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


SCREAMING_SNAKE_CASE									=						'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE									=						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     ,__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Union[str, Any]							=							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=2_048     ,dec_dropout=0.2     ,)
	_lowercase :    Any							=							torch.load(__UpperCAmelCase     ,lambda __UpperCAmelCase     ,__UpperCAmelCase			: storage			)
	_lowercase :    Any							=							AbsSummarizer(__UpperCAmelCase     ,torch.device('cpu'			)     ,__UpperCAmelCase			)
	original.eval()

	_lowercase :    Optional[int]							=							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 :    Tuple							=							BertTokenizer.from_pretrained('bert-base-uncased'			)

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

	_lowercase :    Tuple							=							new_model(
	    __UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase     ,__UpperCAmelCase			)[0]
	_lowercase :    Dict							=							new_model.generator(__UpperCAmelCase			)

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

	_lowercase :    int							=							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__":
						SCREAMING_SNAKE_CASE									=						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.',
						)
						SCREAMING_SNAKE_CASE									=						parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)


"""simple docstring"""







from __future__ import annotations


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    Any							=							[True] * limit
	_lowercase :    Union[str, Any]							=							False
	_lowercase :    Any							=							False
	_lowercase :    List[Any]							=							True

	for i in range(3     ,int(limit**0.5 + 1			)     ,2			):
		_lowercase :    Optional[Any]							=							i * 2
		while index < limit:
			_lowercase :    int							=							False
			_lowercase :    Tuple							=							index + i

	_lowercase :    Optional[Any]							=							[2]

	for i in range(3     ,__UpperCAmelCase     ,2			):
		if is_prime[i]:
			primes.append(__UpperCAmelCase			)

	return primes


def      __lowerCAmelCase(		__UpperCAmelCase = 1_000_000			):






	"""simple docstring"""





	_lowercase :    Optional[Any]							=							prime_sieve(__UpperCAmelCase			)
	_lowercase :    int							=							0
	_lowercase :    List[Any]							=							0

	for i in range(len(__UpperCAmelCase			)			):
		for j in range(i + length     ,len(__UpperCAmelCase			)			):
			_lowercase :    List[Any]							=							sum(primes[i:j]			)
			if sol >= ceiling:
				break

			if sol in primes:
				_lowercase :    Optional[int]							=							j - i
				_lowercase :    Tuple							=							sol

	return largest


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



