






'''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)
_A     :      List[Any]   					=logging.getLogger(__name__)


_A     :      int   					='''Hello world! cécé herlolip'''


_A     :      Any   					=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 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase							)				->	Union[str, Any]:
							lowerCamelCase__ :     List[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=2048    ,      dec_dropout=0.2    ,      )
							lowerCamelCase__ :     Tuple	  =	torch.load(UpperCamelCase    ,      lambda UpperCamelCase    ,      UpperCamelCase							: storage							)
							lowerCamelCase__ :     Optional[Any]	  =	AbsSummarizer(UpperCamelCase    ,      torch.device("""cpu"""							)    ,      UpperCamelCase							)
							original.eval()

							lowerCamelCase__ :     Dict	  =	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"""							)
							lowerCamelCase__ :     str	  =	BertTokenizer.from_pretrained("""bert-base-uncased"""							)

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

							lowerCamelCase__ :     List[Any]	  =	new_model(
							    UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase    ,      UpperCamelCase							)[0]
							lowerCamelCase__ :     int	  =	new_model.generator(UpperCamelCase							)

							lowerCamelCase__ :     Optional[Any]	  =	torch.max(torch.abs(output_converted_model - output_original_model							)							).item()
							print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase							)							)
							lowerCamelCase__ :     int	  =	torch.max(torch.abs(output_converted_generator - output_original_generator							)							).item()
							print("""Maximum absolute difference beween weights: {:.2f}""".format(UpperCamelCase							)							)

							lowerCamelCase__ :     List[Any]	  =	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__":
			_A     :      Dict   					=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.''',
			)
			_A     :      int   					=parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)









'''simple docstring'''







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

_A     :      Optional[Any]   					=typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
_A     :      Optional[int]   					=typing.Union[np.floataa, int, float]  # noqa: UP007





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return np.sqrt(np.sum((np.asarray(UpperCamelCase				) - np.asarray(UpperCamelCase				)) ** 2				)				)





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	VectorOut:
							return sum((va - va) ** 2 for va, va in zip(UpperCamelCase    ,      UpperCamelCase				)				) ** (1 / 2)


if __name__ == "__main__":





			def 			SCREAMING_SNAKE_CASE_	()				->	None:
										from timeit import timeit

										print("""Without Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)
										print("""With Numpy"""				)
										print(
										    timeit(
										        """euclidean_distance([1, 2, 3], [4, 5, 6])"""    ,      number=10000    ,      globals=globals()    ,      )				)



			benchmark()

