'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       def    __A			( self					)						->	str:
           A_      =  tempfile.mkdtemp()

           A_      =  CLIPImageProcessor()
           A_      =  BertTokenizer.from_pretrained(
               '''hf-internal-testing/tiny-random-BertModel'''       ,	model_input_names=['''input_ids''', '''attention_mask''']					)

           A_      =  GitProcessor(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           processor.save_pretrained(self.tmpdirname					)

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	str:
           return AutoProcessor.from_pretrained(self.tmpdirname       ,	**_SCREAMING_SNAKE_CASE					).tokenizer

       def    __A			( self       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           return AutoProcessor.from_pretrained(self.tmpdirname       ,	**_SCREAMING_SNAKE_CASE					).image_processor

       def    __A			( self					)						->	List[str]:
           shutil.rmtree(self.tmpdirname					)

       def    __A			( self					)						->	Optional[int]:
           A_      =  [np.random.randint(255       ,	size=(3, 30, 400)       ,	dtype=np.uinta					)]

           A_      =  [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE       ,	0       ,	-1					)					) for x in image_inputs]

           return image_inputs

       def    __A			( self					)						->	Union[str, Any]:
           A_      =  GitProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()					)
           processor.save_pretrained(self.tmpdirname					)

           A_      =  self.get_tokenizer(bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''					)
           A_      =  self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE       ,	padding_value=1.0					)

           A_      =  GitProcessor.from_pretrained(
               self.tmpdirname       ,	bos_token='''(BOS)'''       ,	eos_token='''(EOS)'''       ,	do_normalize=_SCREAMING_SNAKE_CASE       ,	padding_value=1.0					)

           self.assertEqual(processor.tokenizer.get_vocab()       ,	tokenizer_add_kwargs.get_vocab()					)
           self.assertIsInstance(processor.tokenizer       ,	_SCREAMING_SNAKE_CASE					)

           self.assertEqual(processor.image_processor.to_json_string()       ,	image_processor_add_kwargs.to_json_string()					)
           self.assertIsInstance(processor.image_processor       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	Any:
           A_      =  self.get_image_processor()
           A_      =  self.get_tokenizer()

           A_      =  GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,	image_processor=_SCREAMING_SNAKE_CASE					)

           A_      =  self.prepare_image_inputs()

           A_      =  image_processor(_SCREAMING_SNAKE_CASE       ,	return_tensors='''np'''					)
           A_      =  processor(images=_SCREAMING_SNAKE_CASE       ,	return_tensors='''np'''					)

           for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()       ,	input_processor[key].sum()       ,	delta=1E-2					)

       def    __A			( self					)						->	Optional[Any]:
           A_      =  self.get_image_processor()
           A_      =  self.get_tokenizer()

           A_      =  GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,	image_processor=_SCREAMING_SNAKE_CASE					)

           A_      =  '''lower newer'''

           A_      =  processor(text=_SCREAMING_SNAKE_CASE					)

           A_      =  tokenizer(_SCREAMING_SNAKE_CASE       ,	return_token_type_ids=_SCREAMING_SNAKE_CASE					)

           for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]       ,	encoded_processor[key]					)

       def    __A			( self					)						->	Optional[Any]:
           A_      =  self.get_image_processor()
           A_      =  self.get_tokenizer()

           A_      =  GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,	image_processor=_SCREAMING_SNAKE_CASE					)

           A_      =  '''lower newer'''
           A_      =  self.prepare_image_inputs()

           A_      =  processor(text=_SCREAMING_SNAKE_CASE       ,	images=_SCREAMING_SNAKE_CASE					)

           self.assertListEqual(list(inputs.keys()					)       ,	['''input_ids''', '''attention_mask''', '''pixel_values''']					)

           # test if it raises when no input is passed
           with pytest.raises(_SCREAMING_SNAKE_CASE					):
               processor()

       def    __A			( self					)						->	int:
           A_      =  self.get_image_processor()
           A_      =  self.get_tokenizer()

           A_      =  GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,	image_processor=_SCREAMING_SNAKE_CASE					)

           A_      =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           A_      =  processor.batch_decode(_SCREAMING_SNAKE_CASE					)
           A_      =  tokenizer.batch_decode(_SCREAMING_SNAKE_CASE					)

           self.assertListEqual(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

       def    __A			( self					)						->	List[Any]:
           A_      =  self.get_image_processor()
           A_      =  self.get_tokenizer()

           A_      =  GitProcessor(tokenizer=_SCREAMING_SNAKE_CASE       ,	image_processor=_SCREAMING_SNAKE_CASE					)

           A_      =  '''lower newer'''
           A_      =  self.prepare_image_inputs()

           A_      =  processor(text=_SCREAMING_SNAKE_CASE       ,	images=_SCREAMING_SNAKE_CASE					)

           # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
           self.assertListEqual(list(inputs.keys()					)       ,	['''input_ids''', '''attention_mask''', '''pixel_values''']					)







'''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  : Any		       =    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)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    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  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    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  : Dict		       =    {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    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  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

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

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






