

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__":
      __a						:List[str]            = 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=16)
      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)

      __a      ,			__a						:Optional[Any]            = parser.parse_known_args()

      # overwrite batch size until we have tf_glue.py
      __a						:Tuple            = 16
      __a						:str            = 16

      # Set up logging
      __a						:Optional[int]            = 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
      __a						:Optional[int]            = TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      __a						:List[str]            = AutoTokenizer.from_pretrained(args.model_name_or_path)

      # Load dataset
      __a      ,			__a						:Any            = load_dataset('imdb', split=['train', 'test'])
      __a						:List[str]            = train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
      __a						:str            = test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

      # Preprocess train dataset
      __a						:List[Any]            = 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'])

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

      # Preprocess test dataset
      __a						:List[Any]            = 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'])

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

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

      __a						:str            = time.time()
      __a						:Union[str, Any]            = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
      __a						:Tuple            = 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}")


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

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}

class      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

            if token_ids_a is None:
                  return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            A_	     =							[self.cls_token_id]
            A_	     =							[self.sep_token_id]
            return cls + token_ids_a + sep + token_ids_a + sep





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

            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 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            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 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            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(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  ):
                  copyfile(self.vocab_file	, UpperCAmelCase  )

            return (out_vocab_file,)