






"""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         =							logging.getLogger(__name__)


_lowerCAmelCase         =							"Hello world! cécé herlolip"


_lowerCAmelCase         =							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	(		snake_case__   ,   snake_case__					):
	A_		:						Any	    =   BertAbsConfig(
	    temp_dir="""."""   ,   finetune_bert=snake_case__   ,   large=snake_case__   ,   share_emb=snake_case__   ,   use_bert_emb=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=2_048   ,   dec_dropout=0.2   ,   )
	A_		:						str	    =   torch.load(snake_case__   ,   lambda snake_case__   ,   snake_case__					: storage					)
	A_		:						Optional[int]	    =   AbsSummarizer(snake_case__   ,   torch.device("""cpu"""					)   ,   snake_case__					)
	original.eval()

	A_		:						int	    =   BertAbsSummarizer(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_		:						Dict	    =   BertTokenizer.from_pretrained("""bert-base-uncased"""					)

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

	A_		:						Tuple	    =   new_model(
	    snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__					)[0]
	A_		:						int	    =   new_model.generator(snake_case__					)

	A_		:						str	    =   torch.max(torch.abs(output_converted_model - output_original_model					)					).item()
	print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__					)					)
	A_		:						List[Any]	    =   torch.max(torch.abs(output_converted_generator - output_original_generator					)					).item()
	print("""Maximum absolute difference beween weights: {:.2f}""".format(snake_case__					)					)

	A_		:						Tuple	    =   torch.allclose(snake_case__   ,   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__":
							_lowerCAmelCase         =							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         =							parser.parse_args()

							convert_bertabs_checkpoints(
							    args.bertabs_checkpoint_path,
							    args.pytorch_dump_folder_path,
							)









"""simple docstring"""
import math






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * power_factor






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__					):
	if (
	    not isinstance(snake_case__   ,   (int, float)					)
	    or power_factor < -1
	    or power_factor > 1
	):
		raise ValueError("""power_factor must be a valid float value between -1 and 1."""					)
	return apparent_power * math.sqrt(1 - power_factor**2					)


if __name__ == "__main__":
							import doctest

							doctest.testmod()

