
"""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		       =							logging.getLogger(__name__)


_a		       =							'Hello world! cécé herlolip'


_a		       =							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 	__a     (		__lowerCamelCase,		__lowerCamelCase  ):
   UpperCAmelCase_					:					Optional[int]			     =    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,		)
   UpperCAmelCase_					:					Dict			     =    torch.load(__lowerCamelCase,		lambda __lowerCamelCase,		__lowerCamelCase  : storage  )
   UpperCAmelCase_					:					Tuple			     =    AbsSummarizer(__lowerCamelCase,		torch.device("cpu"  ),		__lowerCamelCase  )
   original.eval()

   UpperCAmelCase_					:					Tuple			     =    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"  )
   UpperCAmelCase_					:					Tuple			     =    BertTokenizer.from_pretrained("bert-base-uncased"  )

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

   UpperCAmelCase_					:					List[str]			     =    new_model(
       __lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  )[0]
   UpperCAmelCase_					:					List[Any]			     =    new_model.generator(__lowerCamelCase  )

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

   UpperCAmelCase_					:					Optional[int]			     =    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__":
    _a		       =							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		       =							parser.parse_args()

    convert_bertabs_checkpoints(
        args.bertabs_checkpoint_path,
        args.pytorch_dump_folder_path,
    )




"""simple docstring"""





from collections import namedtuple

_a		       =							namedtuple('from_to', 'from_ to')

_a		       =							{
    'cubicmeter': from_to(1, 1),
    'litre': from_to(0.001, 1_000),
    'kilolitre': from_to(1, 1),
    'gallon': from_to(0.0_0454, 264.172),
    'cubicyard': from_to(0.7_6455, 1.3_0795),
    'cubicfoot': from_to(0.028, 35.3147),
    'cup': from_to(0.0_0023_6588, 4226.75),
}







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if from_type not in METRIC_CONVERSION:
      raise ValueError(
          f"""Invalid 'from_type' value: {from_type!r}  Supported values are:\n"""
          + ", ".join(__lowerCamelCase  )  )
   if to_type not in METRIC_CONVERSION:
      raise ValueError(
          f"""Invalid 'to_type' value: {to_type!r}.  Supported values are:\n"""
          + ", ".join(__lowerCamelCase  )  )
   return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
    import doctest

    doctest.testmod()


