

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        _lowercase  (					a_					:       List[str]					)					->						Tuple:




					'''simple docstring'''







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

					return orig_key




def        _lowercase  (					a_					:       List[str]       ,a_					:       Any					)					->						Tuple:




					'''simple docstring'''







					for key in orig_state_dict.copy().keys():
										__magic_name__				=     orig_state_dict.pop(UpperCamelCase__					)

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

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

					return orig_state_dict




def        _lowercase  (					a_					:       Optional[int]       ,a_					:       List[str]       ,a_					:       Any					)					->						Optional[int]:




					'''simple docstring'''







					__magic_name__				=     torch.load(UpperCamelCase__       ,map_location='cpu'					)['model_state_dict']
					__magic_name__				=     MraConfig.from_json_file(UpperCamelCase__					)
					__magic_name__				=     MraForMaskedLM(UpperCamelCase__					)

					__magic_name__				=     convert_checkpoint_helper(config.max_position_embeddings       ,UpperCamelCase__					)

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

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


if __name__ == "__main__":
						A__							=				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."
						)
						A__							=				parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from hashlib import mda, shaaaa

__A						:		Optional[int]          =						(shaaaa, mda)

class 		lowercase_       :




					def __init__(   self:		str,     _lowercase:		int = 8):


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									__lowerCAmelCase					=  size




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									self.bitarray |= h




					def     _lowercase						(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.hash_(_lowercase)
									return (h & self.bitarray) == h




					def __contains__(   self:		Dict,     _lowercase:		str):


									'''simple docstring'''


									return self.exists(_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(_lowercase)[2:]
									return res.zfill(self.size)




					@property
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									return self.format_bin(self.bitarray)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  0b0
									for func in HASH_FUNCTIONS:
													__lowerCAmelCase					=  (
													    int.from_bytes(func(value.encode()).digest(),     """little""") % self.size
													)
													res |= 2**position
									return res




					def     _lowercase						(   self:		Any,     _lowercase:		str):


									'''simple docstring'''


									return self.format_bin(self.hash_(_lowercase))







					@property
					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  bin(self.bitarray).count("""1""")
									return (n_ones / self.size) ** len(_lowercase)





