"""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(5000))  # smaller the size for train dataset to 5k
	a							=test_dataset.shuffle().select(range(500))  # 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}''')



"""simple docstring"""


import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


a							=TypeVar('T', bound='TaskTemplate')






@dataclass(frozen=__lowerCAmelCase	)
class    __UpperCAmelCase						(	abc.ABC	):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				A__      :   str
				A__      :   ClassVar[Features]
				A__      :   ClassVar[Features]

				def 		_a	(      self	, _lowerCamelCase      ):
										return copy.deepcopy(self      )

				@property
				def 		_a	(      self      ):
										return Features(**self.input_schema	, **self.label_schema      )

				@property
				@abc.abstractmethod
				def 		_a	(      self      ):
										raise NotImplementedError






				@classmethod
				def 		_a	(      cls	, _lowerCamelCase      ):
										lowerCamelCase__					={f.name for f in dataclasses.fields(cls      )}
										return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )


