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 SPIECE_UNDERLINE, logging


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{"vocab_file": "spiece.model"}

_lowerCAmelCase       =		{
    "vocab_file": {
        "TsinghuaAI/CPM-Generate": "https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model",
    }
}
class     lowerCAmelCase_     (       __lowercase ):







							def __init__(   self      :     Any ,			_A      :     Optional[int] ,			_A      :     Union[str, Any]=False ,			_A      :     Dict=True ,			_A      :     str=False ,			_A      :     int="<s>" ,			_A      :     Optional[Any]="</s>" ,			_A      :     Any="<unk>" ,			_A      :     List[Any]="<sep>" ,			_A      :     Any="<pad>" ,			_A      :     List[str]="<cls>" ,			_A      :     Tuple="<mask>" ,			_A      :     List[Any]=["<eop>", "<eod>"] ,			_A      :     Optional[Dict[str, Any]] = None ,			**_A      :     Optional[Any] ,			):
												_UpperCamelCase			     =     AddedToken(_A ,			lstrip=_A ,			rstrip=_A		) if isinstance(_A ,			_A		) else mask_token

												_UpperCamelCase			     =     {} if sp_model_kwargs is None else sp_model_kwargs

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

												_UpperCamelCase			     =     3

												_UpperCamelCase			     =     do_lower_case
												_UpperCamelCase			     =     remove_space
												_UpperCamelCase			     =     keep_accents
												_UpperCamelCase			     =     vocab_file

												_UpperCamelCase			     =     spm.SentencePieceProcessor(**self.sp_model_kwargs		)
												self.sp_model.Load(_A		)

												try:
																	import jieba
												except ModuleNotFoundError as error:
																	raise error.__class__(
																	    '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
																	    '''See https://pypi.org/project/jieba/ for installation.'''		)
												_UpperCamelCase			     =     jieba
												_UpperCamelCase			     =     str.maketrans(''' \n''' ,			'''\u2582\u2583'''		)







							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def  UpperCamelCase_						(   self      :     int		):
												return len(self.sp_model		)







							def  UpperCamelCase_						(   self      :     Tuple		):
												_UpperCamelCase			     =     {self.convert_ids_to_tokens(_A		): i for i in range(self.vocab_size		)}
												vocab.update(self.added_tokens_encoder		)
												return vocab







							def __getstate__(   self      :     Optional[Any]		):
												_UpperCamelCase			     =     self.__dict__.copy()
												_UpperCamelCase			     =     None
												return state







							def __setstate__(   self      :     str ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     d

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

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







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     Any		):
												if self.remove_space:
																	_UpperCamelCase			     =     ''' '''.join(inputs.strip().split()		)
												else:
																	_UpperCamelCase			     =     inputs
												_UpperCamelCase			     =     outputs.replace('''``''' ,			'''"'''		).replace('''\'\'''' ,			'''"'''		)

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

												return outputs







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

												return new_pieces







							def  UpperCamelCase_						(   self      :     List[str] ,			_A      :     Optional[Any]		):
												return self.sp_model.PieceToId(_A		)







							def  UpperCamelCase_						(   self      :     int ,			_A      :     Any		):
												return self.sp_model.IdToPiece(_A		)







							def  UpperCamelCase_						(   self      :     Optional[int] ,			_A      :     Optional[int]		):
												_UpperCamelCase			     =     ''''''.join(_A		).replace(_A ,			''' '''		).strip()
												return out_string







							def  UpperCamelCase_						(   self      :     Tuple ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												_UpperCamelCase			     =     [self.sep_token_id]
												_UpperCamelCase			     =     [self.cls_token_id]
												if token_ids_a is None:
																	return token_ids_a + sep + cls
												return token_ids_a + sep + token_ids_a + sep + cls







							def  UpperCamelCase_						(   self      :     int ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None ,			_A      :     bool = False		):

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

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







							def  UpperCamelCase_						(   self      :     List[Any] ,			_A      :     List[int] ,			_A      :     Optional[List[int]] = None		):
												_UpperCamelCase			     =     [self.sep_token_id]
												_UpperCamelCase			     =     [2]

