





'''simple docstring'''







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      str )			->						Any:



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

						return orig_key





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      List[str] )			->						List[Any]:



						for key in orig_state_dict.copy().keys():
												UpperCAmelCase_      :   Optional[int]      =	orig_state_dict.pop(_a )

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		UpperCAmelCase_      :   Optional[int]      =	val

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

						return orig_state_dict





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      List[Any],      SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[Any]:
						UpperCAmelCase_      :   List[Any]      =	torch.load(_a,      map_location='''cpu''' )['model_state_dict']
						UpperCAmelCase_      :   Tuple      =	MraConfig.from_json_file(_a )
						UpperCAmelCase_      :   int      =	MraForMaskedLM(_a )

						UpperCAmelCase_      :   Optional[int]      =	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__":
					snake_case_  :							List[str]            =	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."
					)
					snake_case_  :							str            =	parser.parse_args()
					convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''
from .stack import Stack







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    __UpperCamelCase						:						Stack[str]		     =	Stack()
    __UpperCamelCase						:						Any		     =	{'(': ')', '[': ']', '{': '}'}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(_a      )
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


if __name__ == "__main__":
  from doctest import testmod

  testmod()

  a=	['''((()))''', '''((())''', '''(()))''']
  print('''Balanced parentheses demonstration:\n''')
  for example in examples:
    a=	'''''' if balanced_parentheses(example) else '''not '''
    print(F"""{example} is {not_str}balanced""")



