



import numpy as np
from PIL import Image




def 						UpperCAmelCase (    _lowerCamelCase     ):
			A							,			A							,			A				:       Optional[int]														=    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
			return 0.29_89 * r + 0.58_70 * g + 0.11_40 * b




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (gray > 127) & (gray <= 255)




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       Dict														=    np.zeros_like(_a     )
			A				:       Dict														=    np.zeros(
			    (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)     )

			# Copy image to padded image
			A				:       int														=    image

			# Iterate over image & apply kernel
			for x in range(image.shape[1]     ):
						for y in range(image.shape[0]     ):
									A				:       Optional[int]														=    (
									    kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
									).sum()
									A				:       Union[str, Any]														=    int(summation == 5     )
			return output


# kernel to be applied
__SCREAMING_SNAKE_CASE     =	np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
							# read original image
							__SCREAMING_SNAKE_CASE     =	np.array(Image.open(r"""..\image_data\lena.jpg"""))
							# Apply erosion operation to a binary image
							__SCREAMING_SNAKE_CASE     =	erosion(grayabinary(rgbagray(image)), structuring_element)
							# Save the output image
							__SCREAMING_SNAKE_CASE     =	Image.fromarray(output).convert("""RGB""")
							pil_img.save("""result_erosion.png""")





'''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}""")




