"""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)
__lowerCAmelCase     :				List[Any]    				=    logging.getLogger(__name__)


__lowerCAmelCase     :				List[Any]    				=    "Hello world! cécé herlolip"


__lowerCAmelCase     :				int    				=    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__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		lowerCAmelCase__   = BertAbsConfig(
		    temp_dir="""."""      ,	finetune_bert=lowerCamelCase__      ,	large=lowerCamelCase__      ,	share_emb=lowerCamelCase__      ,	use_bert_emb=lowerCamelCase__      ,	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__   = torch.load(lowerCamelCase__      ,	lambda lowerCamelCase__      ,	lowerCamelCase__  : storage  )
		lowerCAmelCase__   = AbsSummarizer(lowerCamelCase__      ,	torch.device("""cpu"""  )      ,	lowerCamelCase__  )
		original.eval()

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

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

		lowerCAmelCase__   = new_model(
		    lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__      ,	lowerCamelCase__  )[0]
		lowerCAmelCase__   = new_model.generator(lowerCamelCase__  )

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

		lowerCAmelCase__   = 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__":
			__lowerCAmelCase     :				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.",
			)
			__lowerCAmelCase     :				Tuple    				=    parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)





"""simple docstring"""




def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		return abs(lowerCamelCase__  ) if a == 0 else greatest_common_divisor(b % a      ,	lowerCamelCase__  )







def 				_UpperCAmelCase		(							lowerCamelCase__      ,	lowerCamelCase__  ):





		"""simple docstring"""




		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				lowerCAmelCase__ ,       lowerCAmelCase__   = y, x % y
		return abs(lowerCamelCase__  )







def 				_UpperCAmelCase		(							):





		"""simple docstring"""




		try:
				lowerCAmelCase__   = input("""Enter two integers separated by comma (,): """  ).split(""","""  )
				lowerCAmelCase__   = int(nums[0]  )
				lowerCAmelCase__   = int(nums[1]  )
				print(
				    f"""greatest_common_divisor({num_a}, {num_a}) = """
				    f"""{greatest_common_divisor(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
				print(f"""By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(lowerCamelCase__      ,	lowerCamelCase__  )}"""  )
		except (IndexError, UnboundLocalError, ValueError):
				print("""Wrong input"""  )


if __name__ == "__main__":
			main()




