






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 		__UpperCAmelCase     (	a_				,	a_):
							snake_case_           =				BertAbsConfig(
							    temp_dir='.'				,	finetune_bert=a_				,	large=a_				,	share_emb=a_				,	use_bert_emb=a_				,	encoder='bert'				,	max_pos=5_12				,	enc_layers=6				,	enc_hidden_size=5_12				,	enc_heads=8				,	enc_ff_size=5_12				,	enc_dropout=0.2				,	dec_layers=6				,	dec_hidden_size=7_68				,	dec_heads=8				,	dec_ff_size=20_48				,	dec_dropout=0.2				,	)
							snake_case_           =				torch.load(a_				,	lambda a_				,	a_: storage)
							snake_case_           =				AbsSummarizer(a_				,	torch.device('cpu')				,	a_)
							original.eval()

							snake_case_           =				BertAbsSummarizer(a_				,	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')
							snake_case_           =				BertTokenizer.from_pretrained('bert-base-uncased')

							# prepare the model inputs
							snake_case_           =				tokenizer.encode('This is sample éàalj\'-.')
							encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(a_)))
							snake_case_           =				torch.tensor(a_).unsqueeze(0)
							snake_case_           =				tokenizer.encode('This is sample 3 éàalj\'-.')
							decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(a_)))
							snake_case_           =				torch.tensor(a_).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
							snake_case_           =				encoder_input_ids
							snake_case_           =				decoder_input_ids
							snake_case_           =				snake_case_           =				None
							snake_case_           =				None
							snake_case_           =				snake_case_           =				None
							snake_case_           =				snake_case_           =				None
							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
							snake_case_           =				original(a_				,	a_				,	a_				,	a_				,	a_				,	a_				,	a_)[0]
							snake_case_           =				original.generator(a_)

							snake_case_           =				new_model(
							    a_				,	a_				,	a_				,	a_				,	a_)[0]
							snake_case_           =				new_model.generator(a_)

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

							snake_case_           =				torch.allclose(a_				,	a_				,	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,
	)













import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_rembert import RemBertTokenizer
else:
	lowercase    				=   None

lowercase    				=   logging.get_logger(__name__)
lowercase    				=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

lowercase    				=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

lowercase    				=   {
    "google/rembert": 256,
}

lowercase    				=   "▁"




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					VOCAB_FILES_NAMES
	lowerCAmelCase						=					PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase						=					RemBertTokenizer




	def __init__(			self			,     a=None			,     a=None			,     a=True			,     a=True			,     a=False			,     a="[CLS]"			,     a="[SEP]"			,     a="<unk>"			,     a="[SEP]"			,     a="<pad>"			,     a="[CLS]"			,     a="[MASK]"			,     **a			,     )			->       Union[str, Any]:
								# Mask token behave like a normal word, i.e. include the space before it
								snake_case_           =				AddedToken(a			,     lstrip=a			,     rstrip=a   ) if isinstance(a			,     a   ) else mask_token

								super().__init__(
								    a			,     tokenizer_file=a			,     do_lower_case=a			,     remove_space=a			,     keep_accents=a			,     bos_token=a			,     eos_token=a			,     unk_token=a			,     sep_token=a			,     pad_token=a			,     cls_token=a			,     mask_token=a			,     **a			,     )

								snake_case_           =				do_lower_case
								snake_case_           =				remove_space
								snake_case_           =				keep_accents
								snake_case_           =				vocab_file
								snake_case_           =				False if not self.vocab_file else True




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[self.cls_token_id]
								if token_ids_a is None:
															return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep




	def 				_UpperCamelCase     (			self			,     a			,     a = None			,     a = False   )			->       List[int]:

								if already_has_special_tokens:
															if token_ids_a is not None:
																						raise ValueError(
																						    'You should not supply a second sequence if the provided sequence of '
																						    'ids is already formatted with special tokens for the model.'   )
															return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
															return [1] + ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]
								return [1] + ([0] * len(a   )) + [1]




	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       List[int]:
								snake_case_           =				[self.sep_token_id]
								snake_case_           =				[self.cls_token_id]

								if token_ids_a is None:
															return len(cls + token_ids_a + sep   ) * [0]
								return len(cls + token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]






	def 				_UpperCamelCase     (			self			,     a			,     a = None   )			->       Tuple[str]:
								if not os.path.isdir(a   ):
															logger.error('Vocabulary path ({}) should be a directory'.format(a   )   )
															return
								snake_case_           =				os.path.join(
								    a			,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

								if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ):
															copyfile(self.vocab_file			,     a   )

								return (out_vocab_file,)





