






"""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)
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 _lowerCAmelCase	(  __lowerCamelCase:Dict ,						__lowerCamelCase:int   ):

						'''simple docstring'''






						__magic_name__            =     BertAbsConfig(
						    temp_dir="." ,						finetune_bert=__lowerCamelCase ,						large=__lowerCamelCase ,						share_emb=__lowerCamelCase ,						use_bert_emb=__lowerCamelCase ,						encoder="bert" ,						max_pos=5_1_2 ,						enc_layers=6 ,						enc_hidden_size=5_1_2 ,						enc_heads=8 ,						enc_ff_size=5_1_2 ,						enc_dropout=0.2 ,						dec_layers=6 ,						dec_hidden_size=7_6_8 ,						dec_heads=8 ,						dec_ff_size=2_0_4_8 ,						dec_dropout=0.2 ,						)
						__magic_name__            =     torch.load(__lowerCamelCase ,						lambda __lowerCamelCase ,						__lowerCamelCase   : storage   )
						__magic_name__            =     AbsSummarizer(__lowerCamelCase ,						torch.device("cpu"   ) ,						__lowerCamelCase   )
						original.eval()

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

						# prepare the model inputs
						__magic_name__            =     tokenizer.encode("This is sample éàalj'-."   )
						encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__lowerCamelCase   ))   )
						__magic_name__            =     torch.tensor(__lowerCamelCase   ).unsqueeze(0   )
						__magic_name__            =     tokenizer.encode("This is sample 3 éàalj'-."   )
						decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__lowerCamelCase   ))   )
						__magic_name__            =     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
						__magic_name__            =     encoder_input_ids
						__magic_name__            =     decoder_input_ids
						__magic_name__            =     __magic_name__            =     None
						__magic_name__            =     None
						__magic_name__            =     __magic_name__            =     None
						__magic_name__            =     __magic_name__            =     None
						__magic_name__            =     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
						__magic_name__            =     original(__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )[0]
						__magic_name__            =     original.generator(__lowerCamelCase   )

						__magic_name__            =     new_model(
						    __lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase   )[0]
						__magic_name__            =     new_model.generator(__lowerCamelCase   )

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

						__magic_name__            =     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__":
			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,
			)











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:int = 2_0_0_0_0_0_0   ):

						'''simple docstring'''






						__magic_name__            =     [0 for i in range(n + 1   )]
						__magic_name__            =     1
						__magic_name__            =     1

						for i in range(2 ,						int(n**0.5   ) + 1   ):
												if primality_list[i] == 0:
																		for j in range(i * i ,						n + 1 ,						__lowerCamelCase   ):
																								__magic_name__            =     1
						__magic_name__            =     0
						for i in range(__lowerCamelCase   ):
												if primality_list[i] == 0:
																		sum_of_primes += i
						return sum_of_primes


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



