






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    :   List[Any]        =			logging.getLogger(__name__)


_a    :   str        =			"""Hello world! cécé herlolip"""


_a    :   Optional[Any]        =			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 				snake_case__							(							UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Any       ):
    lowerCAmelCase__   :int      							=				BertAbsConfig(
        temp_dir="."    ,			finetune_bert=UpperCAmelCase    ,			large=UpperCAmelCase    ,			share_emb=UpperCAmelCase    ,			use_bert_emb=UpperCAmelCase    ,			encoder="bert"    ,			max_pos=5_1_2    ,			enc_layers=6    ,			enc_hidden_size=5_1_2    ,			enc_heads=8    ,			enc_ff_size=5_1_2    ,			enc_dropout=0.2    ,			dec_layers=6    ,			dec_hidden_size=7_6_8    ,			dec_heads=8    ,			dec_ff_size=2_0_4_8    ,			dec_dropout=0.2    ,			)
    lowerCAmelCase__   :Any      							=				torch.load(UpperCAmelCase    ,			lambda UpperCAmelCase    ,			UpperCAmelCase       : storage       )
    lowerCAmelCase__   :int      							=				AbsSummarizer(UpperCAmelCase    ,			torch.device("cpu"       )    ,			UpperCAmelCase       )
    original.eval()

    lowerCAmelCase__   :Union[str, Any]      							=				BertAbsSummarizer(UpperCAmelCase    ,			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__   :Optional[int]      							=				BertTokenizer.from_pretrained("bert-base-uncased"       )

    # prepare the model inputs
    lowerCAmelCase__   :int      							=				tokenizer.encode("This is sample éàalj'-."       )
    encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase       ))       )
    lowerCAmelCase__   :List[Any]      							=				torch.tensor(UpperCAmelCase       ).unsqueeze(0       )
    lowerCAmelCase__   :Tuple      							=				tokenizer.encode("This is sample 3 éàalj'-."       )
    decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(UpperCAmelCase       ))       )
    lowerCAmelCase__   :List[str]      							=				torch.tensor(UpperCAmelCase       ).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__   :Union[str, Any]      							=				encoder_input_ids
    lowerCAmelCase__   :Optional[Any]      							=				decoder_input_ids
    lowerCAmelCase__   :int      							=				None
    lowerCAmelCase__   :List[Any]      							=				None
    lowerCAmelCase__   :Tuple      							=				None
    lowerCAmelCase__   :Tuple      							=				None
    lowerCAmelCase__   :Optional[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
    lowerCAmelCase__   :Optional[int]      							=				original(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )[0]
    lowerCAmelCase__   :Any      							=				original.generator(UpperCAmelCase       )

    lowerCAmelCase__   :int      							=				new_model(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )[0]
    lowerCAmelCase__   :Optional[Any]      							=				new_model.generator(UpperCAmelCase       )

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

    lowerCAmelCase__   :Optional[int]      							=				torch.allclose(UpperCAmelCase    ,			UpperCAmelCase    ,			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    :   List[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    :   Union[str, Any]        =			parser.parse_args()

 convert_bertabs_checkpoints(
     args.bertabs_checkpoint_path,
     args.pytorch_dump_folder_path,
 )











def 				snake_case__							(							UpperCAmelCase							:  str       ):
    return "".join(chr(ord(UpperCAmelCase       ) - 3_2       ) if "a" <= char <= "z" else char for char in word       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()



