
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					(    __lowerCamelCase     : Union[str, Any]				,					__lowerCamelCase     : List[Any]    ):
       snake_case       :   List[Any]							=      BertAbsConfig(
           temp_dir="."				,					finetune_bert=UpperCamelCase_				,					large=UpperCamelCase_				,					share_emb=UpperCamelCase_				,					use_bert_emb=UpperCamelCase_				,					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				,					)
       snake_case       :   Any							=      torch.load(UpperCamelCase_				,					lambda __lowerCamelCase				,					__lowerCamelCase    : storage    )
       snake_case       :   Optional[int]							=      AbsSummarizer(UpperCamelCase_				,					torch.device("cpu"    )				,					UpperCamelCase_    )
       original.eval()

       snake_case       :   Any							=      BertAbsSummarizer(UpperCamelCase_				,					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"    )
       snake_case       :   List[Any]							=      BertTokenizer.from_pretrained("bert-base-uncased"    )

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

       snake_case       :   List[str]							=      new_model(
           UpperCamelCase_				,					UpperCamelCase_				,					UpperCamelCase_				,					UpperCamelCase_				,					UpperCamelCase_    )[0]
       snake_case       :   str							=      new_model.generator(UpperCamelCase_    )

       snake_case       :   Optional[int]							=      torch.max(torch.abs(output_converted_model - output_original_model    )    ).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(UpperCamelCase_    )    )
       snake_case       :   Tuple							=      torch.max(torch.abs(output_converted_generator - output_original_generator    )    ).item()
       print("Maximum absolute difference beween weights: {:.2f}".format(UpperCamelCase_    )    )

       snake_case       :   Optional[Any]							=      torch.allclose(UpperCamelCase_				,					UpperCamelCase_				,					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"""





def   _A  (  UpperCamelCase_     :       Any)	->						List[str]:
   '''simple docstring'''


   __lowercase		,__lowercase								=						[], []
   while len(UpperCamelCase_) > 1:
      __lowercase		,__lowercase								=						min(UpperCamelCase_), max(UpperCamelCase_)
      start.append(UpperCamelCase_)
      end.append(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
      collection.remove(UpperCamelCase_)
   end.reverse()
   return start + collection + end


if __name__ == "__main__":
   _a       =							input('Enter numbers separated by a comma:\n').strip()
   _a       =							[int(item) for item in user_input.split(',')]
   print(*merge_sort(unsorted), sep=',')




