


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''encoder.version''',
			    '''decoder.version''',
			    '''model.encoder.version''',
			    '''model.decoder.version''',
			    '''_float_tensor''',
			    '''decoder.output_projection.weight''',
			]
			for k in ignore_keys:
						state_dict.pop(lowercase__       ,			lowercase__  )


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase					, __lowercase 					=   emb.weight.shape
			__lowercase 					=   nn.Linear(lowercase__       ,			lowercase__       ,			bias=lowercase__  )
			__lowercase 					=   emb.weight.data
			return lin_layer


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase="facebook/mbart-large-en-ro"       ,			_UpperCamelCase=False       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







			__lowercase 					=   torch.load(lowercase__       ,			map_location='''cpu'''  )['''model''']
			remove_ignore_keys_(lowercase__  )
			__lowercase 					=   state_dict['''encoder.embed_tokens.weight'''].shape[0]

			__lowercase 					=   MBartConfig.from_pretrained(lowercase__       ,			vocab_size=lowercase__  )
			if mbart_aa and finetuned:
						__lowercase 					=   '''relu'''

			__lowercase 					=   state_dict['''decoder.embed_tokens.weight''']
			__lowercase 					=   MBartForConditionalGeneration(lowercase__  )
			model.model.load_state_dict(lowercase__  )

			if finetuned:
						__lowercase 					=   make_linear_from_emb(model.model.shared  )

			return model


if __name__ == "__main__":
	a : List[str]		=				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
	)
	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument(
	    '''--hf_config''',
	    default='''facebook/mbart-large-cc25''',
	    type=str,
	    help='''Which huggingface architecture to use: mbart-large''',
	)
	parser.add_argument('''--mbart_50''', action='''store_true''', help='''whether the model is mMART-50 checkpoint''')
	parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
	a : int		=				parser.parse_args()
	a : Tuple		=				convert_fairseq_mbart_checkpoint_from_disk(
	    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
	)
	model.save_pretrained(args.pytorch_dump_folder_path)






def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   []
			__lowercase 					=   []
			__lowercase 					=   {
			    '''^''': 3,
			    '''*''': 2,
			    '''/''': 2,
			    '''%''': 2,
			    '''+''': 1,
			    '''-''': 1,
			}  # Priority of each operator
			__lowercase 					=   len(_UpperCamelCase  ) if (len(_UpperCamelCase  ) > 7) else 7

			# Print table header for output
			print(
			    '''Symbol'''.center(8  )       ,			'''Stack'''.center(_UpperCamelCase  )       ,			'''Postfix'''.center(_UpperCamelCase  )       ,			sep=''' | '''       ,			)
			print('''-''' * (print_width * 3 + 7)  )

			for x in infix:
						if x.isalpha() or x.isdigit():
									post_fix.append(_UpperCamelCase  )  # if x is Alphabet / Digit, add it to Postfix
						elif x == "(":
									stack.append(_UpperCamelCase  )  # if x is "(" push to Stack
						elif x == ")":  # if x is ")" pop stack until "(" is encountered
									while stack[-1] != "(":
												post_fix.append(stack.pop()  )  # Pop stack & add the content to Postfix
									stack.pop()
						else:
									if len(_UpperCamelCase  ) == 0:
												stack.append(_UpperCamelCase  )  # If stack is empty, push x to stack
									else:  # while priority of x is not > priority of element in the stack
												while len(_UpperCamelCase  ) > 0 and priority[x] <= priority[stack[-1]]:
															post_fix.append(stack.pop()  )  # pop stack & add to Postfix
												stack.append(_UpperCamelCase  )  # push x to stack

						print(
						    x.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			while len(_UpperCamelCase  ) > 0:  # while stack is not empty
						post_fix.append(stack.pop()  )  # pop stack & add to Postfix
						print(
						    ''' '''.center(8  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			(''''''.join(_UpperCamelCase  )).ljust(_UpperCamelCase  )       ,			sep=''' | '''       ,			)  # Output in tabular format

			return "".join(_UpperCamelCase  )  # return Postfix as str


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   list(infix[::-1]  )  # reverse the infix equation

			for i in range(len(_UpperCamelCase  )  ):
						if infix[i] == "(":
									__lowercase 					=   ''')'''  # change "(" to ")"
						elif infix[i] == ")":
									__lowercase 					=   '''('''  # change ")" to "("

			return (infix_2_postfix(''''''.join(_UpperCamelCase  )  ))[
			    ::-1
			]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
	a : Union[str, Any]		=				input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
	a : Optional[Any]		=				''''''.join(Infix.split())  # Remove spaces from the input
	print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')


