




'''simple docstring'''




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


snake_case__          =						logging.get_logger(__name__)


if is_vision_available():
							import PIL






class     UpperCamelCase_     (lowerCamelCase__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					['pixel_values']







						def __init__(     self      :       List[str]							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Dict[str, int] = None							, _lowerCamelCase      :       PILImageResampling = PILImageResampling.BICUBIC							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Dict[str, int] = None							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Union[int, float] = 1 / 255							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Optional[Union[float, List[float]]] = None							, _lowerCamelCase      :       Optional[Union[float, List[float]]] = None							, _lowerCamelCase      :       bool = True							, **_lowerCamelCase      :       Optional[Any]							, ):





											"""simple docstring"""







											super().__init__(**SCREAMING_SNAKE_CASE_    )
											A_ :     List[Any] =							size if size is not None else {'''shortest_edge''': 224}
											A_ :     Union[str, Any] =							get_size_dict(SCREAMING_SNAKE_CASE_							, default_to_square=SCREAMING_SNAKE_CASE_    )
											A_ :     Optional[Any] =							crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
											A_ :     int =							get_size_dict(SCREAMING_SNAKE_CASE_    )

											A_ :     Optional[int] =							do_resize
											A_ :     List[Any] =							size
											A_ :     Optional[int] =							resample
											A_ :     List[str] =							do_center_crop
											A_ :     Dict =							crop_size
											A_ :     List[str] =							do_rescale
											A_ :     List[str] =							rescale_factor
											A_ :     Optional[Any] =							do_normalize
											A_ :     List[Any] =							image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											A_ :     str =							image_std if image_std is not None else OPENAI_CLIP_STD
											A_ :     Optional[Any] =							do_convert_rgb







						def       _a    (     self      :       str							, _lowerCamelCase      :       np.ndarray							, _lowerCamelCase      :       Dict[str, int]							, _lowerCamelCase      :       PILImageResampling = PILImageResampling.BICUBIC							, _lowerCamelCase      :       Optional[Union[str, ChannelDimension]] = None							, **_lowerCamelCase      :       int							, ):





											"""simple docstring"""







											A_ :     List[Any] =							get_size_dict(SCREAMING_SNAKE_CASE_							, default_to_square=SCREAMING_SNAKE_CASE_    )
											A_ :     Tuple =							get_resize_output_image_size(
											    SCREAMING_SNAKE_CASE_							, size=(size['''height'''], size['''width'''])							, default_to_square=SCREAMING_SNAKE_CASE_    )
											return resize(SCREAMING_SNAKE_CASE_							, size=SCREAMING_SNAKE_CASE_							, resample=SCREAMING_SNAKE_CASE_							, data_format=SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_    )







						def       _a    (     self      :       Any							, _lowerCamelCase      :       np.ndarray							, _lowerCamelCase      :       Dict[str, int]							, _lowerCamelCase      :       Optional[Union[str, ChannelDimension]] = None							, **_lowerCamelCase      :       Dict							, ):





											"""simple docstring"""







											A_ :     List[Any] =							get_size_dict(SCREAMING_SNAKE_CASE_    )
											return center_crop(SCREAMING_SNAKE_CASE_							, size=(size['''height'''], size['''width'''])							, data_format=SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_    )







						def       _a    (     self      :       str							, _lowerCamelCase      :       np.ndarray							, _lowerCamelCase      :       Union[int, float]							, _lowerCamelCase      :       Optional[Union[str, ChannelDimension]] = None							, **_lowerCamelCase      :       Dict							, ):





											"""simple docstring"""







											return rescale(SCREAMING_SNAKE_CASE_							, scale=SCREAMING_SNAKE_CASE_							, data_format=SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_    )







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       np.ndarray							, _lowerCamelCase      :       Union[float, List[float]]							, _lowerCamelCase      :       Union[float, List[float]]							, _lowerCamelCase      :       Optional[Union[str, ChannelDimension]] = None							, **_lowerCamelCase      :       Union[str, Any]							, ):





