


'''simple docstring'''







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():
   from PIL import Image

   from transformers import ViTImageProcessor







class A__	(						unittest.TestCase				):






      def __init__(			self      :Optional[int]					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Any=7					,						SCREAMING_SNAKE_CASE      :List[Any]=3					,						SCREAMING_SNAKE_CASE      :Optional[Any]=1_8					,						SCREAMING_SNAKE_CASE      :List[Any]=3_0					,						SCREAMING_SNAKE_CASE      :Any=4_0_0					,						SCREAMING_SNAKE_CASE      :List[Any]=True					,						SCREAMING_SNAKE_CASE      :Dict=None					,						SCREAMING_SNAKE_CASE      :List[str]=True					,						SCREAMING_SNAKE_CASE      :str=[0.5, 0.5, 0.5]					,						SCREAMING_SNAKE_CASE      :Optional[int]=[0.5, 0.5, 0.5]					,						)	-> List[Any]:




             '''simple docstring'''
             _a   :			int					 =size if size is not None else {"""height""": 1_8, """width""": 1_8}
             _a   :			List[Any]					 =parent
             _a   :			List[str]					 =batch_size
             _a   :			Optional[int]					 =num_channels
             _a   :			Dict					 =image_size
             _a   :			int					 =min_resolution
             _a   :			Dict					 =max_resolution
             _a   :			Union[str, Any]					 =do_resize
             _a   :			int					 =size
             _a   :			int					 =do_normalize
             _a   :			Dict					 =image_mean
             _a   :			List[str]					 =image_std






      def __UpperCAmelCase	(			self      :str     )	-> Any:




             '''simple docstring'''
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
             }







@require_torch
@require_vision
class A__	(						UpperCAmelCase__			,					unittest.TestCase				):
      __UpperCamelCase	:  List[Any]     	=							ViTImageProcessor if is_vision_available() else None






      def __UpperCAmelCase	(			self      :List[str]     )	-> Tuple:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =ViTImageProcessingTester(self     )






      @property
      def __UpperCAmelCase	(			self      :Any     )	-> str:




             '''simple docstring'''
             return self.image_processor_tester.prepare_image_processor_dict()






      def __UpperCAmelCase	(			self      :List[str]     )	-> Optional[Any]:




             '''simple docstring'''
             _a   :			Optional[int]					 =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					,						"""size"""     )     )






      def __UpperCAmelCase	(			self      :List[Any]     )	-> str:




             '''simple docstring'''
             _a   :			Any					 =self.image_processing_class.from_dict(self.image_processor_dict     )
             self.assertEqual(image_processor.size					,						{"""height""": 1_8, """width""": 1_8}     )

             _a   :			Optional[Any]					 =self.image_processing_class.from_dict(self.image_processor_dict					,						size=4_2     )
             self.assertEqual(image_processor.size					,						{"""height""": 4_2, """width""": 4_2}     )






      def __UpperCAmelCase	(			self      :Union[str, Any]     )	-> List[str]:




             '''simple docstring'''
             pass






      def __UpperCAmelCase	(			self      :List[Any]     )	-> int:




             '''simple docstring'''
             # Initialize image_processing
             _a   :			str					 =self.image_processing_class(**self.image_processor_dict     )
             # create random PIL images
             _a   :			Any					 =prepare_image_inputs(self.image_processor_tester					,						equal_resolution=SCREAMING_SNAKE_CASE     )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE					,						Image.Image     )

             # Test not batched input
             _a   :			Any					 =image_processing(image_inputs[0]					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""width"""],
                 )					,						)

             # Test batched
             _a   :			Any					 =image_processing(SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""width"""],
                 )					,						)






      def __UpperCAmelCase	(			self      :Tuple     )	-> Any:




             '''simple docstring'''
             # Initialize image_processing
             _a   :			Dict					 =self.image_processing_class(**self.image_processor_dict     )
             # create random numpy tensors
             _a   :			Any					 =prepare_image_inputs(self.image_processor_tester					,						equal_resolution=SCREAMING_SNAKE_CASE					,						numpify=SCREAMING_SNAKE_CASE     )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE					,						np.ndarray     )

             # Test not batched input
             _a   :			Dict					 =image_processing(image_inputs[0]					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""width"""],
                 )					,						)

             # Test batched
             _a   :			Tuple					 =image_processing(SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""width"""],
                 )					,						)






      def __UpperCAmelCase	(			self      :Tuple     )	-> Tuple:




             '''simple docstring'''
             # Initialize image_processing
             _a   :			Tuple					 =self.image_processing_class(**self.image_processor_dict     )
             # create random PyTorch tensors
             _a   :			Any					 =prepare_image_inputs(self.image_processor_tester					,						equal_resolution=SCREAMING_SNAKE_CASE					,						torchify=SCREAMING_SNAKE_CASE     )
             for image in image_inputs:
                    self.assertIsInstance(SCREAMING_SNAKE_CASE					,						torch.Tensor     )

             # Test not batched input
             _a   :			Optional[Any]					 =image_processing(image_inputs[0]					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     1,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""width"""],
                 )					,						)

             # Test batched
             _a   :			List[str]					 =image_processing(SCREAMING_SNAKE_CASE					,						return_tensors="""pt"""     ).pixel_values
             self.assertEqual(
                 encoded_images.shape					,						(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.size["""height"""],
                     self.image_processor_tester.size["""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__":
   A__:   Optional[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)

   A__   ,	A__:   List[Any]    =	parser.parse_known_args()

   # overwrite batch size until we have tf_glue.py
   A__:   Tuple    =	16
   A__:   List[str]    =	16

   # Set up logging
   A__:   str    =	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__:   int    =	TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   A__:   Any    =	AutoTokenizer.from_pretrained(args.model_name_or_path)

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

   # Preprocess train dataset
   A__:   Optional[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'''])

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

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

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

   A__:   str    =	time.time()
   A__:   Dict    =	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}")


