'''simple docstring'''




import random
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  import PIL

  from transformers import FlavaImageProcessor
  from transformers.image_utils import PILImageResampling
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )
else:
  __snake_case  : Any		       =    None




class       __UpperCAmelCase							(							unittest.TestCase	):





       '''simple docstring'''



       def __init__( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE=7       ,	_SCREAMING_SNAKE_CASE=3       ,	_SCREAMING_SNAKE_CASE=30       ,	_SCREAMING_SNAKE_CASE=400       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=1 / 255       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=FLAVA_IMAGE_MEAN       ,	_SCREAMING_SNAKE_CASE=FLAVA_IMAGE_STD       ,	_SCREAMING_SNAKE_CASE=14       ,	_SCREAMING_SNAKE_CASE=75       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=16       ,	_SCREAMING_SNAKE_CASE=0.3       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=True       ,	_SCREAMING_SNAKE_CASE=FLAVA_CODEBOOK_MEAN       ,	_SCREAMING_SNAKE_CASE=FLAVA_CODEBOOK_STD       ,	)						->	Union[str, Any]:
           A_      =  size if size is not None else {'''height''': 224, '''width''': 224}
           A_      =  crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
           A_      =  codebook_size if codebook_size is not None else {'''height''': 112, '''width''': 112}
           A_      =  codebook_crop_size if codebook_crop_size is not None else {'''height''': 112, '''width''': 112}

           A_      =  parent
           A_      =  batch_size
           A_      =  num_channels
           A_      =  do_resize
           A_      =  do_rescale
           A_      =  rescale_factor
           A_      =  min_resolution
           A_      =  max_resolution
           A_      =  size
           A_      =  resample if resample is not None else PILImageResampling.BICUBIC
           A_      =  do_normalize
           A_      =  image_mean
           A_      =  image_std
           A_      =  do_center_crop
           A_      =  crop_size

           A_      =  input_size_patches
           A_      =  total_mask_patches
           A_      =  mask_group_max_patches
           A_      =  mask_group_min_patches
           A_      =  mask_group_min_aspect_ratio
           A_      =  mask_group_max_aspect_ratio

           A_      =  codebook_do_resize
           A_      =  codebook_size
           A_      =  codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
           A_      =  codebook_do_center_crop
           A_      =  codebook_crop_size
           A_      =  codebook_do_map_pixels
           A_      =  codebook_do_normalize
           A_      =  codebook_image_mean
           A_      =  codebook_image_std

       def    __A			( self					)						->	Any:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "resample": self.resample,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
               "input_size_patches": self.input_size_patches,
               "total_mask_patches": self.total_mask_patches,
               "mask_group_max_patches": self.mask_group_max_patches,
               "mask_group_min_patches": self.mask_group_min_patches,
               "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
               "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
               "codebook_do_resize": self.codebook_do_resize,
               "codebook_size": self.codebook_size,
               "codebook_resample": self.codebook_resample,
               "codebook_do_center_crop": self.codebook_do_center_crop,
               "codebook_crop_size": self.codebook_crop_size,
               "codebook_do_map_pixels": self.codebook_do_map_pixels,
               "codebook_do_normalize": self.codebook_do_normalize,
               "codebook_image_mean": self.codebook_image_mean,
               "codebook_image_std": self.codebook_image_std,
           }

       def    __A			( self					)						->	Tuple:
           return (self.size["height"], self.size["width"])

       def    __A			( self					)						->	int:
           return (
               (self.input_size_patches, self.input_size_patches)
               if not isinstance(self.input_size_patches       ,	_SCREAMING_SNAKE_CASE					)
               else self.input_size_patches
           )

       def    __A			( self					)						->	Dict:
           return (self.codebook_size["height"], self.codebook_size["width"])




@require_torch
@require_vision
class       __UpperCAmelCase							(							_UpperCamelCase		, unittest.TestCase	):





       '''simple docstring'''



       __lowercase   :   Optional[int]  					=							FlavaImageProcessor if is_vision_available() else None
       __lowercase   :   Union[str, Any]  					=							None

       def    __A			( self					)						->	int:
           A_      =  FlavaImageProcessingTester(self					)

       @property
       def    __A			( self					)						->	int:
           return self.image_processor_tester.prepare_image_processor_dict()

       def    __A			( self					)						->	int:
           A_      =  self.image_processing_class(**self.image_processor_dict					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''image_mean'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''image_std'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''do_normalize'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''do_resize'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''resample'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''crop_size'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''do_center_crop'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''do_rescale'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''rescale_factor'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''masking_generator'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_do_resize'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_size'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_resample'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_do_center_crop'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_crop_size'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_do_map_pixels'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_do_normalize'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_image_mean'''					)					)
           self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,	'''codebook_image_std'''					)					)

       def    __A			( self					)						->	Tuple:
           A_      =  self.image_processing_class.from_dict(self.image_processor_dict					)
           self.assertEqual(image_processor.size       ,	{'''height''': 224, '''width''': 224}					)
           self.assertEqual(image_processor.crop_size       ,	{'''height''': 224, '''width''': 224}					)
           self.assertEqual(image_processor.codebook_size       ,	{'''height''': 112, '''width''': 112}					)
           self.assertEqual(image_processor.codebook_crop_size       ,	{'''height''': 112, '''width''': 112}					)

