





'''simple docstring'''

lowercase_					= [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_					= [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
lowercase_					= {
    0: """Sunday""",
    1: """Monday""",
    2: """Tuesday""",
    3: """Wednesday""",
    4: """Thursday""",
    5: """Friday""",
    6: """Saturday""",
}





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    int					,       __lowerCamelCase :    int    )						->str:
    assert len(str(__lowerCamelCase    )    ) > 2, "year should be in YYYY format"
    assert 1 <= month <= 12, "month should be between 1 to 12"
    assert 1 <= day <= 31, "day should be between 1 to 31"

    # Doomsday algorithm:
    _SCREAMING_SNAKE_CASE									=			year // 100
    _SCREAMING_SNAKE_CASE									=			(5 * (century % 4) + 2) % 7
    _SCREAMING_SNAKE_CASE									=			year % 100
    _SCREAMING_SNAKE_CASE									=			centurian % 12
    _SCREAMING_SNAKE_CASE									=			(
        (centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
    ) % 7
    _SCREAMING_SNAKE_CASE									=			(
        DOOMSDAY_NOT_LEAP[month - 1]
        if (year % 4 != 0) or (centurian == 0 and (year % 400) == 0)
        else DOOMSDAY_LEAP[month - 1]
    )
    _SCREAMING_SNAKE_CASE									=			(dooms_day + day - day_anchor) % 7
    return WEEK_DAY_NAMES[week_day]


if __name__ == "__main__":
      import doctest

      doctest.testmod()










'''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)
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 :    Optional[Any]					,       __lowerCamelCase :    Union[str, Any]    )						->List[Any]:
    _SCREAMING_SNAKE_CASE									=			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					,       )
    _SCREAMING_SNAKE_CASE									=			torch.load(__lowerCamelCase					,       lambda __lowerCamelCase					,       __lowerCamelCase    : storage    )
    _SCREAMING_SNAKE_CASE									=			AbsSummarizer(__lowerCamelCase					,       torch.device("""cpu"""    )					,       __lowerCamelCase    )
    original.eval()

    _SCREAMING_SNAKE_CASE									=			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"""    )
    _SCREAMING_SNAKE_CASE									=			BertTokenizer.from_pretrained("""bert-base-uncased"""    )

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

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

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

    _SCREAMING_SNAKE_CASE									=			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,
      )



