

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_			:					int    			=     logging.getLogger(__name__)


A_			:					Any    			=     'Hello world! cécé herlolip'


A_			:					Dict    			=     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 		UpperCamelCase   (lowercase_:					List[str]       , lowercase_:					Dict   )	-> Union[str, Any]:
	A__				:       Union[str, Any]	  =						BertAbsConfig(
	    temp_dir="""."""       , finetune_bert=lowercase_       , large=lowercase_       , share_emb=lowercase_       , use_bert_emb=lowercase_       , encoder="""bert"""       , max_pos=512       , enc_layers=6       , enc_hidden_size=512       , enc_heads=8       , enc_ff_size=512       , enc_dropout=0.2       , dec_layers=6       , dec_hidden_size=768       , dec_heads=8       , dec_ff_size=2048       , dec_dropout=0.2       , )
	A__				:       List[Any]	  =						torch.load(lowercase_       , lambda lowercase_       , lowercase_   : storage   )
	A__				:       Optional[Any]	  =						AbsSummarizer(lowercase_       , torch.device("""cpu"""   )       , lowercase_   )
	original.eval()

	A__				:       Any	  =						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"""   )
	A__				:       Any	  =						BertTokenizer.from_pretrained("""bert-base-uncased"""   )

	# prepare the model inputs
	A__				:       List[str]	  =						tokenizer.encode("""This is sample éàalj'-."""   )
	encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowercase_   ))   )
	A__				:       int	  =						torch.tensor(lowercase_   ).unsqueeze(0   )
	A__				:       str	  =						tokenizer.encode("""This is sample 3 éàalj'-."""   )
	decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(lowercase_   ))   )
	A__				:       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
	A__				:       List[Any]	  =						encoder_input_ids
	A__				:       Optional[int]	  =						decoder_input_ids
	A__				:       int	  =						None
	A__				:       Union[str, Any]	  =						None
	A__				:       List[str]	  =						None
	A__				:       str	  =						None
	A__				:       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
	A__				:       Union[str, Any]	  =						original(lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )[0]
	A__				:       str	  =						original.generator(lowercase_   )

	A__				:       Optional[int]	  =						new_model(
	    lowercase_       , lowercase_       , lowercase_       , lowercase_       , lowercase_   )[0]
	A__				:       List[Any]	  =						new_model.generator(lowercase_   )

	A__				:       Union[str, Any]	  =						torch.max(torch.abs(output_converted_model - output_original_model   )   ).item()
	print("""Maximum absolute difference beween weights: {:.2f}""".format(lowercase_   )   )
	A__				:       List[str]	  =						torch.max(torch.abs(output_converted_generator - output_original_generator   )   ).item()
	print("""Maximum absolute difference beween weights: {:.2f}""".format(lowercase_   )   )

	A__				:       Any	  =						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_			:					str    			=     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_			:					str    			=     parser.parse_args()

	convert_bertabs_checkpoints(
	    args.bertabs_checkpoint_path,
	    args.pytorch_dump_folder_path,
	)






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



