



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)
lowercase_	       =			logging.getLogger(__name__)


lowercase_	       =			'''Hello world! cécé herlolip'''


lowercase_	       =			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		(  __lowerCamelCase	:    Dict				,   __lowerCamelCase	:    Optional[Any]	)  ->						int:
					__lowerCAmelCase	   =BertAbsConfig(
					    temp_dir="""."""				,   finetune_bert=__lowerCamelCase				,   large=__lowerCamelCase				,   share_emb=__lowerCamelCase				,   use_bert_emb=__lowerCamelCase				,   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				,   )
					__lowerCAmelCase	   =torch.load(__lowerCamelCase				,   lambda __lowerCamelCase				,   __lowerCamelCase	: storage	)
					__lowerCAmelCase	   =AbsSummarizer(__lowerCamelCase				,   torch.device("""cpu"""	)				,   __lowerCamelCase	)
					original.eval()

					__lowerCAmelCase	   =BertAbsSummarizer(__lowerCamelCase				,   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(__lowerCamelCase	))	)
					__lowerCAmelCase	   =torch.tensor(__lowerCamelCase	).unsqueeze(0	)
					__lowerCAmelCase	   =tokenizer.encode("""This is sample 3 éàalj'-."""	)
					decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(__lowerCamelCase	))	)
					__lowerCAmelCase	   =torch.tensor(__lowerCamelCase	).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(__lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase				,   __lowerCamelCase	)[0]
					__lowerCAmelCase	   =original.generator(__lowerCamelCase	)

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

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

					__lowerCAmelCase	   =torch.allclose(__lowerCamelCase				,   __lowerCamelCase				,   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__":
				lowercase_	       =			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.''',
				)
				lowercase_	       =			parser.parse_args()

				convert_bertabs_checkpoints(
				    args.bertabs_checkpoint_path,
				    args.pytorch_dump_folder_path,
				)









def 					__lowerCAmelCase		(  __lowerCamelCase	:    list[int]	)  ->						int:
					if not numbers:
										return 0

					if not isinstance(__lowerCamelCase				,   (list, tuple)	) or not all(
					    isinstance(__lowerCamelCase				,   __lowerCamelCase	) for number in numbers	):
										raise ValueError("""numbers must be an iterable of integers"""	)

					__lowerCAmelCase	   =__lowerCAmelCase	   =__lowerCAmelCase	   =numbers[0]

					for i in range(1				,   len(__lowerCamelCase	)	):
										# update the maximum and minimum subarray products
										__lowerCAmelCase	   =numbers[i]
										if number < 0:
															__lowerCAmelCase   ,	__lowerCAmelCase	   =min_till_now, max_till_now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   max_till_now * number	)
										__lowerCAmelCase	   =min(__lowerCamelCase				,   min_till_now * number	)

										# update the maximum product found till now
										__lowerCAmelCase	   =max(__lowerCamelCase				,   __lowerCamelCase	)

					return max_prod




