

'''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 json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase,unittest.TestCase				):
    _A         =							HerbertTokenizer
    _A         =							HerbertTokenizerFast
    _A         =							True




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(F"{get_tests_dir()}/fixtures/sample_text_no_unicode.txt"    ,					encoding="utf-8"      ) as f_data:
                SCREAMING_SNAKE_CASE_				:      List[str]					=	f_data.read().replace("\n\n"    ,					"\n"      ).strip()

          SCREAMING_SNAKE_CASE_				:      Dict					=	[
              "<s>",
              "</s>",
              "l",
              "o",
              "w",
              "e",
              "r",
              "s",
              "t",
              "i",
              "d",
              "n",
              "w</w>",
              "r</w>",
              "t</w>",
              "lo",
              "low",
              "er</w>",
              "low</w>",
              "lowest</w>",
              "newer</w>",
              "wider</w>",
              ",</w>",
              "<unk>",
          ]
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	dict(zip(lowercase__    ,					range(len(lowercase__      )      )      )      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	["l o 123", "lo w 1456", "e r</w> 1789", ""]

          SCREAMING_SNAKE_CASE_				:      Any					=	os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["vocab_file"]      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	os.path.join(self.tmpdirname    ,					VOCAB_FILES_NAMES["merges_file"]      )
          with open(self.vocab_file    ,					"w"      ) as fp:
                fp.write(json.dumps(lowercase__      )      )
          with open(self.merges_file    ,					"w"      ) as fp:
                fp.write("\n".join(lowercase__      )      )




    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	"lower newer"
          SCREAMING_SNAKE_CASE_				:      str					=	"lower newer"
          return input_text, output_text




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	self.tokenizer_class(vocab_file=self.vocab_file    ,					merges_file=self.merges_file      )

          SCREAMING_SNAKE_CASE_				:      int					=	"lower"
          SCREAMING_SNAKE_CASE_				:      List[str]					=	["low", "er</w>"]
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	tokenizer.tokenize(lowercase__      )
          self.assertListEqual(lowercase__    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	tokens + ["<unk>"]
          SCREAMING_SNAKE_CASE_				:      Dict					=	[16, 17, 23]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase__      )    ,					lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          if not self.test_rust_tokenizer:
                return

          SCREAMING_SNAKE_CASE_				:      Dict					=	self.get_tokenizer()
          SCREAMING_SNAKE_CASE_				:      Any					=	self.get_rust_tokenizer()

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	"lower,newer"

          SCREAMING_SNAKE_CASE_				:      int					=	tokenizer.tokenize(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	rust_tokenizer.tokenize(lowercase__      )
          self.assertListEqual(lowercase__    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	tokenizer.encode(lowercase__    ,					add_special_tokens=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      int					=	rust_tokenizer.encode(lowercase__    ,					add_special_tokens=lowercase__      )
          self.assertListEqual(lowercase__    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Any					=	self.get_rust_tokenizer()
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	tokenizer.encode(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	rust_tokenizer.encode(lowercase__      )
          self.assertListEqual(lowercase__    ,					lowercase__      )







    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	self.tokenizer_class.from_pretrained("allegro/herbert-base-cased"      )

          SCREAMING_SNAKE_CASE_				:      List[Any]					=	tokenizer.encode("konstruowanie sekwencji"    ,					add_special_tokens=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	tokenizer.encode("konstruowanie wielu sekwencji"    ,					add_special_tokens=lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	tokenizer.build_inputs_with_special_tokens(lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	tokenizer.build_inputs_with_special_tokens(lowercase__    ,					lowercase__      )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]

