
"""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, 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, is_vision_available, logging


if is_vision_available():
    import PIL


_a   :      Any       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       ["""pixel_values"""]
    def __init__(     self    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = False    ,     snake_case_ = None    ,     snake_case_ = 1 / 2_55    ,     snake_case_ = False    ,     snake_case_ = True    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = True    ,     **snake_case_    ,     ):
      super().__init__(**snake_case_  )
      _snake_case  :    Tuple            = size if size is not None else {"height": 3_46, "width": 3_46}
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    List[str]            = crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
      _snake_case  :    List[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    Any            = do_resize
      _snake_case  :    Optional[int]            = size
      _snake_case  :    List[str]            = resample
      _snake_case  :    Union[str, Any]            = do_center_crop
      _snake_case  :    str            = crop_size
      _snake_case  :    str            = do_rescale
      _snake_case  :    Tuple            = rescale_factor
      _snake_case  :    Optional[Any]            = rescale_offset
      _snake_case  :    str            = do_normalize
      _snake_case  :    Dict            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _snake_case  :    Optional[Any]            = image_std if image_std is not None else IMAGENET_STANDARD_STD
      _snake_case  :    Dict            = include_top
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return resize(
          snake_case_    ,     size=(size["height"], size["width"])    ,     resample=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    int            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return center_crop(snake_case_    ,     size=(size["height"], size["width"])    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = True    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      if offset:
        _snake_case  :    Optional[int]            = (image - 127.5) * scale
        if data_format is not None:
          _snake_case  :    Union[str, Any]            = to_channel_dimension_format(snake_case_    ,     snake_case_  )
        _snake_case  :    Tuple            = rescaled_image.astype(np.floataa  )
      else:
        _snake_case  :    Optional[int]            = rescale(snake_case_    ,     scale=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
      return rescaled_image
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      return normalize(snake_case_    ,     mean=snake_case_    ,     std=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )


    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_=None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = ChannelDimension.FIRST    ,     **snake_case_    ,     ):
      _snake_case  :    Dict            = do_resize if do_resize is not None else self.do_resize
      _snake_case  :    Tuple            = resample if resample is not None else self.resample
      _snake_case  :    Any            = do_center_crop if do_center_crop is not None else self.do_center_crop
      _snake_case  :    str            = do_rescale if do_rescale is not None else self.do_rescale
      _snake_case  :    int            = rescale_factor if rescale_factor is not None else self.rescale_factor
      _snake_case  :    Any            = rescale_offset if rescale_offset is not None else self.rescale_offset
      _snake_case  :    Tuple            = do_normalize if do_normalize is not None else self.do_normalize
      _snake_case  :    str            = image_mean if image_mean is not None else self.image_mean
      _snake_case  :    Any            = image_std if image_std is not None else self.image_std
      _snake_case  :    Any            = include_top if include_top is not None else self.include_top

      _snake_case  :    List[str]            = size if size is not None else self.size
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    str            = crop_size if crop_size is not None else self.crop_size
      _snake_case  :    Optional[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    List[str]            = make_list_of_images(snake_case_  )

      if not valid_images(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 or resample is None:
        raise ValueError("Size and resample 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."  )

      # All transformations expect numpy arrays.
      _snake_case  :    str            = [to_numpy_array(snake_case_  ) for image in images]

      if do_resize:
        _snake_case  :    List[str]            = [self.resize(image=snake_case_    ,     size=snake_case_    ,     resample=snake_case_  ) for image in images]

      if do_center_crop:
        _snake_case  :    Optional[Any]            = [self.center_crop(image=snake_case_    ,     size=snake_case_  ) for image in images]

      if do_rescale:
        _snake_case  :    Tuple            = [self.rescale(image=snake_case_    ,     scale=snake_case_    ,     offset=snake_case_  ) for image in images]

      if do_normalize:
        _snake_case  :    str            = [self.normalize(image=snake_case_    ,     mean=snake_case_    ,     std=snake_case_  ) for image in images]

      if include_top:
        _snake_case  :    Optional[int]            = [self.normalize(image=snake_case_    ,     mean=[0, 0, 0]    ,     std=snake_case_  ) for image in images]

      _snake_case  :    Union[str, Any]            = [to_channel_dimension_format(snake_case_    ,     snake_case_  ) for image in images]

      _snake_case  :    Any            = {"pixel_values": images}
      return BatchFeature(data=snake_case_    ,     tensor_type=snake_case_  )





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__":
							SCREAMING_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=1_6)
							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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

							# Set up logging
							SCREAMING_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
							SCREAMING_SNAKE_CASE__       			=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
							SCREAMING_SNAKE_CASE__       			=				AutoTokenizer.from_pretrained(args.model_name_or_path)

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_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"""])

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

							# Preprocess test dataset
							SCREAMING_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"""])

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

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

							SCREAMING_SNAKE_CASE__       			=				time.time()
							SCREAMING_SNAKE_CASE__       			=				model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
							SCREAMING_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}""")

