
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__":
					lowerCamelCase__        =						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)

					lowerCamelCase__ ,   lowerCamelCase__        =						parser.parse_known_args()

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

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

					# Load dataset
					lowerCamelCase__ ,   lowerCamelCase__        =						load_dataset("""imdb""", split=["""train""", """test"""])
					lowerCamelCase__        =						train_dataset.shuffle().select(range(5000))  # smaller the size for train dataset to 5k
					lowerCamelCase__        =						test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

					# Preprocess train dataset
					lowerCamelCase__        =						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"""])

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

					# Preprocess test dataset
					lowerCamelCase__        =						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"""])

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

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

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

lowerCamelCase__        =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	A__				:






				def __init__(		self   : Optional[int]       ,	a   : int  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[str]   =						name
							lowerCAmelCase__ : List[Any]   =						[]
							lowerCAmelCase__ : int   =						[]






				def 				_lowerCamelCase						(		self   : List[str]       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							self.inbound.append(a  )






				def 				_lowerCamelCase						(		self   : Dict       ,	a   : Union[str, Any]  ):






							'''simple docstring'''







							self.outbound.append(a  )






				def __repr__(		self   : Tuple  ):






							'''simple docstring'''







							return f'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=3			,				SCREAMING_SNAKE_CASE_=0.85	)							->						Optional[Any]:
			lowerCAmelCase__ : Tuple   =						{}
			for node in nodes:
						lowerCAmelCase__ : Union[str, Any]   =						1

			lowerCAmelCase__ : List[str]   =						{}
			for node in nodes:
						lowerCAmelCase__ : str   =						len(node.outbound	)

			for i in range(SCREAMING_SNAKE_CASE_	):
						print(F'''======= Iteration {i + 1} ======='''	)
						for _, node in enumerate(SCREAMING_SNAKE_CASE_	):
									lowerCAmelCase__ : Optional[int]   =						(1 - d) + d * sum(
									    ranks[ib] / outbounds[ib] for ib in node.inbound	)
						print(SCREAMING_SNAKE_CASE_	)
def 					lowerCAmelCase__     (    )							->						Dict:
			lowerCAmelCase__ : List[Any]   =						list(input('Enter Names of the Nodes: '	).split()	)

			lowerCAmelCase__ : Union[str, Any]   =						[Node(SCREAMING_SNAKE_CASE_	) for name in names]

			for ri, row in enumerate(SCREAMING_SNAKE_CASE_	):
						for ci, col in enumerate(SCREAMING_SNAKE_CASE_	):
									if col == 1:
												nodes[ci].add_inbound(names[ri]	)
												nodes[ri].add_outbound(names[ci]	)

			print('======= Nodes ======='	)
			for node in nodes:
						print(SCREAMING_SNAKE_CASE_	)

			page_rank(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
					main()