

'''simple docstring'''





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)
lowerCamelCase      				=     logging.getLogger(__name__)


lowerCamelCase      				=     '''Hello world! cécé herlolip'''


lowerCamelCase      				=     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        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							BertAbsConfig(
					    temp_dir='.'		,					finetune_bert=__snake_case		,					large=__snake_case		,					share_emb=__snake_case		,					use_bert_emb=__snake_case		,					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=2_048		,					dec_dropout=0.2		,					)
					__lowerCAmelCase					=							torch.load(__snake_case		,					lambda UpperCAmelCase__		,					UpperCAmelCase__      : storage      )
					__lowerCAmelCase					=							AbsSummarizer(__snake_case		,					torch.device('cpu'      )		,					__snake_case      )
					original.eval()

					__lowerCAmelCase					=							BertAbsSummarizer(__snake_case		,					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					=							BertTokenizer.from_pretrained('bert-base-uncased'      )

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

					__lowerCAmelCase					=							new_model(
					    __snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case      )[0]
					__lowerCAmelCase					=							new_model.generator(__snake_case      )

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

					__lowerCAmelCase					=							torch.allclose(__snake_case		,					__snake_case		,					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__":
		lowerCamelCase      				=     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.''',
		)
		lowerCamelCase      				=     parser.parse_args()

		convert_bertabs_checkpoints(
		    args.bertabs_checkpoint_path,
		    args.pytorch_dump_folder_path,
		)









def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert x is not None
					assert y is not None

					__lowerCAmelCase					=							len(UpperCAmelCase__      )
					__lowerCAmelCase					=							len(UpperCAmelCase__      )

					# declaring the array for storing the dp values
					__lowerCAmelCase					=							[[0] * (n + 1) for _ in range(m + 1      )]  # noqa: E741

					for i in range(1		,					m + 1      ):
										for j in range(1		,					n + 1      ):
															__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

															__lowerCAmelCase					=							max(l[i - 1][j]		,					l[i][j - 1]		,					l[i - 1][j - 1] + match      )

					__lowerCAmelCase					=							''
					__lowerCAmelCase,	__lowerCAmelCase					=							m, n
					while i > 0 and j > 0:
										__lowerCAmelCase					=							1 if x[i - 1] == y[j - 1] else 0

										if l[i][j] == l[i - 1][j - 1] + match:
															if match == 1:
																				__lowerCAmelCase					=							x[i - 1] + seq
															i -= 1
															j -= 1
										elif l[i][j] == l[i - 1][j]:
															i -= 1
										else:
															j -= 1

					return l[m][n], seq


if __name__ == "__main__":
		lowerCamelCase      				=     '''AGGTAB'''
		lowerCamelCase      				=     '''GXTXAYB'''
		lowerCamelCase      				=     4
		lowerCamelCase      				=     '''GTAB'''

		lowerCamelCase				,				lowerCamelCase      				=     longest_common_subsequence(a, b)
		print('''len =''', ln, ''', sub-sequence =''', subseq)
		import doctest

		doctest.testmod()






