



'''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'''






import numpy as np

UpperCamelCase__:     Any										=    [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]

class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	def __init__(		self    :  Any	)    ->	None:
								UpperCAmelCase				: Optional[int]											=	np.array(__snake_case	)







	def   A			(		self    :  str	,    __snake_case    :  str	)    ->	np.ndarray:
								UpperCAmelCase							,	UpperCAmelCase				: Dict											=	np.where(letter == self.SQUARE	)
								UpperCAmelCase				: Dict											=	np.concatenate([indexa + 1, indexa + 1]	)
								return indexes







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  int	)    ->	str:
								UpperCAmelCase				: int											=	self.SQUARE[indexa - 1, indexa - 1]
								return letter







	def   A			(		self    :  List[Any]	,    __snake_case    :  str	)    ->	str:
								UpperCAmelCase				: Any											=	message.lower()
								UpperCAmelCase				: List[Any]											=	message.replace(''' '''	,    ''''''	)
								UpperCAmelCase				: List[Any]											=	message.replace('''j'''	,    '''i'''	)

								UpperCAmelCase				: Tuple											=	np.empty((2, len(__snake_case	))	)
								for letter_index in range(len(__snake_case	)	):
															UpperCAmelCase				: str											=	self.letter_to_numbers(message[letter_index]	)

															UpperCAmelCase				: Any											=	numbers[0]
															UpperCAmelCase				: Optional[Any]											=	numbers[1]

								UpperCAmelCase				: List[str]											=	first_step.reshape(2 * len(__snake_case	)	)
								UpperCAmelCase				: Union[str, Any]											=	''''''
								for numbers_index in range(len(__snake_case	)	):
															UpperCAmelCase				: Dict											=	int(second_step[numbers_index * 2]	)
															UpperCAmelCase				: Union[str, Any]											=	int(second_step[(numbers_index * 2) + 1]	)
															UpperCAmelCase				: Dict											=	self.numbers_to_letter(__snake_case	,    __snake_case	)
															UpperCAmelCase				: int											=	encoded_message + letter

								return encoded_message







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  str	)    ->	str:
								UpperCAmelCase				: str											=	message.lower()
								message.replace(''' '''	,    ''''''	)
								UpperCAmelCase				: Optional[int]											=	np.empty(2 * len(__snake_case	)	)
								for letter_index in range(len(__snake_case	)	):
															UpperCAmelCase				: int											=	self.letter_to_numbers(message[letter_index]	)
															UpperCAmelCase				: Optional[Any]											=	numbers[0]
															UpperCAmelCase				: List[Any]											=	numbers[1]

								UpperCAmelCase				: Tuple											=	first_step.reshape((2, len(__snake_case	))	)
								UpperCAmelCase				: str											=	''''''
								for numbers_index in range(len(__snake_case	)	):
															UpperCAmelCase				: Tuple											=	int(second_step[0, numbers_index]	)
															UpperCAmelCase				: List[str]											=	int(second_step[1, numbers_index]	)
															UpperCAmelCase				: Union[str, Any]											=	self.numbers_to_letter(__snake_case	,    __snake_case	)
															UpperCAmelCase				: Union[str, Any]											=	decoded_message + letter

								return decoded_message





