


"""simple docstring"""






import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__snake_case		=       logging.getLogger(__name__)


@dataclass
class     __lowerCamelCase  :

     '''simple docstring'''
     A_       :	str    =		field(
         metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
     A_       :	Optional[str]    =		field(
         default=lowercase_  ,					metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
     A_       :	Optional[str]    =		field(
         default=lowercase_  ,					metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
     A_       :	Optional[str]    =		field(
         default=lowercase_  ,					metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}  ,					)
     A_       :	bool    =		field(default=lowercase_  ,					metadata={'help': 'Whether tp freeze the encoder.'}				)
     A_       :	bool    =		field(default=lowercase_  ,					metadata={'help': 'Whether  to freeze the embeddings.'}				)







@dataclass
class     __lowerCamelCase  :

     '''simple docstring'''
     A_       :	str    =		field(
         metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
     A_       :	Optional[str]    =		field(
         default='summarization'  ,					metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}  ,					)
     A_       :	Optional[int]    =		field(
         default=1_024  ,					metadata={
             'help': (
                 'The maximum total input sequence length after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }  ,					)
     A_       :	Optional[int]    =		field(
         default=128  ,					metadata={
             'help': (
                 'The maximum total sequence length for target text after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }  ,					)
     A_       :	Optional[int]    =		field(
         default=142  ,					metadata={
             'help': (
                 'The maximum total sequence length for validation target text after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded. '
                 'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
                 'during ``evaluate`` and ``predict``.'
             )
         }  ,					)
     A_       :	Optional[int]    =		field(
         default=142  ,					metadata={
             'help': (
                 'The maximum total sequence length for test target text after tokenization. Sequences longer '
                 'than this will be truncated, sequences shorter will be padded.'
             )
         }  ,					)
     A_       :	Optional[int]    =		field(default=-1  ,					metadata={'help': '# training examples. -1 means use all.'}				)
     A_       :	Optional[int]    =		field(default=-1  ,					metadata={'help': '# validation examples. -1 means use all.'}				)
     A_       :	Optional[int]    =		field(default=-1  ,					metadata={'help': '# test examples. -1 means use all.'}				)
     A_       :	Optional[str]    =		field(default=lowercase_  ,					metadata={'help': 'Source language id for translation.'}				)
     A_       :	Optional[str]    =		field(default=lowercase_  ,					metadata={'help': 'Target language id for translation.'}				)
     A_       :	Optional[int]    =		field(default=lowercase_  ,					metadata={'help': '# num_beams to use for evaluation.'}				)
     A_       :	bool    =		field(
         default=lowercase_  ,					metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}  ,					)







def       A_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					str       ):






     """simple docstring"""

     logger.info(f'***** {split} metrics *****'       )
     for key in sorted(metrics.keys()       ):
          logger.info(f'  {key} = {metrics[key]}'       )
     save_json(_lowerCAmelCase,     os.path.join(_lowerCAmelCase,     f'{split}_results.json'       )       )
def       A_   (   ):






     """simple docstring"""

     _a    	=				HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)       )

     if len(sys.argv       ) == 2 and sys.argv[1].endswith('''.json'''       ):
          # If we pass only one argument to the script and it's the path to a json file,
          # let's parse it to get our arguments.
          _a	,      _a	,      _a    	=				parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
     else:
          _a	,      _a	,      _a    	=				parser.parse_args_into_dataclasses()

     check_output_dir(_lowerCAmelCase       )

     # Setup logging
     logging.basicConfig(
         format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',     datefmt='''%m/%d/%Y %H:%M:%S''',     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN,     )
     logger.warning(
         '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''',     training_args.local_rank,     training_args.device,     training_args.n_gpu,     bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED       ),     training_args.fpaa,     )
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank       ):
          transformers.utils.logging.set_verbosity_info()
     logger.info('''Training/evaluation parameters %s''',     _lowerCAmelCase       )

     # Set seed
     set_seed(training_args.seed       )

     # Load pretrained model and tokenizer
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.

     _a    	=				AutoConfig.from_pretrained(
         model_args.config_name if model_args.config_name else model_args.model_name_or_path,     cache_dir=model_args.cache_dir,     )

     _a    	=				('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
     for p in extra_model_params:
          if getattr(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase       ):
               assert hasattr(_lowerCAmelCase,     _lowerCAmelCase       ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
               setattr(_lowerCAmelCase,     _lowerCAmelCase,     getattr(_lowerCAmelCase,     _lowerCAmelCase       )       )

