



'''simple docstring'''

import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get('''SDP_ENABLED''') or is_sagemaker_dp_enabled():
		_A					:     Dict      =True
		_A					:     int      ='''p3dn.24xlarge'''
		import smdistributed.dataparallel.tensorflow as sdp
else:
		_A					:     Any      =False







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase,					_lowercase=None       )			->      List[str]:
	_lowercase       :							int  	=		tqdm(_lowercase       )
	for i, batch in enumerate(_lowercase       ):
		with tf.GradientTape() as tape:
			_lowercase	,     _lowercase       :							List[Any]  	=		batch
			_lowercase       :							Optional[int]  	=		model(_lowercase       )
			_lowercase       :							Dict  	=		loss(_lowercase,					outputs.logits       )

		if SDP_ENABLED:
			_lowercase       :							Tuple  	=		sdp.DistributedGradientTape(_lowercase,					sparse_as_dense=_lowercase       )

		_lowercase       :							Tuple  	=		tape.gradient(_lowercase,					model.trainable_variables       )
		opt.apply_gradients(zip(_lowercase,					model.trainable_variables       )       )

		pbar.set_description(f'''Loss: {loss_value:.4f}'''       )

		if SDP_ENABLED and i == 0:
			sdp.broadcast_variables(model.variables,					root_rank=0       )
			sdp.broadcast_variables(opt.variables(),					root_rank=0       )

		if max_steps and i >= max_steps:
			break

	_lowercase       :							str  	=		{'loss': loss_value.numpy()}
	return train_results







def     __UpperCamelCase					(				_lowercase,					_lowercase,					_lowercase       )			->      List[Any]:
	# Load dataset
	_lowercase	,     _lowercase       :							Optional[Any]  	=		load_dataset('imdb',					split=['train', 'test']       )

	# Preprocess train dataset
	_lowercase       :							Tuple  	=		train_dataset.map(
	    lambda _lowercase       : tokenizer(e['text'],					truncation=_lowercase,					padding='max_length'       ),					batched=_lowercase       )
	train_dataset.set_format(type='tensorflow',					columns=['input_ids', 'attention_mask', 'label']       )

	_lowercase       :							Tuple  	=		{
	    x: train_dataset[x].to_tensor(default_value=0,					shape=[None, tokenizer.model_max_length]       )
	    for x in ['input_ids', 'attention_mask']
	}
	_lowercase       :							List[Any]  	=		tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])       )

	# Preprocess test dataset
	_lowercase       :							int  	=		test_dataset.map(
	    lambda _lowercase       : tokenizer(e['text'],					truncation=_lowercase,					padding='max_length'       ),					batched=_lowercase       )
	test_dataset.set_format(type='tensorflow',					columns=['input_ids', 'attention_mask', 'label']       )

	_lowercase       :							Any  	=		{
	    x: test_dataset[x].to_tensor(default_value=0,					shape=[None, tokenizer.model_max_length]       )
	    for x in ['input_ids', 'attention_mask']
	}
	_lowercase       :							List[str]  	=		tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])       )

	if SDP_ENABLED:
		_lowercase       :							Dict  	=		tf_train_dataset.shard(sdp.size(),					sdp.rank()       )
		_lowercase       :							str  	=		tf_test_dataset.shard(sdp.size(),					sdp.rank()       )
	_lowercase       :							Union[str, Any]  	=		tf_train_dataset.batch(_lowercase,					drop_remainder=_lowercase       )
	_lowercase       :							List[Any]  	=		tf_test_dataset.batch(_lowercase,					drop_remainder=_lowercase       )

	return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
		_A					:     List[Any]      =argparse.ArgumentParser()

		# Hyperparameters sent by the client are passed as command-line arguments to the script.
		parser.add_argument('''--epochs''', type=int, default=3)
		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)
		parser.add_argument('''--max_steps''', type=int, default=None)

		# Data, model, and output directories
		parser.add_argument('''--output_data_dir''', type=str, default=os.environ['''SM_OUTPUT_DATA_DIR'''])
		parser.add_argument('''--model_dir''', type=str, default=os.environ['''SM_MODEL_DIR'''])
		parser.add_argument('''--n_gpus''', type=str, default=os.environ['''SM_NUM_GPUS'''])