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







							def  UpperCamelCase_						(   self      :     Union[str, Any] ,			_A      :     str ,			_A      :     Optional[str] = None		):
												if not os.path.isdir(_A		):
																	logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
																	return
												_UpperCamelCase			     =     os.path.join(
												    _A ,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

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

												return (out_vocab_file,)







							def  UpperCamelCase_						(   self      :     Tuple ,			*_A      :     Any ,			**_A      :     str		):
												_UpperCamelCase			     =     super()._decode(*_A ,			**_A		)
												_UpperCamelCase			     =     text.replace(''' ''' ,			''''''		).replace('''\u2582''' ,			''' '''		).replace('''\u2583''' ,			'''\n'''		)
												return text




'''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,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



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







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,metadata={
            'help': (
                'The maximum total sequence length for target text after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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``.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_42      ,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.'
            )
        }      ,)
    lowercase  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''simple docstring'''





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







def 							UpperCamelCase_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  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.
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  parser.parse_args_into_dataclasses()

  check_output_dir(A__   )

  # 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"""				,       A__   )

  # 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.

  lowerCAmelCase_     : List[Any]     =  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				,       )

  lowerCAmelCase_     : List[Any]     =  ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
  for p in extra_model_params:
    if getattr(A__				,       A__				,       A__   ):
      assert hasattr(A__				,       A__   ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
      setattr(A__				,       A__				,       getattr(A__				,       A__   )   )

  lowerCAmelCase_     : Any     =  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				,       )
  lowerCAmelCase_     : Union[str, Any]     =  AutoModelForSeqaSeqLM.from_pretrained(
      model_args.model_name_or_path				,       from_tf=""".ckpt""" in model_args.model_name_or_path				,       config=A__				,       cache_dir=model_args.cache_dir				,       )

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

  # set num_beams for evaluation
  if data_args.eval_beams is None:
    lowerCAmelCase_     : List[Any]     =  model.config.num_beams

  # set decoder_start_token_id for MBart
  if model.config.decoder_start_token_id is None and isinstance(A__				,       (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(A__				,       A__   ):
      lowerCAmelCase_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang   )

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

  lowerCAmelCase_     : Any     =  SeqaSeqDataset

  # Get datasets
  lowerCAmelCase_     : List[str]     =  (
      dataset_class(
          A__				,       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
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       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
  )
  lowerCAmelCase_     : List[Any]     =  (
      dataset_class(
          A__				,       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
  lowerCAmelCase_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  SeqaSeqTrainer(
      model=A__				,       args=A__				,       data_args=A__				,       train_dataset=A__				,       eval_dataset=A__				,       data_collator=SeqaSeqDataCollator(
          A__				,       A__				,       model.config.decoder_start_token_id				,       training_args.tpu_num_cores   )				,       compute_metrics=A__				,       tokenizer=A__				,       )

  lowerCAmelCase_     : Dict     =  {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***"""   )

    lowerCAmelCase_     : Optional[int]     =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
    lowerCAmelCase_     : Union[str, Any]     =  train_result.metrics
    lowerCAmelCase_     : Optional[int]     =  data_args.n_train

    trainer.save_model()  # this also saves the tokenizer

    if trainer.is_world_process_zero():
      handle_metrics("""train"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      # 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 ***"""   )

    lowerCAmelCase_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  round(metrics["""val_loss"""]				,       4   )

    if trainer.is_world_process_zero():
      handle_metrics("""val"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

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

    lowerCAmelCase_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

    if trainer.is_world_process_zero():
      lowerCAmelCase_     : int     =  round(metrics["""test_loss"""]				,       4   )
      handle_metrics("""test"""				,       A__				,       training_args.output_dir   )
      all_metrics.update(A__   )

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  tokenizer.batch_decode(
            test_output.predictions				,       skip_special_tokens=A__				,       clean_up_tokenization_spaces=A__   )
        lowerCAmelCase_     : List[Any]     =  lmap(str.strip				,       A__   )
        write_txt_file(A__				,       os.path.join(training_args.output_dir				,       """test_generations.txt"""   )   )

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

  return all_metrics







def 							UpperCamelCase_     (       A__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