     _a    	=				AutoTokenizer.from_pretrained(
         model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,     cache_dir=model_args.cache_dir,     )
     _a    	=				AutoModelForSeqaSeqLM.from_pretrained(
         model_args.model_name_or_path,     from_tf='''.ckpt''' in model_args.model_name_or_path,     config=_lowerCAmelCase,     cache_dir=model_args.cache_dir,     )

     # use task specific params
     use_task_specific_params(_lowerCAmelCase,     data_args.task       )

     # set num_beams for evaluation
     if data_args.eval_beams is None:
          _a    	=				model.config.num_beams

     # set decoder_start_token_id for MBart
     if model.config.decoder_start_token_id is None and isinstance(_lowerCAmelCase,     (MBartTokenizer, MBartTokenizerFast)       ):
          assert (
              data_args.tgt_lang is not None and data_args.src_lang is not None
          ), "mBart requires --tgt_lang and --src_lang"
          if isinstance(_lowerCAmelCase,     _lowerCAmelCase       ):
               _a    	=				tokenizer.lang_code_to_id[data_args.tgt_lang]
          else:
               _a    	=				tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

     if model_args.freeze_embeds:
          freeze_embeds(_lowerCAmelCase       )
     if model_args.freeze_encoder:
          freeze_params(model.get_encoder()       )
          assert_all_frozen(model.get_encoder()       )

     _a    	=				SeqaSeqDataset

     # Get datasets
     _a    	=				(
         dataset_class(
             _lowerCAmelCase,     type_path='''train''',     data_dir=data_args.data_dir,     n_obs=data_args.n_train,     max_target_length=data_args.max_target_length,     max_source_length=data_args.max_source_length,     prefix=model.config.prefix or '''''',     )
         if training_args.do_train
         else None
     )
     _a    	=				(
         dataset_class(
             _lowerCAmelCase,     type_path='''val''',     data_dir=data_args.data_dir,     n_obs=data_args.n_val,     max_target_length=data_args.val_max_target_length,     max_source_length=data_args.max_source_length,     prefix=model.config.prefix or '''''',     )
         if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
         else None
     )
     _a    	=				(
         dataset_class(
             _lowerCAmelCase,     type_path='''test''',     data_dir=data_args.data_dir,     n_obs=data_args.n_test,     max_target_length=data_args.test_max_target_length,     max_source_length=data_args.max_source_length,     prefix=model.config.prefix or '''''',     )
         if training_args.do_predict
         else None
     )

     # Initialize our Trainer
     _a    	=				(
         build_compute_metrics_fn(data_args.task,     _lowerCAmelCase       ) if training_args.predict_with_generate else None
     )
     _a    	=				SeqaSeqTrainer(
         model=_lowerCAmelCase,     args=_lowerCAmelCase,     data_args=_lowerCAmelCase,     train_dataset=_lowerCAmelCase,     eval_dataset=_lowerCAmelCase,     data_collator=SeqaSeqDataCollator(
             _lowerCAmelCase,     _lowerCAmelCase,     model.config.decoder_start_token_id,     training_args.tpu_num_cores       ),     compute_metrics=_lowerCAmelCase,     tokenizer=_lowerCAmelCase,     )

     _a    	=				{}
     # Training
     if training_args.do_train:
          logger.info('''*** Train ***'''       )

          _a    	=				trainer.train(
              model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
          _a    	=				train_result.metrics
          _a    	=				data_args.n_train

          trainer.save_model()  # this also saves the tokenizer

          if trainer.is_world_process_zero():
               handle_metrics('''train''',     _lowerCAmelCase,     training_args.output_dir       )
               all_metrics.update(_lowerCAmelCase       )

               # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
               trainer.state.save_to_json(os.path.join(training_args.output_dir,     '''trainer_state.json'''       )       )

               # For convenience, we also re-save the tokenizer to the same directory,
               # so that you can share your model easily on huggingface.co/models =)
               tokenizer.save_pretrained(training_args.output_dir       )

    # Evaluation
     if training_args.do_eval:
          logger.info('''*** Evaluate ***'''       )

          _a    	=				trainer.evaluate(metric_key_prefix='''val'''       )
          _a    	=				data_args.n_val
          _a    	=				round(metrics['''val_loss'''],     4       )

          if trainer.is_world_process_zero():
               handle_metrics('''val''',     _lowerCAmelCase,     training_args.output_dir       )
               all_metrics.update(_lowerCAmelCase       )

     if training_args.do_predict:
          logger.info('''*** Predict ***'''       )

