

'''simple docstring'''






import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
      snake_case_					  =	argparse.ArgumentParser()

      # Hyperparameters sent by the client are passed as command-line arguments to the script.
      parser.add_argument('--epochs', type=int, default=1)
      parser.add_argument('--per_device_train_batch_size', type=int, default=1_6)
      parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
      parser.add_argument('--model_name_or_path', type=str)
      parser.add_argument('--learning_rate', type=str, default=5E-5)
      parser.add_argument('--do_train', type=bool, default=True)
      parser.add_argument('--do_eval', type=bool, default=True)
      parser.add_argument('--output_dir', type=str)

      snake_case_  ,     snake_case_					  =	parser.parse_known_args()

      # overwrite batch size until we have tf_glue.py
      snake_case_					  =	1_6
      snake_case_					  =	1_6

      # Set up logging
      snake_case_					  =	logging.getLogger(__name__)

      logging.basicConfig(
          level=logging.getLevelName('INFO'),
          handlers=[logging.StreamHandler(sys.stdout)],
          format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
      )

      # Load model and tokenizer
      snake_case_					  =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      snake_case_					  =	AutoTokenizer.from_pretrained(args.model_name_or_path)

      # Load dataset
      snake_case_  ,     snake_case_					  =	load_dataset('imdb', split=['train', 'test'])
      snake_case_					  =	train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
      snake_case_					  =	test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

      # Preprocess train dataset
      snake_case_					  =	train_dataset.map(
          lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
      )
      train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

      snake_case_					  =	{
          x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
          for x in ['input_ids', 'attention_mask']
      }
      snake_case_					  =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
          args.per_device_train_batch_size
      )

      # Preprocess test dataset
      snake_case_					  =	test_dataset.map(
          lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
      )
      test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

      snake_case_					  =	{
          x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
          for x in ['input_ids', 'attention_mask']
      }
      snake_case_					  =	tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
          args.per_device_eval_batch_size
      )

      # fine optimizer and loss
      snake_case_					  =	tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      snake_case_					  =	tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      snake_case_					  =	[tf.keras.metrics.SparseCategoricalAccuracy()]
      model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

      snake_case_					  =	time.time()
      snake_case_					  =	model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      snake_case_					  =	time.time() - start_train_time

      logger.info('*** Train ***')
      logger.info(F'''train_runtime = {end_train_time}''')
      for key, value in train_results.history.items():
            logger.info(F'''  {key} = {value}''')




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_					  =	{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):




    def __init__( self    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=False    ,					lowercase__="<s>"    ,					lowercase__="</s>"    ,					lowercase__="<unk>"    ,					lowercase__="<sep>"    ,					lowercase__="<pad>"    ,					lowercase__="<cls>"    ,					lowercase__="<mask>"    ,					lowercase__=["<eop>", "<eod>"]    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	AddedToken(lowercase__    ,					lstrip=lowercase__    ,					rstrip=lowercase__      ) if isinstance(lowercase__    ,					lowercase__      ) else mask_token

          super().__init__(
              vocab_file=lowercase__    ,					tokenizer_file=lowercase__    ,					do_lower_case=lowercase__    ,					remove_space=lowercase__    ,					keep_accents=lowercase__    ,					bos_token=lowercase__    ,					eos_token=lowercase__    ,					unk_token=lowercase__    ,					sep_token=lowercase__    ,					pad_token=lowercase__    ,					cls_token=lowercase__    ,					mask_token=lowercase__    ,					additional_special_tokens=lowercase__    ,					**lowercase__    ,					)

          SCREAMING_SNAKE_CASE_				:      List[str]					=	3
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	do_lower_case
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	remove_space
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	keep_accents
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_file
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	False if not self.vocab_file else True

          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."      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	jieba
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	str.maketrans(" \n"    ,					"\u2582\u2583"      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      Dict					=	[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   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."      )

          if not os.path.isdir(lowercase__      ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
                return
          SCREAMING_SNAKE_CASE_				:      str					=	os.path.join(
              lowercase__    ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase__      ):
                copyfile(self.vocab_file    ,					lowercase__      )

          return (out_vocab_file,)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	[
              " ".join([x.translate(self.translator      ) for x in self.jieba.cut(lowercase__    ,					cut_all=lowercase__      )]      )
              for text in batch_text_or_text_pairs
          ]
          return super()._batch_encode_plus(lowercase__    ,					*lowercase__    ,					**lowercase__      )







    def   __lowerCamelCase				( self    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	super()._decode(*lowercase__    ,					**lowercase__      )
          SCREAMING_SNAKE_CASE_				:      str					=	text.replace(" "    ,					""      ).replace("\u2582"    ,					" "      ).replace("\u2583"    ,					"\n"      )
          return text

