
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,
 )






import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__lowerCAmelCase					:			int			      =		{
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__lowerCAmelCase					:			Optional[int]			      =		logging.WARNING


def     UpperCAmelCase_  (						) ->						Optional[Any]:
    __lowercase		:			Any	    =       os.getenv('''DATASETS_VERBOSITY'''	,					__lowerCAmelCase	)
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
                F'has to be one of: { ", ".join(log_levels.keys()	) }'	)
    return _default_log_level


def     UpperCAmelCase_  (						) ->						str:
    return __name__.split('''.'''	)[0]


def     UpperCAmelCase_  (						) ->						logging.Logger:
    return logging.getLogger(_get_library_name()	)


def     UpperCAmelCase_  (						) ->						None:
    # Apply our default configuration to the library root logger.
    __lowercase		:			Union[str, Any]	    =       _get_library_root_logger()
    library_root_logger.setLevel(_get_default_logging_level()	)


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			List[Any]	    =       _get_library_root_logger()
    library_root_logger.setLevel(logging.NOTSET	)


def     UpperCAmelCase_  (						__lowerCAmelCase = None	) ->						logging.Logger:
    if name is None:
        __lowercase		:			Optional[Any]	    =       _get_library_name()
    return logging.getLogger(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						int:
    return _get_library_root_logger().getEffectiveLevel()


def     UpperCAmelCase_  (						__lowerCAmelCase	) ->						None:
    _get_library_root_logger().setLevel(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						int:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						List[str]:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						Optional[int]:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						Any:
    return set_verbosity(__lowerCAmelCase	)


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			Tuple	    =       False


def     UpperCAmelCase_  (						) ->						None:
    __lowercase		:			Dict	    =       True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __init__(      self		:       List[Any]				,       *_snake_case		:       List[Any]				,       **_snake_case		:       Dict				):  # pylint: disable=unused-argument
          __lowercase		:			Tuple	    =       args[0] if args else None




      def __iter__(      self		:       Tuple				):
          return iter(self._iterator				)




      def __getattr__(      self		:       Optional[int]				,       _snake_case		:       Union[str, Any]				):

          def empty_fn(*_snake_case		:       Tuple				,       **_snake_case		:       int				):  # pylint: disable=unused-argument
              return

          return empty_fn




      def __enter__(      self		:       Optional[Any]				):
          return self




      def __exit__(      self		:       Optional[Any]				,       _snake_case		:       Any				,       _snake_case		:       List[Any]				,       _snake_case		:       List[Any]				):
          return






__lowerCAmelCase					:			int			      =		True


class 						__lowerCAmelCase	:



      """simple docstring"""







      def __call__(      self		:       List[str]				,       *_snake_case		:       Dict				,       _snake_case		:       str=False				,       **_snake_case		:       List[str]				):
          if _tqdm_active and not disable:
              return tqdm_lib.tqdm(*_snake_case				,       **_snake_case				)
          else:
              return EmptyTqdm(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Optional[Any]				,       *_snake_case		:       str				,       **_snake_case		:       List[str]				):
          __lowercase		:			Dict	    =       None
          if _tqdm_active:
              return tqdm_lib.tqdm.set_lock(*_snake_case				,       **_snake_case				)




      def 		snake_case_			(      self		:       Tuple				):
          if _tqdm_active:
              return tqdm_lib.tqdm.get_lock()






__lowerCAmelCase					:			Tuple			      =		_tqdm_cls()


def     UpperCAmelCase_  (						) ->						bool:
    global _tqdm_active
    return bool(_tqdm_active	)


def     UpperCAmelCase_  (						) ->						Union[str, Any]:
    global _tqdm_active
    __lowercase		:			Dict	    =       True





def     UpperCAmelCase_  (						) ->						Tuple:
    global _tqdm_active
    __lowercase		:			Optional[int]	    =       False