          _a    	=				trainer.predict(test_dataset=_lowerCAmelCase,     metric_key_prefix='''test'''       )
          _a    	=				test_output.metrics
          _a    	=				data_args.n_test

          if trainer.is_world_process_zero():
               _a    	=				round(metrics['''test_loss'''],     4       )
               handle_metrics('''test''',     _lowerCAmelCase,     training_args.output_dir       )
               all_metrics.update(_lowerCAmelCase       )

               if training_args.predict_with_generate:
                    _a    	=				tokenizer.batch_decode(
                        test_output.predictions,     skip_special_tokens=_lowerCAmelCase,     clean_up_tokenization_spaces=_lowerCAmelCase       )
                    _a    	=				lmap(str.strip,     _lowerCAmelCase       )
                    write_txt_file(_lowerCAmelCase,     os.path.join(training_args.output_dir,     '''test_generations.txt'''       )       )

     if trainer.is_world_process_zero():
          save_json(_lowerCAmelCase,     os.path.join(training_args.output_dir,     '''all_results.json'''       )       )

     return all_metrics
def       A_   (   _lowerCAmelCase     :					str       ):






     """simple docstring"""

     main()


if __name__ == "__main__":
   main()




"""simple docstring"""






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case		=       logging.get_logger(__name__)
__snake_case		=       {'''vocab_file''': '''spiece.model'''}

__snake_case		=       {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

__snake_case		=       {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

__snake_case		=       '''▁'''


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		VOCAB_FILES_NAMES
     A_       :	int    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Optional[int]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[MASK]"    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _a    	=				(
              AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase    ,				normalized=__UpperCAmelCase							)
              if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							)
              else mask_token
          )

          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=__UpperCAmelCase    ,				remove_space=__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)

          _a    	=				do_lower_case
          _a    	=				remove_space
          _a    	=				keep_accents
          _a    	=				vocab_file

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							str:
          return len(self.sp_model							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				{self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(      self							)       ->							int:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Tuple:
          _a    	=				d

          # for backward compatibility
          if not hasattr(self    ,				'''sp_model_kwargs'''							):
               _a    	=				{}

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          if self.remove_space:
               _a    	=				''' '''.join(inputs.strip().split()							)
          else:
               _a    	=				inputs
          _a    	=				outputs.replace('''``'''    ,				'''"'''							).replace('''\'\''''    ,				'''"'''							)

          if not self.keep_accents:
               _a    	=				unicodedata.normalize('''NFKD'''    ,				__UpperCAmelCase							)
               _a    	=				''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase							)]							)
          if self.do_lower_case:
               _a    	=				outputs.lower()

          return outputs


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				self.preprocess_text(__UpperCAmelCase							)
          _a    	=				self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)
          _a    	=				[]
          for piece in pieces:
               if len(__UpperCAmelCase							) > 1 and piece[-1] == str(''','''							) and piece[-2].isdigit():
                    _a    	=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase    ,				''''''							)							)
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]							) == 1:
                              _a    	=				cur_pieces[1:]
                         else:
                              _a    	=				cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]							)
                    new_pieces.extend(__UpperCAmelCase							)
               else:
                    new_pieces.append(__UpperCAmelCase							)

          return new_pieces


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          return self.sp_model.PieceToId(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							int:
          return self.sp_model.IdToPiece(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				[]
          _a    	=				''''''
          _a    	=				False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(__UpperCAmelCase							) + token
                    _a    	=				True
                    _a    	=				[]
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
                    _a    	=				False
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[self.cls_token_id]
          if token_ids_a is None:
               return cls + token_ids_a + sep
          return cls + token_ids_a + sep + token_ids_a + sep


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          if token_ids_a is not None:
               return [1] + ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]
          return [1] + ([0] * len(__UpperCAmelCase							)) + [1]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep							) * [0]
          return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]





     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				os.path.join(
              __UpperCAmelCase    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCAmelCase							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file    ,				__UpperCAmelCase							)
          elif not os.path.isfile(self.vocab_file							):
               with open(__UpperCAmelCase    ,				'''wb'''							) as fi:
                    _a    	=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase							)

          return (out_vocab_file,)