



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


A_		:  Optional[int]										=  logging.get_logger(__name__)



class 		_lowercase		(							UpperCAmelCase__						):
   _UpperCAmelCase  						=      ['''pixel_values''']

   def __init__(   self      :							Optional[int]				, __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      :							Any				, )							->					None:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase				)
    a							=		size if size is not None else {"height": 224, "width": 224}
    a							=		get_size_dict(__lowerCAmelCase				)
    a							=		do_resize
    a							=		do_rescale
    a							=		do_normalize
    a							=		size
    a							=		resample
    a							=		rescale_factor
    a							=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    a							=		image_std if image_std is not None else IMAGENET_STANDARD_STD

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							Dict[str, int]				, __lowerCAmelCase      :							PILImageResampling = PILImageResampling.BILINEAR				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							int				, )							->					np.ndarray:


    """simple docstring"""






    a							=		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      :							Any				, __lowerCAmelCase      :							np.ndarray				, __lowerCAmelCase      :							float				, __lowerCAmelCase      :							Optional[Union[str, ChannelDimension]] = None				, **__lowerCAmelCase      :							Tuple				)							->					np.ndarray:


    """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      :							str				, )							->					np.ndarray:


    """simple docstring"""






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




   def 		A				(   self      :							str				, __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      :							Optional[int]				, )							->					List[Any]:


    """simple docstring"""






    a							=		do_resize if do_resize is not None else self.do_resize
    a							=		do_rescale if do_rescale is not None else self.do_rescale
    a							=		do_normalize if do_normalize is not None else self.do_normalize
    a							=		resample if resample is not None else self.resample
    a							=		rescale_factor if rescale_factor is not None else self.rescale_factor
    a							=		image_mean if image_mean is not None else self.image_mean
    a							=		image_std if image_std is not None else self.image_std

    a							=		size if size is not None else self.size
    a							=		get_size_dict(__lowerCAmelCase				)

    a							=		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							=		[to_numpy_array(__lowerCAmelCase				) for image in images]

    if do_resize:
     a							=		[self.resize(image=__lowerCAmelCase				, size=__lowerCAmelCase				, resample=__lowerCAmelCase				) for image in images]

    if do_rescale:
     a							=		[self.rescale(image=__lowerCAmelCase				, scale=__lowerCAmelCase				) for image in images]

    if do_normalize:
     a							=		[self.normalize(image=__lowerCAmelCase				, mean=__lowerCAmelCase				, std=__lowerCAmelCase				) for image in images]

    a							=		[to_channel_dimension_format(__lowerCAmelCase				, __lowerCAmelCase				) for image in images]

    a							=		{"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__":
   A					       =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					       =parser.parse_known_args()

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

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

   # Load dataset
   A       , A					       =load_dataset('imdb', split=['train', 'test'])
   A					       =train_dataset.shuffle().select(range(50_00))  # smaller the size for train dataset to 5k
   A					       =test_dataset.shuffle().select(range(5_00))  # smaller the size for test dataset to 500

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

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

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

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




