




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__     :     str		    =       logging.getLogger(__name__)


SCREAMING_SNAKE_CASE__     :     Optional[int]		    =       "Hello world! cécé herlolip"


SCREAMING_SNAKE_CASE__     :     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 lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							str:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						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=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_								=						torch.load(SCREAMING_SNAKE_CASE       ,    lambda SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE : storage )
			SCREAMING_SNAKE_CASE_								=						AbsSummarizer(SCREAMING_SNAKE_CASE       ,    torch.device('cpu' )       ,    SCREAMING_SNAKE_CASE )
			original.eval()

			SCREAMING_SNAKE_CASE_								=						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' )
			SCREAMING_SNAKE_CASE_								=						BertTokenizer.from_pretrained('bert-base-uncased' )

			# prepare the model inputs
			SCREAMING_SNAKE_CASE_								=						tokenizer.encode('This is sample éàalj\'-.' )
			encoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(SCREAMING_SNAKE_CASE )) )
			SCREAMING_SNAKE_CASE_								=						torch.tensor(SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			SCREAMING_SNAKE_CASE_								=						tokenizer.encode('This is sample 3 éàalj\'-.' )
			decoder_input_ids.extend([tokenizer.pad_token_id] * (5_12 - len(SCREAMING_SNAKE_CASE )) )
			SCREAMING_SNAKE_CASE_								=						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
			SCREAMING_SNAKE_CASE_								=						encoder_input_ids
			SCREAMING_SNAKE_CASE_								=						decoder_input_ids
			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						None
			SCREAMING_SNAKE_CASE_								=						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_								=						original(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )[0]
			SCREAMING_SNAKE_CASE_								=						original.generator(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE_								=						new_model(
			    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )[0]
			SCREAMING_SNAKE_CASE_								=						new_model.generator(SCREAMING_SNAKE_CASE )

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

			SCREAMING_SNAKE_CASE_								=						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__":
				SCREAMING_SNAKE_CASE__     :     Optional[Any]		    =       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__     :     Union[str, Any]		    =       parser.parse_args()

				convert_bertabs_checkpoints(
				    args.bertabs_checkpoint_path,
				    args.pytorch_dump_folder_path,
				)






def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

			if not isinstance(SCREAMING_SNAKE_CASE       ,    (list, tuple) ) or not all(
			    isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) for number in numbers ):
						raise ValueError('numbers must be an iterable of integers' )

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
