
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					:			Tuple			      =		logging.getLogger(__name__)


__lowerCAmelCase					:			List[Any]			      =		"Hello world! cécé herlolip"


__lowerCAmelCase					:			Optional[int]			      =		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_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						Union[str, Any]:
    __lowercase		:			List[str]	    =       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=2_048	,					dec_dropout=0.2	,					)
    __lowercase		:			Tuple	    =       torch.load(__lowerCAmelCase	,					lambda __lowerCAmelCase	,					__lowerCAmelCase	: storage	)
    __lowercase		:			Optional[int]	    =       AbsSummarizer(__lowerCAmelCase	,					torch.device('''cpu'''	)	,					__lowerCAmelCase	)
    original.eval()

    __lowercase		:			str	    =       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'''	)
    __lowercase		:			Optional[Any]	    =       BertTokenizer.from_pretrained('''bert-base-uncased'''	)

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

    __lowercase		:			List[Any]	    =       new_model(
        __lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)[0]
    __lowercase		:			Dict	    =       new_model.generator(__lowerCAmelCase	)

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

    __lowercase		:			Dict	    =       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__":
 __lowerCAmelCase					:			Optional[Any]			      =		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					:			str			      =		parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )




from __future__ import annotations


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						int:
    if not nums:
        return 0
    __lowercase		:			List[Any]	    =       nums[0]
    __lowercase		:			Union[str, Any]	    =       0
    for num in nums[1:]:
        __lowercase    ,      __lowercase		:			Dict	    =       (
            max_excluding + num,
            max(__lowerCAmelCase	,					__lowerCAmelCase	),
        )
    return max(__lowerCAmelCase	,					__lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()