											"""simple docstring"""







											return normalize(SCREAMING_SNAKE_CASE_							, mean=SCREAMING_SNAKE_CASE_							, std=SCREAMING_SNAKE_CASE_							, data_format=SCREAMING_SNAKE_CASE_							, **SCREAMING_SNAKE_CASE_    )







						def       _a    (     self      :       Union[str, Any]							, _lowerCamelCase      :       ImageInput							, _lowerCamelCase      :       bool = None							, _lowerCamelCase      :       Dict[str, int] = None							, _lowerCamelCase      :       PILImageResampling = None							, _lowerCamelCase      :       bool = None							, _lowerCamelCase      :       int = None							, _lowerCamelCase      :       bool = None							, _lowerCamelCase      :       float = None							, _lowerCamelCase      :       bool = None							, _lowerCamelCase      :       Optional[Union[float, List[float]]] = None							, _lowerCamelCase      :       Optional[Union[float, List[float]]] = None							, _lowerCamelCase      :       bool = None							, _lowerCamelCase      :       Optional[Union[str, TensorType]] = None							, _lowerCamelCase      :       Optional[ChannelDimension] = ChannelDimension.FIRST							, **_lowerCamelCase      :       Any							, ):





											"""simple docstring"""







											A_ :     List[Any] =							do_resize if do_resize is not None else self.do_resize
											A_ :     Optional[Any] =							size if size is not None else self.size
											A_ :     Tuple =							get_size_dict(SCREAMING_SNAKE_CASE_							, default_to_square=SCREAMING_SNAKE_CASE_    )
											A_ :     Dict =							resample if resample is not None else self.resample
											A_ :     List[Any] =							do_center_crop if do_center_crop is not None else self.do_center_crop
											A_ :     Any =							crop_size if crop_size is not None else self.crop_size
											A_ :     Any =							get_size_dict(SCREAMING_SNAKE_CASE_    )
											A_ :     str =							do_rescale if do_rescale is not None else self.do_rescale
											A_ :     List[str] =							rescale_factor if rescale_factor is not None else self.rescale_factor
											A_ :     Dict =							do_normalize if do_normalize is not None else self.do_normalize
											A_ :     Tuple =							image_mean if image_mean is not None else self.image_mean
											A_ :     Tuple =							image_std if image_std is not None else self.image_std
											A_ :     Dict =							do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											A_ :     Tuple =							make_list_of_images(SCREAMING_SNAKE_CASE_    )

											if not valid_images(SCREAMING_SNAKE_CASE_    ):
																raise ValueError(
																    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
																    '''torch.Tensor, tf.Tensor or jax.ndarray.'''    )

											if do_resize and size is None:
																raise ValueError('''Size must be specified if do_resize is True.'''    )

											if do_center_crop and crop_size is None:
																raise ValueError('''Crop size must be specified if do_center_crop is True.'''    )

											if do_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

											if do_normalize and (image_mean is None or image_std is None):
																raise ValueError('''Image mean and std must be specified if do_normalize is True.'''    )

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																A_ :     List[str] =							[convert_to_rgb(SCREAMING_SNAKE_CASE_    ) for image in images]

											# All transformations expect numpy arrays.
											A_ :     Tuple =							[to_numpy_array(SCREAMING_SNAKE_CASE_    ) for image in images]

											if do_resize:
																A_ :     Tuple =							[self.resize(image=SCREAMING_SNAKE_CASE_							, size=SCREAMING_SNAKE_CASE_							, resample=SCREAMING_SNAKE_CASE_    ) for image in images]

											if do_center_crop:
																A_ :     List[Any] =							[self.center_crop(image=SCREAMING_SNAKE_CASE_							, size=SCREAMING_SNAKE_CASE_    ) for image in images]

											if do_rescale:
																A_ :     Optional[Any] =							[self.rescale(image=SCREAMING_SNAKE_CASE_							, scale=SCREAMING_SNAKE_CASE_    ) for image in images]

											if do_normalize:
																A_ :     Optional[Any] =							[self.normalize(image=SCREAMING_SNAKE_CASE_							, mean=SCREAMING_SNAKE_CASE_							, std=SCREAMING_SNAKE_CASE_    ) for image in images]

											A_ :     Union[str, Any] =							[to_channel_dimension_format(SCREAMING_SNAKE_CASE_							, SCREAMING_SNAKE_CASE_    ) for image in images]

											A_ :     Union[str, Any] =							{'''pixel_values''': images}
											return BatchFeature(data=SCREAMING_SNAKE_CASE_							, tensor_type=SCREAMING_SNAKE_CASE_    )









'''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=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__          =						parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							snake_case__          =						16
							snake_case__          =						16

							# 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(50_00))  # smaller the size for train dataset to 5k
							snake_case__          =						test_dataset.shuffle().select(range(5_00))  # 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}')



