






'''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)
SCREAMING_SNAKE_CASE							=						logging.getLogger(__name__)


SCREAMING_SNAKE_CASE							=						'Hello world! cécé herlolip'


SCREAMING_SNAKE_CASE							=						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 				lowercase_					(     __A			:							int       ,   __A			:							Any      )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=BertAbsConfig(
     temp_dir='''.'''       ,   finetune_bert=__A       ,   large=__A       ,   share_emb=__A       ,   use_bert_emb=__A       ,   encoder='''bert'''       ,   max_pos=5_1_2       ,   enc_layers=6       ,   enc_hidden_size=5_1_2       ,   enc_heads=8       ,   enc_ff_size=5_1_2       ,   enc_dropout=0.2       ,   dec_layers=6       ,   dec_hidden_size=7_6_8       ,   dec_heads=8       ,   dec_ff_size=2_0_4_8       ,   dec_dropout=0.2       ,   )
 lowercase       : Any						=torch.load(__A       ,   lambda __A       ,   __A      : storage      )
 lowercase       : Union[str, Any]						=AbsSummarizer(__A       ,   torch.device('''cpu'''      )       ,   __A      )
 original.eval()

 lowercase       : Tuple						=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'''      )
 lowercase       : Tuple						=BertTokenizer.from_pretrained('''bert-base-uncased'''      )

 # prepare the model inputs
 lowercase       : Tuple						=tokenizer.encode('''This is sample éàalj\'-.'''      )
 encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__A      ))      )
 lowercase       : Dict						=torch.tensor(__A      ).unsqueeze(0      )
 lowercase       : Optional[int]						=tokenizer.encode('''This is sample 3 éàalj\'-.'''      )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(__A      ))      )
 lowercase       : Any						=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
 lowercase       : Any						=encoder_input_ids
 lowercase       : int						=decoder_input_ids
 lowercase       : Any						=None
 lowercase       : Any						=None
 lowercase       : int						=None
 lowercase       : List[str]						=None
 lowercase       : str						=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       : List[Any]						=original(__A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A      )[0]
 lowercase       : str						=original.generator(__A      )

 lowercase       : Optional[int]						=new_model(
     __A       ,   __A       ,   __A       ,   __A       ,   __A      )[0]
 lowercase       : Union[str, Any]						=new_model.generator(__A      )

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

 lowercase       : Optional[Any]						=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__":
      SCREAMING_SNAKE_CASE							=						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.',
      )
      SCREAMING_SNAKE_CASE							=						parser.parse_args()

      convert_bertabs_checkpoints(
          args.bertabs_checkpoint_path,
          args.pytorch_dump_folder_path,
      )











'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE							=						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							=						{
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}
class UpperCAmelCase_       (     __A					,							__A					):




      """simple docstring"""




      UpperCamelCase_             =      '''bit'''
      UpperCamelCase_             =      ['''preactivation''', '''bottleneck''']
      UpperCamelCase_             =      ['''SAME''', '''VALID''']






      def __init__(			self      :		Any  ,   UpperCAmelCase      :		Optional[int]=3  ,   UpperCAmelCase      :		Any=64  ,   UpperCAmelCase      :		List[str]=[256, 512, 1024, 2048]  ,   UpperCAmelCase      :		Dict=[3, 4, 6, 3]  ,   UpperCAmelCase      :		List[Any]="preactivation"  ,   UpperCAmelCase      :		Tuple="relu"  ,   UpperCAmelCase      :		Tuple=None  ,   UpperCAmelCase      :		Tuple=32  ,   UpperCAmelCase      :		Optional[int]=0.0  ,   UpperCAmelCase      :		Union[str, Any]=False  ,   UpperCAmelCase      :		Dict=32  ,   UpperCAmelCase      :		Any=1  ,   UpperCAmelCase      :		str=None  ,   UpperCAmelCase      :		Union[str, Any]=None  ,   **UpperCAmelCase      :		str  ,   )	->	Dict:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       if layer_type not in self.layer_types:
        raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
       if global_padding is not None:
        if global_padding.upper() in self.supported_padding:
         lowercase       : Any						=global_padding.upper()
        else:
         raise ValueError(f'Padding strategy {global_padding} not supported'		)
       lowercase       : List[Any]						=num_channels
       lowercase       : List[str]						=embedding_size
       lowercase       : Union[str, Any]						=hidden_sizes
       lowercase       : Union[str, Any]						=depths
       lowercase       : Optional[int]						=layer_type
       lowercase       : Optional[Any]						=hidden_act
       lowercase       : List[str]						=global_padding
       lowercase       : Dict						=num_groups
       lowercase       : Optional[Any]						=drop_path_rate
       lowercase       : Union[str, Any]						=embedding_dynamic_padding
       lowercase       : Any						=output_stride
       lowercase       : Union[str, Any]						=width_factor

       lowercase       : Union[str, Any]						=['''stem'''] + [f'stage{idx}' for idx in range(1  ,   len(UpperCAmelCase		) + 1		)]
       lowercase	,		lowercase       : List[str]						=get_aligned_output_features_output_indices(
           out_features=UpperCAmelCase  ,   out_indices=UpperCAmelCase  ,   stage_names=self.stage_names		)



