


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)
snake_case_						:			Dict													=							logging.getLogger(__name__)


snake_case_						:			Optional[int]													=							'''Hello world! cécé herlolip'''


snake_case_						:			Tuple													=							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__	,a__):
				_SCREAMING_SNAKE_CASE			  =BertAbsConfig(
				    temp_dir='''.'''	,finetune_bert=a__	,large=a__	,share_emb=a__	,use_bert_emb=a__	,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	,)
				_SCREAMING_SNAKE_CASE			  =torch.load(a__	,lambda a__	,a__: storage)
				_SCREAMING_SNAKE_CASE			  =AbsSummarizer(a__	,torch.device('''cpu''')	,a__)
				original.eval()

				_SCREAMING_SNAKE_CASE			  =BertAbsSummarizer(a__	,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''')
				_SCREAMING_SNAKE_CASE			  =BertTokenizer.from_pretrained('''bert-base-uncased''')

				# prepare the model inputs
				_SCREAMING_SNAKE_CASE			  =tokenizer.encode('''This is sample éàalj\'-.''')
				encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a__)))
				_SCREAMING_SNAKE_CASE			  =torch.tensor(a__).unsqueeze(0)
				_SCREAMING_SNAKE_CASE			  =tokenizer.encode('''This is sample 3 éàalj\'-.''')
				decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(a__)))
				_SCREAMING_SNAKE_CASE			  =torch.tensor(a__).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
				_SCREAMING_SNAKE_CASE			  =encoder_input_ids
				_SCREAMING_SNAKE_CASE			  =decoder_input_ids
				_SCREAMING_SNAKE_CASE			  =_SCREAMING_SNAKE_CASE			  =None
				_SCREAMING_SNAKE_CASE			  =None
				_SCREAMING_SNAKE_CASE			  =_SCREAMING_SNAKE_CASE			  =None
				_SCREAMING_SNAKE_CASE			  =_SCREAMING_SNAKE_CASE			  =None
				_SCREAMING_SNAKE_CASE			  =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
				_SCREAMING_SNAKE_CASE			  =original(a__	,a__	,a__	,a__	,a__	,a__	,a__)[0]
				_SCREAMING_SNAKE_CASE			  =original.generator(a__)

				_SCREAMING_SNAKE_CASE			  =new_model(
				    a__	,a__	,a__	,a__	,a__)[0]
				_SCREAMING_SNAKE_CASE			  =new_model.generator(a__)

				_SCREAMING_SNAKE_CASE			  =torch.max(torch.abs(output_converted_model - output_original_model)).item()
				print('''Maximum absolute difference beween weights: {:.2f}'''.format(a__))
				_SCREAMING_SNAKE_CASE			  =torch.max(torch.abs(output_converted_generator - output_original_generator)).item()
				print('''Maximum absolute difference beween weights: {:.2f}'''.format(a__))

				_SCREAMING_SNAKE_CASE			  =torch.allclose(a__	,a__	,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__":
							snake_case_						:			Optional[int]													=							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.''',
							)
							snake_case_						:			int													=							parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=False):
				if smoothing:
								if n == 0:
												raise ValueError('''log10(0) is undefined.''')
								return round(1 + logaa(n / (1 + df))	,3)

				if df == 0:
								raise ZeroDivisionError('''df must be > 0''')
				elif n == 0:
								raise ValueError('''log10(0) is undefined.''')
				return round(logaa(n / df)	,3)







def 						lowerCamelCase(   a__	,a__):
				return round(tf * idf	,3)