import argparse
import logging
from collections import namedtuple

import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer  # The authors' implementation

from transformers import BertTokenizer


logging.basicConfig(level=logging.INFO)
A__           =							logging.getLogger(__name__)


A__           =							"""Hello world! cécé herlolip"""


A__           =							namedtuple(
    """BertAbsConfig""",
    [
        """temp_dir""",
        """large""",
        """use_bert_emb""",
        """finetune_bert""",
        """encoder""",
        """share_emb""",
        """max_pos""",
        """enc_layers""",
        """enc_hidden_size""",
        """enc_heads""",
        """enc_ff_size""",
        """enc_dropout""",
        """dec_layers""",
        """dec_hidden_size""",
        """dec_heads""",
        """dec_ff_size""",
        """dec_dropout""",
    ],
)





def  _lowerCamelCase					(   a_				: Optional[Any]						,			a_				: Optional[Any]):
   lowerCamelCase						:List[str]						  =     BertAbsConfig(
       temp_dir='''.'''						,			finetune_bert=lowercase__						,			large=lowercase__						,			share_emb=lowercase__						,			use_bert_emb=lowercase__						,			encoder='''bert'''						,			max_pos=5_12						,			enc_layers=6						,			enc_hidden_size=5_12						,			enc_heads=8						,			enc_ff_size=5_12						,			enc_dropout=0.2						,			dec_layers=6						,			dec_hidden_size=7_68						,			dec_heads=8						,			dec_ff_size=20_48						,			dec_dropout=0.2						,			)
   lowerCamelCase						:Optional[int]						  =     torch.load(lowercase__						,			lambda a_						,			a_: storage)
   lowerCamelCase						:Union[str, Any]						  =     AbsSummarizer(lowercase__						,			torch.device('''cpu''')						,			lowercase__)
   original.eval()

   lowerCamelCase						:Dict						  =     BertAbsSummarizer(lowercase__						,			torch.device('''cpu'''))
   new_model.eval()

   # -------------------
   # Convert the weights
   # -------------------

   logging.info('''convert the model''')
   new_model.bert.load_state_dict(original.bert.state_dict())
   new_model.decoder.load_state_dict(original.decoder.state_dict())
   new_model.generator.load_state_dict(original.generator.state_dict())

   # ----------------------------------
   # Make sure the outpus are identical
   # ----------------------------------

   logging.info('''Make sure that the models\' outputs are identical''')
   lowerCamelCase						:List[Any]						  =     BertTokenizer.from_pretrained('''bert-base-uncased''')

   # prepare the model inputs
   lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode('''This is sample éàalj\'-.''')
   encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowercase__)))
   lowerCamelCase						:Any						  =     torch.tensor(lowercase__).unsqueeze(0)
   lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode('''This is sample 3 éàalj\'-.''')
   decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowercase__)))
   lowerCamelCase						:str						  =     torch.tensor(lowercase__).unsqueeze(0)

   # failsafe to make sure the weights reset does not affect the
   # loaded weights.
   assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight)) == 0

   # forward pass
   lowerCamelCase						:Optional[int]						  =     encoder_input_ids
   lowerCamelCase						:List[str]						  =     decoder_input_ids
   lowerCamelCase						:Tuple						  =     None
   lowerCamelCase						:int						  =     None
   lowerCamelCase						:List[str]						  =     None
   lowerCamelCase						:Optional[Any]						  =     None
   lowerCamelCase						:Optional[int]						  =     None

   # The original model does not apply the geneator layer immediatly but rather in
   # the beam search (where it combines softmax + linear layer). Since we already
   # apply the softmax in our generation process we only apply the linear layer here.
   # We make sure that the outputs of the full stack are identical
   lowerCamelCase						:Dict						  =     original(lowercase__						,			lowercase__						,			lowercase__						,			lowercase__						,			lowercase__						,			lowercase__						,			lowercase__)[0]
   lowerCamelCase						:Union[str, Any]						  =     original.generator(lowercase__)

   lowerCamelCase						:List[Any]						  =     new_model(
       lowercase__						,			lowercase__						,			lowercase__						,			lowercase__						,			lowercase__)[0]
   lowerCamelCase						:Dict						  =     new_model.generator(lowercase__)

   lowerCamelCase						:Dict						  =     torch.max(torch.abs(output_converted_model - output_original_model)).item()
   print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowercase__))
   lowerCamelCase						:Optional[Any]						  =     torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
   print('''Maximum absolute difference beween weights: {:.2f}'''.format(lowercase__))

   lowerCamelCase						:List[str]						  =     torch.allclose(lowercase__						,			lowercase__						,			atol=1e-3)
   if are_identical:
      logging.info('''all weights are equal up to 1e-3''')
   else:
      raise ValueError('''the weights are different. The new model is likely different from the original one.''')

   # The model has been saved with torch.save(model) and this is bound to the exact
   # directory structure. We save the state_dict instead.
   logging.info('''saving the model\'s state dictionary''')
   torch.save(
       new_model.state_dict()						,			'''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''')


if __name__ == "__main__":
   A__           =							argparse.ArgumentParser()
   parser.add_argument(
       """--bertabs_checkpoint_path""",
       default=None,
       type=str,
       required=True,
       help="""Path the official PyTorch dump.""",
   )
   parser.add_argument(
       """--pytorch_dump_folder_path""",
       default=None,
       type=str,
       required=True,
       help="""Path to the output PyTorch model.""",
   )
   A__           =							parser.parse_args()

   convert_bertabs_checkpoints(
       args.bertabs_checkpoint_path,
       args.pytorch_dump_folder_path,
   )




import operator as op





def  _lowerCamelCase					(   a_				: Tuple):
			lowerCamelCase						:int						  =     []
			lowerCamelCase						:List[str]						  =     lambda a_						,			a_: int(x / y)  # noqa: E731 integer division operation
			lowerCamelCase						:Optional[int]						  =     {
			    '''^''': op.pow,
			    '''*''': op.mul,
			    '''/''': div,
			    '''+''': op.add,
			    '''-''': op.sub,
			}  # operators & their respective operation

			# print table header
			print('''Symbol'''.center(8)						,			'''Action'''.center(12)						,			'''Stack'''						,			sep=''' | ''')
			print('''-''' * (30 + len(a_)))

			for x in post_fix:
						if x.isdigit():  # if x in digit
									stack.append(a_)  # append x to stack
									# output in tabular format
									print(x.rjust(8)						,			('''push(''' + x + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')
						else:
									lowerCamelCase						:Optional[Any]						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									lowerCamelCase						:str						  =     stack.pop()  # pop stack
									# output in tabular format
									print(''''''.rjust(8)						,			('''pop(''' + a + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | ''')

									stack.append(
									    str(opr[x](int(a_)						,			int(a_))))  # evaluate the 2 values popped from stack & push result to stack
									# output in tabular format
									print(
									    x.rjust(8)						,			('''push(''' + a + x + b + ''')''').ljust(12)						,			''','''.join(a_)						,			sep=''' | '''						,			)

			return int(stack[0])


if __name__ == "__main__":
			A__           =							input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))



