






"""simple docstring"""





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

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


A__					: Any=	logging.getLogger(__name__)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
				"""simple docstring"""

				return (preds == labels).mean()
@dataclass
class    __lowerCamelCase    :
			a      :				str 						=field(
			    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}  )
			a      :				Optional[str] 						=field(
			    default=_a							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
			a      :				Optional[str] 						=field(
			    default=_a							,	metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
			a      :				Optional[str] 						=field(
			    default=_a							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}							,	)







@dataclass
class    __lowerCamelCase    :
			a      :				str 						=field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(processors.keys()  )}  )
			a      :				str 						=field(metadata={"""help""": """Should contain the data files for the task."""}  )
			a      :				int 						=field(
			    default=1_2_8							,	metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }							,	)
			a      :				bool 						=field(
			    default=_a							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )







def   lowerCAmelCase_( )					->	List[Any]:
				"""simple docstring"""

				UpperCamelCase__         =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)					)
				UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir					)
				    and os.listdir(training_args.output_dir					)
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
								    ' --overwrite_output_dir to overcome.'					)

				# 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.local_rank != -1					) ,	training_args.fpaa ,	)
				# 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()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info('Training/evaluation parameters %s' ,	SCREAMING_SNAKE_CASE					)

				# Set seed
				set_seed(training_args.seed					)

				try:
								UpperCamelCase__         =    processors[data_args.task_name]()
								UpperCamelCase__         =    processor.get_labels()
								UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
				except KeyError:
								raise ValueError('Task not found: %s' % (data_args.task_name)					)

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

				UpperCamelCase__         =    AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,	num_labels=SCREAMING_SNAKE_CASE ,	finetuning_task=data_args.task_name ,	cache_dir=model_args.cache_dir ,	)
				UpperCamelCase__         =    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 ,	)
				UpperCamelCase__         =    AutoModelForMultipleChoice.from_pretrained(
				    model_args.model_name_or_path ,	from_tf=bool('.ckpt' in model_args.model_name_or_path					) ,	config=SCREAMING_SNAKE_CASE ,	cache_dir=model_args.cache_dir ,	)

				# Get datasets
				UpperCamelCase__         =    (
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir ,	tokenizer=SCREAMING_SNAKE_CASE ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.train ,	)
				    if training_args.do_train
				    else None
				)
				UpperCamelCase__         =    (
				    MultipleChoiceDataset(
				        data_dir=data_args.data_dir ,	tokenizer=SCREAMING_SNAKE_CASE ,	task=data_args.task_name ,	max_seq_length=data_args.max_seq_length ,	overwrite_cache=data_args.overwrite_cache ,	mode=Split.dev ,	)
				    if training_args.do_eval
				    else None
				)

				def compute_metrics(SCREAMING_SNAKE_CASE					) -> Dict:
								UpperCamelCase__         =    np.argmax(p.predictions ,	axis=1					)
								return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE ,	p.label_ids					)}

				# Data collator
				UpperCamelCase__         =    DataCollatorWithPadding(SCREAMING_SNAKE_CASE ,	pad_to_multiple_of=8					) if training_args.fpaa else None

				# Initialize our Trainer
				UpperCamelCase__         =    Trainer(
				    model=SCREAMING_SNAKE_CASE ,	args=SCREAMING_SNAKE_CASE ,	train_dataset=SCREAMING_SNAKE_CASE ,	eval_dataset=SCREAMING_SNAKE_CASE ,	compute_metrics=SCREAMING_SNAKE_CASE ,	data_collator=SCREAMING_SNAKE_CASE ,	)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_master():
												tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
				UpperCamelCase__         =    {}
				if training_args.do_eval:
								logger.info('*** Evaluate ***'					)

								UpperCamelCase__         =    trainer.evaluate()

								UpperCamelCase__         =    os.path.join(training_args.output_dir ,	'eval_results.txt'					)
								if trainer.is_world_master():
												with open(SCREAMING_SNAKE_CASE ,	'w'					) as writer:
																logger.info('***** Eval results *****'					)
																for key, value in result.items():
																				logger.info('  %s = %s' ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
																				writer.write('%s = %s\n' % (key, value)					)

																results.update(SCREAMING_SNAKE_CASE					)

				return results







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Any:
				"""simple docstring"""

				main()


if __name__ == "__main__":
		main()












"""simple docstring"""





import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
  from ...tokenization_utils_base import TextInput
from ...utils import logging


A__					: Dict=	logging.get_logger(__name__)

A__					: str=	{"""vocab_file""": """spiece.model"""}

A__					: Union[str, Any]=	{
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
A__					: Union[str, Any]=	{
    """t5-small""": 5_12,
    """t5-base""": 5_12,
    """t5-large""": 5_12,
    """t5-3b""": 5_12,
    """t5-11b""": 5_12,
}

A__					: Optional[Any]=	"""▁"""
class    __lowerCamelCase    (  _a  ):
   a      :				Dict 						=VOCAB_FILES_NAMES
   a      :				str 						=PRETRAINED_VOCAB_FILES_MAP
   a      :				Union[str, Any] 						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a      :				List[str] 						=["""input_ids""", """attention_mask"""]

   def __init__(					self ,				snake_case_ ,				snake_case_="</s>" ,				snake_case_="<unk>" ,				snake_case_="<pad>" ,				snake_case_=100 ,				snake_case_=None ,				snake_case_ = None ,				snake_case_=True ,				**snake_case_ ,				)		->       None:
       # Add extra_ids to the special token list
       if extra_ids > 0 and additional_special_tokens is None:
           UpperCamelCase__         =    [F'<extra_id_{i}>' for i in range(snake_case_      )]
       elif extra_ids > 0 and additional_special_tokens is not None:
           # Check that we have the right number of extra_id special tokens
           UpperCamelCase__         =    len(set(filter(lambda snake_case_      : bool('extra_id' in str(snake_case_      )      ) ,				snake_case_      )      )      )
           if extra_tokens != extra_ids:
               raise ValueError(
                   F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
                   ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
                   ' tokens'      )
       if legacy:
           logger.warning_once(
               F'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
               ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'      )

