

'''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					:			List[str]							=    logging.getLogger(__name__)


_a					:			Any							=    'Hello world! cécé herlolip'


_a					:			List[str]							=    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    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple							, SCREAMING_SNAKE_CASE   : List[Any]    ):
  UpperCAmelCase    				=						BertAbsConfig(
      temp_dir='.'							, finetune_bert=__A							, large=__A							, share_emb=__A							, use_bert_emb=__A							, 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    				=						torch.load(__A							, lambda SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    : storage    )
  UpperCAmelCase    				=						AbsSummarizer(__A							, torch.device('cpu'    )							, __A    )
  original.eval()

  UpperCAmelCase    				=						BertAbsSummarizer(__A							, 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    				=						BertTokenizer.from_pretrained('bert-base-uncased'    )

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

  UpperCAmelCase    				=						new_model(
      __A							, __A							, __A							, __A							, __A    )[0]
  UpperCAmelCase    				=						new_model.generator(__A    )

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

  UpperCAmelCase    				=						torch.allclose(__A							, __A							, 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					:			Union[str, Any]							=    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					:			List[Any]							=    parser.parse_args()

  convert_bertabs_checkpoints(
      args.bertabs_checkpoint_path,
      args.pytorch_dump_folder_path,
  )










'''simple docstring'''







import functools





def 		_UpperCAmelCase (				__A				: list[int]    ,		__A				: list[int]   ):

				# Validation
				if not isinstance(__A    ,		__A   ) or not all(isinstance(__A    ,		__A   ) for day in days   ):
								raise ValueError('''The parameter days should be a list of integers'''   )

				if len(__A   ) != 3 or not all(isinstance(__A    ,		__A   ) for cost in costs   ):
								raise ValueError('''The parameter costs should be a list of three integers'''   )

				if len(__A   ) == 0:
								return 0

				if min(__A   ) <= 0:
								raise ValueError('''All days elements should be greater than 0'''   )

				if max(__A   ) >= 3_66:
								raise ValueError('''All days elements should be less than 366'''   )

				a_		:	List[Any]									=   set(__A   )

				@functools.cache
				def dynamic_programming(__A				: int   ) -> int:
								if index > 3_65:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1   )

								return min(
								    costs[0] + dynamic_programming(index + 1   )    ,		costs[1] + dynamic_programming(index + 7   )    ,		costs[2] + dynamic_programming(index + 30   )    ,		)

				return dynamic_programming(1   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




