
		if "mlm_class" in orig_key:
				lowerCAmelCase__  :     Any       	=   orig_key.replace('''mlm.mlm_class'''      ,  '''cls.predictions.decoder'''			)
		if "mlm" in orig_key:
				lowerCAmelCase__  :     Union[str, Any]       	=   orig_key.replace('''mlm'''      ,  '''cls.predictions.transform'''			)
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase__  :     Union[str, Any]       	=   orig_key.replace('''backbone.backbone.encoders'''      ,  '''encoder.layer'''			)
		if "cls" not in orig_key:
				lowerCAmelCase__  :     Dict       	=   '''mra.''' + orig_key

		return orig_key






def 	lowerCamelCase_       (			_a      ,  _a			):




		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				lowerCAmelCase__  :     List[Any]       	=   orig_state_dict.pop(_a			)

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

		lowerCAmelCase__  :     Union[str, Any]       	=   orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCAmelCase__  :     List[str]       	=   torch.arange(_a			).expand((1, -1)			) + 2

		return orig_state_dict






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a			):




		"""simple docstring"""






		lowerCAmelCase__  :     int       	=   torch.load(_a      ,  map_location='''cpu'''			)['''model_state_dict''']
		lowerCAmelCase__  :     str       	=   MraConfig.from_json_file(_a			)
		lowerCAmelCase__  :     Optional[int]       	=   MraForMaskedLM(_a			)

		lowerCAmelCase__  :     str       	=   convert_checkpoint_helper(config.max_position_embeddings      ,  _a			)

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

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


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








from itertools import combinations_with_replacement






def 	lowerCamelCase_       (			_a = 100			):




		"""simple docstring"""






		lowerCAmelCase__  :     list[int]       	=   [*list(range(1      ,  21			)			), 25]
		lowerCAmelCase__  :     list[int]       	=   [2 * x for x in range(1      ,  21			)] + [50]
		lowerCAmelCase__  :     list[int]       	=   [3 * x for x in range(1      ,  21			)]
		lowerCAmelCase__  :     list[int]       	=   singles + doubles + triples + [0]

		lowerCAmelCase__  :     int       	=   0
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(_a      ,  2			):
						lowerCAmelCase__  :     Dict       	=   double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


if __name__ == "__main__":
		print(f'''{solution() = }''')




