


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
       import datasets





class  lowerCAmelCase__(    UpperCamelCase_						):






    '''simple docstring'''





    @slow
    @require_torch
    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny'''       ,   '''prajjwal1/bert-tiny'''	)
       UpperCAmelCase_							:      Optional[Any]						=   BertTokenizer.from_pretrained('''bert-base-uncased'''	)

       UpperCAmelCase_							:      Any						=   bertabert.config.encoder.vocab_size
       UpperCAmelCase_							:      List[str]						=   tokenizer.sep_token_id
       UpperCAmelCase_							:      Any						=   tokenizer.cls_token_id
       UpperCAmelCase_							:      Tuple						=   128

       UpperCAmelCase_							:      str						=   datasets.load_dataset('''cnn_dailymail'''       ,   '''3.0.0'''       ,   split='''train[:1%]'''	)
       UpperCAmelCase_							:      List[str]						=   datasets.load_dataset('''cnn_dailymail'''       ,   '''3.0.0'''       ,   split='''validation[:1%]'''	)

       UpperCAmelCase_							:      Optional[int]						=   train_dataset.select(range(32	)	)
       UpperCAmelCase_							:      Tuple						=   val_dataset.select(range(16	)	)

       UpperCAmelCase_							:      List[str]						=   4

       def _map_to_encoder_decoder_inputs(__snake_case				:   Optional[int]	):
          # Tokenizer will automatically set [BOS] <text> [EOS]
          UpperCAmelCase_							:      Dict						=   tokenizer(batch['''article''']       ,   padding='''max_length'''       ,   truncation=__A       ,   max_length=512	)
          UpperCAmelCase_							:      List[str]						=   tokenizer(batch['''highlights''']       ,   padding='''max_length'''       ,   truncation=__A       ,   max_length=128	)
          UpperCAmelCase_							:      str						=   inputs.input_ids
          UpperCAmelCase_							:      int						=   inputs.attention_mask

          UpperCAmelCase_							:      Tuple						=   outputs.input_ids
          UpperCAmelCase_							:      Dict						=   outputs.input_ids.copy()
          UpperCAmelCase_							:      Optional[Any]						=   [
              [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["labels"]
          ]
          UpperCAmelCase_							:      List[Any]						=   outputs.attention_mask

          assert all(len(__A	) == 512 for x in inputs.input_ids	)
          assert all(len(__A	) == 128 for x in outputs.input_ids	)

          return batch

       def _compute_metrics(__snake_case				:   Union[str, Any]	):
          UpperCAmelCase_							:      Dict						=   pred.label_ids
          UpperCAmelCase_							:      Any						=   pred.predictions

          # all unnecessary tokens are removed
          UpperCAmelCase_							:      str						=   tokenizer.batch_decode(__A       ,   skip_special_tokens=__A	)
          UpperCAmelCase_							:      Any						=   tokenizer.batch_decode(__A       ,   skip_special_tokens=__A	)

          UpperCAmelCase_							:      str						=   sum([int(pred_str[i] == label_str[i]	) for i in range(len(__A	)	)]	) / len(__A	)

          return {"accuracy": accuracy}

       # map train dataset
       UpperCAmelCase_							:      Tuple						=   train_dataset.map(
           _map_to_encoder_decoder_inputs       ,   batched=__A       ,   batch_size=__A       ,   remove_columns=['''article''', '''highlights''']       ,   )
       train_dataset.set_format(
           type='''torch'''       ,   columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']       ,   )

       # same for validation dataset
       UpperCAmelCase_							:      int						=   val_dataset.map(
           _map_to_encoder_decoder_inputs       ,   batched=__A       ,   batch_size=__A       ,   remove_columns=['''article''', '''highlights''']       ,   )
       val_dataset.set_format(
           type='''torch'''       ,   columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels''']       ,   )

       UpperCAmelCase_							:      Dict						=   self.get_auto_remove_tmp_dir()

       UpperCAmelCase_							:      List[str]						=   SeqaSeqTrainingArguments(
           output_dir=__A       ,   per_device_train_batch_size=__A       ,   per_device_eval_batch_size=__A       ,   predict_with_generate=__A       ,   evaluation_strategy='''steps'''       ,   do_train=__A       ,   do_eval=__A       ,   warmup_steps=0       ,   eval_steps=2       ,   logging_steps=2       ,   )

       # instantiate trainer
       UpperCAmelCase_							:      Dict						=   SeqaSeqTrainer(
           model=__A       ,   args=__A       ,   compute_metrics=_compute_metrics       ,   train_dataset=__A       ,   eval_dataset=__A       ,   tokenizer=__A       ,   )

       # start training
       trainer.train()



import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def      snake_case_						( __lowercase			,       __lowercase       ):
   # Load checkpoint
   UpperCAmelCase_							:      Tuple						=   torch.load(__lowercase			,       map_location='''cpu'''       )

   UpperCAmelCase_							:      Optional[int]						=   chkpt['''model''']

   # We have the base model one level deeper than the original XLM repository
   UpperCAmelCase_							:      str						=   {}
   for k, v in state_dict.items():
      if "pred_layer" in k:
         UpperCAmelCase_							:      Tuple						=   v
      else:
         UpperCAmelCase_							:      Union[str, Any]						=   v

   UpperCAmelCase_							:      int						=   chkpt['''params''']
   UpperCAmelCase_							:      Union[str, Any]						=   {n: v for n, v in config.items() if not isinstance(__lowercase			,       (torch.FloatTensor, numpy.ndarray)       )}

   UpperCAmelCase_							:      int						=   chkpt['''dico_word2id''']
   UpperCAmelCase_							:      List[Any]						=   {s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 1_3 else s.replace('''@@'''			,       ''''''       ): i for s, i in vocab.items()}

   # Save pytorch-model
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
   UpperCAmelCase_							:      Tuple						=   pytorch_dump_folder_path + '''/''' + CONFIG_NAME
   UpperCAmelCase_							:      Dict						=   pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

   print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
   torch.save(__lowercase			,       __lowercase       )

   print(F'''Save configuration file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )

   print(F'''Save vocab file to {pytorch_config_dump_path}'''       )
   with open(__lowercase			,       '''w'''			,       encoding='''utf-8'''       ) as f:
      f.write(json.dumps(__lowercase			,       indent=2       ) + '''\n'''       )


if __name__ == "__main__":
       __UpperCamelCase				:  str      					=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--xlm_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.'
       )
       __UpperCamelCase				:  Dict      					=							parser.parse_args()
       convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)