



'''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__:     Union[str, Any]										=    logging.getLogger(__name__)


UpperCamelCase__:     Optional[int]										=    "Hello world! cécé herlolip"


UpperCamelCase__:     str										=    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_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		str						)   ->     Optional[Any]:
							UpperCAmelCase				: int											=	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     ,   )
							UpperCAmelCase				: Union[str, Any]											=	torch.load(_lowerCAmelCase     ,   lambda _lowerCAmelCase     ,   _lowerCAmelCase						: storage						)
							UpperCAmelCase				: Any											=	AbsSummarizer(_lowerCAmelCase     ,   torch.device('''cpu'''						)     ,   _lowerCAmelCase						)
							original.eval()

							UpperCAmelCase				: 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'''						)
							UpperCAmelCase				: List[Any]											=	BertTokenizer.from_pretrained('''bert-base-uncased'''						)

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

							UpperCAmelCase				: int											=	new_model(
							    _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)[0]
							UpperCAmelCase				: int											=	new_model.generator(_lowerCAmelCase						)

							UpperCAmelCase				: List[Any]											=	torch.max(torch.abs(output_converted_model - output_original_model						)						).item()
							print('''Maximum absolute difference beween weights: {:.2f}'''.format(_lowerCAmelCase						)						)
							UpperCAmelCase				: int											=	torch.max(torch.abs(output_converted_generator - output_original_generator						)						).item()
							print('''Maximum absolute difference beween weights: {:.2f}'''.format(_lowerCAmelCase						)						)

							UpperCAmelCase				: Optional[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__":
						UpperCamelCase__:     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.",
						)
						UpperCamelCase__:     List[str]										=    parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)










'''simple docstring'''






UpperCamelCase__:     dict[tuple[int, int, int], int]										=    {}


def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		int						)   ->     int:

							# if we are absent twice, or late 3 consecutive days,
							# no further prize strings are possible
							if late == 3 or absent == 2:
														return 0

							# if we have no days left, and have not failed any other rules,
							# we have a prize string
							if days == 0:
														return 1

							# No easy solution, so now we need to do the recursive calculation

							# First, check if the combination is already in the cache, and
							# if yes, return the stored value from there since we already
							# know the number of possible prize strings from this point on
							UpperCAmelCase				: List[Any]											=	(days, absent, late)
							if key in cache:
														return cache[key]

							# now we calculate the three possible ways that can unfold from
							# this point on, depending on our attendance today

							# 1) if we are late (but not absent), the "absent" counter stays as
							# it is, but the "late" counter increases by one
							UpperCAmelCase				: int											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   late + 1						)

							# 2) if we are absent, the "absent" counter increases by 1, and the
							# "late" counter resets to 0
							UpperCAmelCase				: Optional[Any]											=	_calculate(days - 1     ,   absent + 1     ,   0						)

							# 3) if we are on time, this resets the "late" counter and keeps the
							# absent counter
							UpperCAmelCase				: Tuple											=	_calculate(days - 1     ,   _lowerCAmelCase     ,   0						)

							UpperCAmelCase				: str											=	state_late + state_absent + state_ontime

							UpperCAmelCase				: List[Any]											=	prizestrings
							return prizestrings


def 				snake_case_  (      _lowerCAmelCase						:		int = 30						)   ->     int:
							return _calculate(_lowerCAmelCase     ,   absent=0     ,   late=0						)


if __name__ == "__main__":
						print(solution())





