




'''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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case__          =						logging.get_logger(__name__)






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						_lowerCAmelCase    						=					['pixel_values']







						def __init__(     self      :       str							, _lowerCamelCase      :       bool = True							, _lowerCamelCase      :       Optional[Dict[str, int]] = None							, _lowerCamelCase      :       PILImageResampling = PILImageResampling.BILINEAR							, _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      :       str							, ):





											"""simple docstring"""







											super().__init__(**_lowerCamelCase    )
											A_ :     Tuple =							size if size is not None else {'''height''': 224, '''width''': 224}
											A_ :     Any =							get_size_dict(_lowerCamelCase    )
											A_ :     Optional[Any] =							do_resize
											A_ :     str =							do_rescale
											A_ :     Any =							do_normalize
											A_ :     Optional[Any] =							size
											A_ :     Optional[Any] =							resample
											A_ :     Union[str, Any] =							rescale_factor
											A_ :     Union[str, Any] =							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											A_ :     Union[str, Any] =							image_std if image_std is not None else IMAGENET_STANDARD_STD







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





											"""simple docstring"""







											A_ :     Dict =							get_size_dict(_lowerCamelCase    )
											if "height" not in size or "width" not in size:
																raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'    )
											return resize(
											    _lowerCamelCase							, size=(size['''height'''], size['''width'''])							, resample=_lowerCamelCase							, data_format=_lowerCamelCase							, **_lowerCamelCase    )







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





											"""simple docstring"""







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







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





											"""simple docstring"""







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







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





											"""simple docstring"""







											A_ :     List[str] =							do_resize if do_resize is not None else self.do_resize
											A_ :     Any =							do_rescale if do_rescale is not None else self.do_rescale
											A_ :     List[str] =							do_normalize if do_normalize is not None else self.do_normalize
											A_ :     int =							resample if resample is not None else self.resample
											A_ :     List[str] =							rescale_factor if rescale_factor is not None else self.rescale_factor
											A_ :     Optional[int] =							image_mean if image_mean is not None else self.image_mean
											A_ :     Any =							image_std if image_std is not None else self.image_std

											A_ :     Tuple =							size if size is not None else self.size
											A_ :     int =							get_size_dict(_lowerCamelCase    )

											A_ :     Dict =							make_list_of_images(_lowerCamelCase    )

											if not valid_images(_lowerCamelCase    ):
																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_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''    )

											# All transformations expect numpy arrays.
											A_ :     List[Any] =							[to_numpy_array(_lowerCamelCase    ) for image in images]

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

											if do_rescale:
																A_ :     Union[str, Any] =							[self.rescale(image=_lowerCamelCase							, scale=_lowerCamelCase    ) for image in images]

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

											A_ :     Dict =							[to_channel_dimension_format(_lowerCamelCase							, _lowerCamelCase    ) for image in images]

											A_ :     str =							{'''pixel_values''': images}
											return BatchFeature(data=_lowerCamelCase							, tensor_type=_lowerCamelCase    )









'''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}')



