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			:							Any     =logging.getLogger(__name__)


lowerCAmelCase			:							int     ="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 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						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   , )
    _lowerCamelCase     :		int 						=						torch.load(lowerCamelCase_   , lambda __A   , __A	: storage	)
    _lowerCamelCase     :		List[str] 						=						AbsSummarizer(lowerCamelCase_   , torch.device("""cpu"""	)   , lowerCamelCase_	)
    original.eval()

    _lowerCamelCase     :		Optional[int] 						=						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     :		int 						=						BertTokenizer.from_pretrained("""bert-base-uncased"""	)

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

    _lowerCamelCase     :		List[Any] 						=						new_model(
        lowerCamelCase_   , lowerCamelCase_   , lowerCamelCase_   , lowerCamelCase_   , lowerCamelCase_	)[0]
    _lowerCamelCase     :		str 						=						new_model.generator(lowerCamelCase_	)

    _lowerCamelCase     :		int 						=						torch.max(torch.abs(output_converted_model - output_original_model	)	).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_	)	)
    _lowerCamelCase     :		Optional[int] 						=						torch.max(torch.abs(output_converted_generator - output_original_generator	)	).item()
    print("""Maximum absolute difference beween weights: {:.2f}""".format(lowerCamelCase_	)	)

    _lowerCamelCase     :		Any 						=						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[int]     =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			:							Dict     =parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