           A_      =  self.image_processing_class.from_dict(
               self.image_processor_dict       ,	size=42       ,	crop_size=84       ,	codebook_size=33       ,	codebook_crop_size=66					)
           self.assertEqual(image_processor.size       ,	{'''height''': 42, '''width''': 42}					)
           self.assertEqual(image_processor.crop_size       ,	{'''height''': 84, '''width''': 84}					)
           self.assertEqual(image_processor.codebook_size       ,	{'''height''': 33, '''width''': 33}					)
           self.assertEqual(image_processor.codebook_crop_size       ,	{'''height''': 66, '''width''': 66}					)

       def    __A			( self					)						->	Dict:
           pass

       def    __A			( self					)						->	List[Any]:
           # Initialize image_processing
           A_      =  self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           A_      =  prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_SCREAMING_SNAKE_CASE					)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,	PIL.Image.Image					)

           # Test not batched input
           A_      =  image_processing(image_inputs[0]       ,	return_tensors='''pt'''					)

           # Test no bool masked pos
           self.assertFalse('''bool_masked_pos''' in encoded_images					)

           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()

           self.assertEqual(
               encoded_images.pixel_values.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

           # Test batched
           A_      =  image_processing(_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)
           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()

           # Test no bool masked pos
           self.assertFalse('''bool_masked_pos''' in encoded_images					)

           self.assertEqual(
               encoded_images.pixel_values.shape       ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,	)

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)						->	Any:
           # Initialize image_processing
           A_      =  self.image_processing_class(**self.image_processor_dict					)
           # create random tensors
           A_      =  prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE					)

           # Test not batched input
           A_      =  image_processing(image_inputs[0]       ,	return_tensors='''pt'''					)

           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

           A_      =  image_processing(_SCREAMING_SNAKE_CASE       ,	return_image_mask=_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)

           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape       ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,	)

           A_  ,A_      =  self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape       ,	(
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )       ,	)

           # Test batched
           A_      =  image_processing(_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					).pixel_values

           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.shape       ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,	)

           # Test masking
           A_      =  image_processing(_SCREAMING_SNAKE_CASE       ,	return_image_mask=_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)

           A_  ,A_      =  self.image_processor_tester.get_expected_image_size()
           self.assertEqual(
               encoded_images.pixel_values.shape       ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,	)

           A_  ,A_      =  self.image_processor_tester.get_expected_mask_size()
           self.assertEqual(
               encoded_images.bool_masked_pos.shape       ,	(
                   self.image_processor_tester.batch_size,
                   expected_height,
                   expected_width,
               )       ,	)

       def    __A			( self					)						->	Optional[int]:
           self._test_call_framework(np.ndarray       ,	prepare_kwargs={'''numpify''': True}					)

       def    __A			( self					)						->	str:
           self._test_call_framework(torch.Tensor       ,	prepare_kwargs={'''torchify''': True}					)

       def    __A			( self					)						->	Tuple:
           # Initialize image_processing
           random.seed(1234					)
           A_      =  self.image_processing_class(**self.image_processor_dict					)
           A_      =  prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_SCREAMING_SNAKE_CASE       ,	torchify=_SCREAMING_SNAKE_CASE					)

           # Test not batched input
           A_      =  image_processing(image_inputs[0]       ,	return_image_mask=_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)
           self.assertEqual(encoded_images.bool_masked_pos.sum().item()       ,	75					)

       def    __A			( self					)						->	str:
           # Initialize image_processing
           A_      =  self.image_processing_class(**self.image_processor_dict					)
           # create random PIL images
           A_      =  prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=_SCREAMING_SNAKE_CASE					)
           for image in image_inputs:
               self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,	PIL.Image.Image					)

           # Test not batched input
           A_      =  image_processing(image_inputs[0]       ,	return_codebook_pixels=_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)
           A_  ,A_      =  self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape       ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,	)

           # Test batched
           A_      =  image_processing(_SCREAMING_SNAKE_CASE       ,	return_codebook_pixels=_SCREAMING_SNAKE_CASE       ,	return_tensors='''pt'''					)
           A_  ,A_      =  self.image_processor_tester.get_expected_codebook_image_size()
           self.assertEqual(
               encoded_images.codebook_pixel_values.shape       ,	(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,	)







'''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}""")