		_A    ,      _A					:     int      =parser.parse_known_args()

		# Set up logging
		_A					:     Tuple      =logging.getLogger(__name__)

		logging.basicConfig(
		    level=logging.getLevelName('''INFO'''),
		    handlers=[logging.StreamHandler(sys.stdout)],
		    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
		)

		if SDP_ENABLED:
				sdp.init()

				_A					:     Optional[Any]      =tf.config.experimental.list_physical_devices('''GPU''')
				for gpu in gpus:
						tf.config.experimental.set_memory_growth(gpu, True)
				if gpus:
						tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], '''GPU''')

    # Load model and tokenizer
		_A					:     Dict      =TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		_A					:     Any      =AutoTokenizer.from_pretrained(args.model_name_or_path)

		# get datasets
		_A    ,      _A					:     Tuple      =get_datasets(
		    tokenizer=tokenizer,
		    train_batch_size=args.per_device_train_batch_size,
		    eval_batch_size=args.per_device_eval_batch_size,
		)

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

		# Training
		if args.do_train:
				# train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
				_A					:     List[Any]      =time.time()
				_A					:     Union[str, Any]      =fit(
				    model,
				    loss,
				    optimizer,
				    tf_train_dataset,
				    args.epochs,
				    args.per_device_train_batch_size,
				    max_steps=args.max_steps,
				)
				_A					:     Optional[Any]      =time.time() - start_train_time
				logger.info('''*** Train ***''')
				logger.info(F'''train_runtime = {end_train_time}''')

				_A					:     str      =os.path.join(args.output_dir, '''train_results.txt''')

				if not SDP_ENABLED or sdp.rank() == 0:
						with open(output_eval_file, '''w''') as writer:
								logger.info('''***** Train results *****''')
								logger.info(train_results)
								for key, value in train_results.items():
										logger.info(F'''  {key} = {value}''')
										writer.write(F'''{key} = {value}\n''')

    # Evaluation
		if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
				_A					:     Optional[int]      =model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
				logger.info('''*** Evaluate ***''')

				_A					:     str      =os.path.join(args.output_dir, '''eval_results.txt''')

				with open(output_eval_file, '''w''') as writer:
						logger.info('''***** Eval results *****''')
						logger.info(result)
						for key, value in result.items():
								logger.info(F'''  {key} = {value}''')
								writer.write(F'''{key} = {value}\n''')

    # Save result
		if SDP_ENABLED:
				if sdp.rank() == 0:
						model.save_pretrained(args.output_dir)
						tokenizer.save_pretrained(args.output_dir)
		else:
				model.save_pretrained(args.output_dir)
				tokenizer.save_pretrained(args.output_dir)





'''simple docstring'''

import bsa
import requests







def     __UpperCamelCase					(				_lowercase       )			->      dict[str, str]:
	return {
	    "name": soup.ha.a.text,
	    "genre": soup.find('span',					class_='genre'       ).text.strip(),
	    "rating": soup.strong.text,
	    "page_link": f'''https://www.imdb.com{soup.a.get("href"       )}''',
	}







def     __UpperCamelCase					(				_lowercase = 5       )			->      tuple:
	_lowercase       :							List[Any]  	=		int(float(_lowercase       )       )
	if num_movies < 1:
		return ()
	_lowercase       :							Any  	=		(
	    'https://www.imdb.com/search/title?title_type='
	    f'''feature&sort=num_votes,desc&count={num_movies}'''
	)
	_lowercase       :							Tuple  	=		bsa.BeautifulSoup(requests.get(_lowercase       ).content,					'html.parser'       )
	return tuple(
	    get_movie_data_from_soup(_lowercase       )
	    for movie in source.find_all('div',					class_='lister-item mode-advanced'       )       )


if __name__ == "__main__":
		import json

		_A					:     Optional[int]      =int(input('''How many movies would you like to see? '''))
		print(
		    ''', '''.join(
		        json.dumps(movie, indent=4) for movie in get_imdb_top_movies(num_movies)
		    )
		)
