'''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__	:     Tuple      =    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__	:     List[str]      =    parser.parse_known_args()

 # overwrite batch size until we have tf_glue.py
 snake_case__	:     Union[str, Any]      =    16
 snake_case__	:     Optional[Any]      =    16

 # Set up logging
 snake_case__	:     Optional[int]      =    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__	:     int      =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 snake_case__	:     str      =    AutoTokenizer.from_pretrained(args.model_name_or_path)

 # Load dataset
 snake_case__     , snake_case__	:     List[str]      =    load_dataset('''imdb''', split=['''train''', '''test'''])
 snake_case__	:     Union[str, Any]      =    train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
 snake_case__	:     Tuple      =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

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

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

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

 snake_case__	:     Union[str, Any]      =    time.time()
 snake_case__	:     Optional[int]      =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
 snake_case__	:     Optional[int]      =    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}''')














import random




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									__UpperCamelCase :   Optional[Any]									=x
									__UpperCamelCase :   int									=y


				def  __lowercase				(					self       ):




									"""simple docstring"""

									return (self.x**2 + self.y**2) <= 1







				@classmethod
				def  __lowercase				(					cls       ):




									"""simple docstring"""

									return cls(x=random.random()				,    y=random.random()       )






def 				A	(			a_   )	->		float:
					if number_of_simulations < 1:
										raise ValueError('At least one simulation is necessary to estimate PI.'   )

					__UpperCamelCase :   List[str]									=0
					for _ in range(a_   ):
										__UpperCamelCase :   Optional[int]									=Point.random_unit_square()

										if random_point.is_in_unit_circle():
															number_in_unit_circle += 1

					return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
							# import doctest

							# doctest.testmod()
							from math import pi

							A_			:Optional[int]	    =		'''Please enter the desired number of Monte Carlo simulations: '''
							A_			:int	    =		estimate_pi(int(input(prompt).strip()))
							print(f"An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}")

