

'''simple docstring'''






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 	_A  (					A__   ):






 """simple docstring"""



 if "model" in orig_key:
  __lowercase													=				orig_key.replace('''model.'''  ,       ''''''   )
 if "norm1" in orig_key:
  __lowercase													=				orig_key.replace('''norm1'''  ,       '''attention.output.LayerNorm'''   )
 if "norm2" in orig_key:
  __lowercase													=				orig_key.replace('''norm2'''  ,       '''output.LayerNorm'''   )
 if "norm" in orig_key:
  __lowercase													=				orig_key.replace('''norm'''  ,       '''LayerNorm'''   )
 if "transformer" in orig_key:
  __lowercase													=				orig_key.split('''.'''   )[0].split('''_'''   )[-1]
  __lowercase													=				orig_key.replace(F"transformer_{layer_num}"  ,       F"encoder.layer.{layer_num}"   )
 if "mha.attn" in orig_key:
  __lowercase													=				orig_key.replace('''mha.attn'''  ,       '''attention.self'''   )
 if "mha" in orig_key:
  __lowercase													=				orig_key.replace('''mha'''  ,       '''attention'''   )
 if "W_q" in orig_key:
  __lowercase													=				orig_key.replace('''W_q'''  ,       '''self.query'''   )
 if "W_k" in orig_key:
  __lowercase													=				orig_key.replace('''W_k'''  ,       '''self.key'''   )
 if "W_v" in orig_key:
  __lowercase													=				orig_key.replace('''W_v'''  ,       '''self.value'''   )
 if "ff1" in orig_key:
  __lowercase													=				orig_key.replace('''ff1'''  ,       '''intermediate.dense'''   )
 if "ff2" in orig_key:
  __lowercase													=				orig_key.replace('''ff2'''  ,       '''output.dense'''   )
 if "ff" in orig_key:
  __lowercase													=				orig_key.replace('''ff'''  ,       '''output.dense'''   )
 if "mlm_class" in orig_key:
  __lowercase													=				orig_key.replace('''mlm.mlm_class'''  ,       '''cls.predictions.decoder'''   )
 if "mlm" in orig_key:
  __lowercase													=				orig_key.replace('''mlm'''  ,       '''cls.predictions.transform'''   )
 if "cls" not in orig_key:
  __lowercase													=				'''yoso.''' + orig_key

 return orig_key


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 for key in orig_state_dict.copy().keys():
  __lowercase													=				orig_state_dict.pop(A__   )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   __lowercase													=				val

 __lowercase													=				orig_state_dict['''cls.predictions.decoder.bias''']
 __lowercase													=				torch.arange(A__   ).expand((1, -1)   ) + 2

 return orig_state_dict


def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				torch.load(A__  ,       map_location='''cpu'''   )['''model_state_dict''']
 __lowercase													=				YosoConfig.from_json_file(A__   )
 __lowercase													=				YosoForMaskedLM(A__   )

 __lowercase													=				convert_checkpoint_helper(config.max_position_embeddings  ,       A__   )

 print(model.load_state_dict(A__   )   )
 model.eval()
 model.save_pretrained(A__   )

 print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to YOSO pytorch checkpoint.'''
 )
 parser.add_argument(
     '''--config_file''',
     default=None,
     type=str,
     required=True,
     help='''The json file for YOSO model config.''',
 )
 parser.add_argument(
     '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
 )
 lowerCAmelCase__      		=			parser.parse_args()
 convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)




'''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)
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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				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  ,       )
 __lowercase													=				torch.load(A__  ,       lambda A__  ,       A__   : storage   )
 __lowercase													=				AbsSummarizer(A__  ,       torch.device('''cpu'''   )  ,       A__   )
 original.eval()

 __lowercase													=				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													=				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(A__   ))   )
 __lowercase													=				torch.tensor(A__   ).unsqueeze(0   )
 __lowercase													=				tokenizer.encode('''This is sample 3 éàalj\'-.'''   )
 decoder_input_ids.extend([tokenizer.pad_token_id] * (512 - len(A__   ))   )
 __lowercase													=				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													=				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(A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__   )[0]
 __lowercase													=				original.generator(A__   )

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

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

 __lowercase													=				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__":
 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,
 )

