




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__ :   str						=							logging.getLogger(__name__)


UpperCamelCase__ :   Union[str, Any]						=							'''Hello world! cécé herlolip'''


UpperCamelCase__ :   Tuple						=							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						(							lowerCamelCase_ :     Dict  ,							lowerCamelCase_ :     str  )       ->  str:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Dict					  =      BertAbsConfig(
			    temp_dir='.'  ,							finetune_bert=lowerCamelCase_  ,							large=lowerCamelCase_  ,							share_emb=lowerCamelCase_  ,							use_bert_emb=lowerCamelCase_  ,							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  ,							)
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      torch.load(lowerCamelCase_  ,							lambda lowerCamelCase_  ,							lowerCamelCase_  : storage  )
			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      AbsSummarizer(lowerCamelCase_  ,							torch.device('cpu'  )  ,							lowerCamelCase_  )
			original.eval()

			SCREAMING_SNAKE_CASE_   : Dict					  =      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'  )
			SCREAMING_SNAKE_CASE_   : List[Any]					  =      BertTokenizer.from_pretrained('bert-base-uncased'  )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      tokenizer.encode('This is sample éàalj\'-.'  )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_  ))  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      torch.tensor(lowerCamelCase_  ).unsqueeze(0  )
			SCREAMING_SNAKE_CASE_   : Tuple					  =      tokenizer.encode('This is sample 3 éàalj\'-.'  )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(lowerCamelCase_  ))  )
			SCREAMING_SNAKE_CASE_   : List[str]					  =      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
			SCREAMING_SNAKE_CASE_   : Dict					  =      encoder_input_ids
			SCREAMING_SNAKE_CASE_   : Dict					  =      decoder_input_ids
			SCREAMING_SNAKE_CASE_   : Any					  =      None
			SCREAMING_SNAKE_CASE_   : int					  =      None
			SCREAMING_SNAKE_CASE_   : Any					  =      None
			SCREAMING_SNAKE_CASE_   : int					  =      None
			SCREAMING_SNAKE_CASE_   : Dict					  =      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
			SCREAMING_SNAKE_CASE_   : str					  =      original(lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )[0]
			SCREAMING_SNAKE_CASE_   : int					  =      original.generator(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      new_model(
			    lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  ,							lowerCamelCase_  )[0]
			SCREAMING_SNAKE_CASE_   : str					  =      new_model.generator(lowerCamelCase_  )

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

			SCREAMING_SNAKE_CASE_   : 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__ :   Optional[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__ :   Any						=							parser.parse_args()

						convert_bertabs_checkpoints(
						    args.bertabs_checkpoint_path,
						    args.pytorch_dump_folder_path,
						)









from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return math.sqrt(sum(pow(a - b  ,							2  ) for a, b in zip(lowerCamelCase_  ,							lowerCamelCase_  )  )  )





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  list[list[list[float] | float]]:







			"""simple docstring"""





			if dataset.ndim != value_array.ndim:
						SCREAMING_SNAKE_CASE_   : Optional[int]					  =      (
						    'Wrong input data\'s dimensions... '
						    F'dataset : {dataset.ndim}, value_array : {value_array.ndim}'
						)
						raise ValueError(lowerCamelCase_  )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									SCREAMING_SNAKE_CASE_   : List[Any]					  =      (
									    'Wrong input data\'s shape... '
									    F'dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'
									)
									raise ValueError(lowerCamelCase_  )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError('Wrong shape'  )

			if dataset.dtype != value_array.dtype:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      (
						    'Input data have different datatype... '
						    F'dataset : {dataset.dtype}, value_array : {value_array.dtype}'
						)
						raise TypeError(lowerCamelCase_  )

			SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      []

			for value in value_array:
						SCREAMING_SNAKE_CASE_   : str					  =      euclidean(lowerCamelCase_  ,							dataset[0]  )
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      dataset[0].tolist()

						for dataset_value in dataset[1:]:
									SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      euclidean(lowerCamelCase_  ,							lowerCamelCase_  )

									if dist > temp_dist:
												SCREAMING_SNAKE_CASE_   : Optional[int]					  =      temp_dist
												SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      dataset_value.tolist()

						answer.append([vector, dist]  )

			return answer





def        __UpperCAmelCase						(							lowerCamelCase_ :     np.ndarray  ,							lowerCamelCase_ :     np.ndarray  )       ->  float:







			"""simple docstring"""





			return np.dot(lowerCamelCase_  ,							lowerCamelCase_  ) / (norm(lowerCamelCase_  ) * norm(lowerCamelCase_  ))


if __name__ == "__main__":
						import doctest

						doctest.testmod()



