


'''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__  			=				logging.getLogger(__name__)


UpperCAmelCase__  			=				'''Hello world! cécé herlolip'''


UpperCAmelCase__  			=				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__(				_SCREAMING_SNAKE_CASE					: List[str],_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	BertAbsConfig(
			    temp_dir='.',finetune_bert=_SCREAMING_SNAKE_CASE,large=_SCREAMING_SNAKE_CASE,share_emb=_SCREAMING_SNAKE_CASE,use_bert_emb=_SCREAMING_SNAKE_CASE,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,)
			__A=	torch.load(_SCREAMING_SNAKE_CASE,lambda _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE : storage )
			__A=	AbsSummarizer(_SCREAMING_SNAKE_CASE,torch.device('cpu' ),_SCREAMING_SNAKE_CASE )
			original.eval()

			__A=	BertAbsSummarizer(_SCREAMING_SNAKE_CASE,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' )
			__A=	BertTokenizer.from_pretrained('bert-base-uncased' )

			# prepare the model inputs
			__A=	tokenizer.encode('This is sample éàalj\'-.' )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_SCREAMING_SNAKE_CASE )) )
			__A=	torch.tensor(_SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			__A=	tokenizer.encode('This is sample 3 éàalj\'-.' )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(_SCREAMING_SNAKE_CASE )) )
			__A=	torch.tensor(_SCREAMING_SNAKE_CASE ).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
			__A=	encoder_input_ids
			__A=	decoder_input_ids
			__A=	__A=	None
			__A=	None
			__A=	__A=	None
			__A=	__A=	None
			__A=	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
			__A=	original(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )[0]
			__A=	original.generator(_SCREAMING_SNAKE_CASE )

			__A=	new_model(
			    _SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )[0]
			__A=	new_model.generator(_SCREAMING_SNAKE_CASE )

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

			__A=	torch.allclose(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,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__  			=				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__  			=				parser.parse_args()

					convert_bertabs_checkpoints(
					    args.bertabs_checkpoint_path,
					    args.pytorch_dump_folder_path,
					)







'''simple docstring'''





def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if exponent == 1:
						return base
			if exponent % 2 == 0:
						__A=	_modexpt(_SCREAMING_SNAKE_CASE,exponent // 2,_SCREAMING_SNAKE_CASE ) % modulo_value
						return (x * x) % modulo_value
			else:
						return (base * _modexpt(_SCREAMING_SNAKE_CASE,exponent - 1,_SCREAMING_SNAKE_CASE )) % modulo_value






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 1777,_SCREAMING_SNAKE_CASE					: int = 1855,_SCREAMING_SNAKE_CASE					: int = 8 ):






			"""simple docstring"""




			__A=	base
			for _ in range(1,_SCREAMING_SNAKE_CASE ):
						__A=	_modexpt(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE,10**digits )

			return result


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