       UpperCamelCase__         =    legacy
       UpperCamelCase__         =    {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           eos_token=snake_case_ ,				unk_token=snake_case_ ,				pad_token=snake_case_ ,				extra_ids=snake_case_ ,				additional_special_tokens=snake_case_ ,				sp_model_kwargs=self.sp_model_kwargs ,				legacy=snake_case_ ,				**snake_case_ ,				)

       UpperCamelCase__         =    vocab_file
       UpperCamelCase__         =    extra_ids

       UpperCamelCase__         =    spm.SentencePieceProcessor(**self.sp_model_kwargs      )
       self.sp_model.Load(snake_case_      )

   @staticmethod
   def 						SCREAMING_SNAKE_CASE__ (					snake_case_ ,				snake_case_ ,				snake_case_      )		->       Optional[Any]:
       if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
           UpperCamelCase__         =    TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
           if init_max_model_length is not None and init_max_model_length != max_model_length:
               return init_max_model_length
           elif init_max_model_length is None:
               warnings.warn(
                   'This tokenizer was incorrectly instantiated with a model max length of'
                   F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
                   ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
                   ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
                   F' {pretrained_model_name_or_path} automatically truncating your input to'
                   F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
                   F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
                   ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
                   ' instantiate this tokenizer with `model_max_length` set to your preferred value.' ,				snake_case_ ,				)

       return max_model_length

   @property
   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       return self.sp_model.get_piece_size() + self._extra_ids

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[int]:
       UpperCamelCase__         =    {self.convert_ids_to_tokens(snake_case_      ): i for i in range(self.vocab_size      )}
       vocab.update(self.added_tokens_encoder      )
       return vocab

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None ,				snake_case_ = False      )		->       List[int]:
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case_ ,				token_ids_a=snake_case_ ,				already_has_special_tokens=snake_case_      )

       # normal case: some special tokens
       if token_ids_a is None:
           return ([0] * len(snake_case_      )) + [1]
       return ([0] * len(snake_case_      )) + [1] + ([0] * len(snake_case_      )) + [1]

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       List[Any]:
       return list(
           set(filter(lambda snake_case_      : bool(re.search(r'<extra_id_\d+>' ,				snake_case_      )      ) is not None ,				self.additional_special_tokens      )      )      )

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       int:
       return [self._convert_token_to_id(snake_case_      ) for token in self.get_sentinel_tokens()]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[int]:
       if len(snake_case_      ) > 0 and token_ids[-1] == self.eos_token_id:
           warnings.warn(
               F'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
               ' eos tokens being added.'      )
           return token_ids
       else:
           return token_ids + [self.eos_token_id]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    [self.eos_token_id]

       if token_ids_a is None:
           return len(token_ids_a + eos      ) * [0]
       return len(token_ids_a + eos + token_ids_a + eos      ) * [0]

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ = None      )		->       List[int]:
       UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
       if token_ids_a is None:
           return token_ids_a
       else:
           UpperCamelCase__         =    self._add_eos_if_not_present(snake_case_      )
           return token_ids_a + token_ids_a

   def __getstate__(					self      )		->       str:
       UpperCamelCase__         =    self.__dict__.copy()
       UpperCamelCase__         =    None
       return state

   def __setstate__(					self ,				snake_case_      )		->       Any:
       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 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[str]:
       # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
       # the beginning of the text
       if not self.legacy:
           UpperCamelCase__         =    SPIECE_UNDERLINE + text.replace(snake_case_ ,				' '      )
       return super().tokenize(snake_case_ ,				**snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				**snake_case_      )		->       List[Any]:
       if not self.legacy:
           UpperCamelCase__         =    text.startswith(snake_case_      )
           if is_first:
               UpperCamelCase__         =    text[1:]

       UpperCamelCase__         =    self.sp_model.encode(snake_case_ ,				out_type=snake_case_      )

       if not self.legacy and not is_first and not text.startswith(' '      ) and tokens[0].startswith(snake_case_      ):
           UpperCamelCase__         =    ([tokens[0][1:]] if len(tokens[0]      ) > 1 else []) + tokens[1:]
       return tokens

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       if token.startswith('<extra_id_'      ):
           UpperCamelCase__         =    re.match(r'<extra_id_(\d+)>' ,				snake_case_      )
           UpperCamelCase__         =    int(match.group(1      )      )
           return self.vocab_size - num - 1
       return self.sp_model.piece_to_id(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       Optional[int]:
       if index < self.sp_model.get_piece_size():
           UpperCamelCase__         =    self.sp_model.IdToPiece(snake_case_      )
       else:
           UpperCamelCase__         =    F'<extra_id_{self.vocab_size - 1 - index}>'
       return token

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       List[str]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    ''
       UpperCamelCase__         =    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(snake_case_      ) + token
               UpperCamelCase__         =    True
               UpperCamelCase__         =    []
           else:
               current_sub_tokens.append(snake_case_      )
               UpperCamelCase__         =    False
       out_string += self.sp_model.decode(snake_case_      )
       return out_string.strip()







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

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

       return (out_vocab_file,)




