



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__ =						logging.getLogger(__name__)


UpperCamelCase__ =						"Hello world! cécé herlolip"


UpperCamelCase__ =						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__    (    lowercase__						,       lowercase__   ) ->      Any:
						__lowercase					=				BertAbsConfig(
						    temp_dir="""."""						,       finetune_bert=lowercase__						,       large=lowercase__						,       share_emb=lowercase__						,       use_bert_emb=lowercase__						,       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						,       )
						__lowercase					=				torch.load(lowercase__						,       lambda lowercase__						,       lowercase__   : storage   )
						__lowercase					=				AbsSummarizer(lowercase__						,       torch.device("""cpu"""   )						,       lowercase__   )
						original.eval()

						__lowercase					=				BertAbsSummarizer(lowercase__						,       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"""   )
						__lowercase					=				BertTokenizer.from_pretrained("""bert-base-uncased"""   )

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

						__lowercase					=				new_model(
						    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__						,       lowercase__   )[0]
						__lowercase					=				new_model.generator(lowercase__   )

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

						__lowercase					=				torch.allclose(lowercase__						,       lowercase__						,       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__ =						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__ =						parser.parse_args()

			convert_bertabs_checkpoints(
			    args.bertabs_checkpoint_path,
			    args.pytorch_dump_folder_path,
			)










UpperCamelCase__ =						{
    "joule": 1.0,
    "kilojoule": 10_00,
    "megajoule": 1_00_00_00,
    "gigajoule": 10_00_00_00_00,
    "wattsecond": 1.0,
    "watthour": 36_00,
    "kilowatthour": 3_60_00_00,
    "newtonmeter": 1.0,
    "calorie_nutr": 41_86.8,
    "kilocalorie_nutr": 4_18_68_00.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 10_55.0_55_85,
    "footpound": 1.355_818,
}






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__   ) ->      float:
						if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
												__lowercase					=				(
												    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
												    F"Valid values are: {', '.join(lowercase__   )}"
												)
												raise ValueError(lowercase__   )
						return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





