'''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__	:     Tuple      =    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__	:     List[str]      =    parser.parse_known_args()

 # overwrite batch size until we have tf_glue.py
 snake_case__	:     Union[str, Any]      =    16
 snake_case__	:     Optional[Any]      =    16

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

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

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

 # Preprocess test dataset
 snake_case__	:     List[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'''])

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

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

 snake_case__	:     Union[str, Any]      =    time.time()
 snake_case__	:     Optional[int]      =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
 snake_case__	:     Optional[int]      =    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 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 OwlViTImageProcessor







class 			UpperCAmelCase			(							unittest.TestCase):

 def __init__(			self			:						Optional[Any],       a_			:						Optional[int],       a_			:						Dict=7,       a_			:						List[str]=3,       a_			:						int=18,       a_			:						Tuple=30,       a_			:						Any=400,       a_			:						Optional[int]=True,       a_			:						Optional[int]=None,       a_			:						Any=True,       a_			:						Optional[int]=None,       a_			:						int=True,       a_			:						Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073],       a_			:						Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711],       a_			:						Optional[int]=True,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    min_resolution
    UpperCamelCase__			    =    max_resolution
    UpperCamelCase__			    =    do_resize
    UpperCamelCase__			    =    size if size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_center_crop
    UpperCamelCase__			    =    crop_size if crop_size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_normalize
    UpperCamelCase__			    =    image_mean
    UpperCamelCase__			    =    image_std
    UpperCamelCase__			    =    do_convert_rgb

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Tuple            =       OwlViTImageProcessor if is_vision_available() else None

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    OwlViTImageProcessingTester(self       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_,       "do_resize"       )       )
    self.assertTrue(hasattr(a_,       "size"       )       )
    self.assertTrue(hasattr(a_,       "do_center_crop"       )       )
    self.assertTrue(hasattr(a_,       "center_crop"       )       )
    self.assertTrue(hasattr(a_,       "do_normalize"       )       )
    self.assertTrue(hasattr(a_,       "image_mean"       )       )
    self.assertTrue(hasattr(a_,       "image_std"       )       )
    self.assertTrue(hasattr(a_,       "do_convert_rgb"       )       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size,       {"height": 18, "width": 18}       )
    self.assertEqual(image_processor.crop_size,       {"height": 18, "width": 18}       )

    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict,       size=42,       crop_size=84       )
    self.assertEqual(image_processor.size,       {"height": 42, "width": 42}       )
    self.assertEqual(image_processor.crop_size,       {"height": 84, "width": 84}       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_       )

    for image in image_inputs:
       self.assertIsInstance(a_,       Image.Image       )

    # Test not batched input
    UpperCamelCase__			    =    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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       numpify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       np.ndarray       )

    # Test not batched input
    UpperCamelCase__			    =    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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       torchify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       torch.Tensor       )

    # Test not batched input
    UpperCamelCase__			    =    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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       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.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )