





from math import factorial

lowerCAmelCase_        =							{str(d): factorial(d) for d in range(1_0)}






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				return sum(DIGIT_FACTORIAL[d] for d in str(_UpperCamelCase					)					)






def 	lowerCamelCase_		(							)						->		int:






				"""simple docstring"""


				snake_case_		:       Optional[int]           =    7 * factorial(9					) + 1
				return sum(i for i in range(3					,							_UpperCamelCase					) if sum_of_digit_factorial(_UpperCamelCase					) == i					)


if __name__ == "__main__":
	print(F'''{solution() = }''')




"""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)
UpperCAmelCase__					=logging.getLogger(__name__)


UpperCAmelCase__					="Hello world! cécé herlolip"


UpperCAmelCase__					=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_   (					UpperCamelCase__						:			Tuple					, UpperCamelCase__						:			Union[str, Any]			):






 """simple docstring"""







 __lowercase						=						BertAbsConfig(
     temp_dir="""."""					, finetune_bert=UpperCamelCase__					, large=UpperCamelCase__					, share_emb=UpperCamelCase__					, use_bert_emb=UpperCamelCase__					, 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					, )
 __lowercase						=						torch.load(UpperCamelCase__					, lambda UpperCamelCase__					, UpperCamelCase__			: storage			)
 __lowercase						=						AbsSummarizer(UpperCamelCase__					, torch.device("""cpu"""			)					, UpperCamelCase__			)
 original.eval()

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

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

 __lowercase						=						new_model(
     UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__					, UpperCamelCase__			)[0]
 __lowercase						=						new_model.generator(UpperCamelCase__			)

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

 __lowercase						=						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__":
       UpperCAmelCase__					=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.",
       )
       UpperCAmelCase__					=parser.parse_args()

       convert_bertabs_checkpoints(
           args.bertabs_checkpoint_path,
           args.pytorch_dump_folder_path,
       )
