


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__":
					UpperCamelCase			:					Optional[Any]         =							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)

					UpperCamelCase     ,UpperCamelCase			:					Tuple         =							parser.parse_known_args()

					# overwrite batch size until we have tf_glue.py
					UpperCamelCase			:					Optional[int]         =							16
					UpperCamelCase			:					Tuple         =							16

					# Set up logging
					UpperCamelCase			:					Dict         =							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
					UpperCamelCase			:					int         =							TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
					UpperCamelCase			:					int         =							AutoTokenizer.from_pretrained(args.model_name_or_path)

					# Load dataset
					UpperCamelCase     ,UpperCamelCase			:					Tuple         =							load_dataset('''imdb''', split=['''train''', '''test'''])
					UpperCamelCase			:					Optional[Any]         =							train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
					UpperCamelCase			:					int         =							test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

					# Preprocess train dataset
					UpperCamelCase			:					Optional[Any]         =							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'''])

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

					# Preprocess test dataset
					UpperCamelCase			:					Optional[int]         =							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'''])

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

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

					UpperCamelCase			:					List[str]         =							time.time()
					UpperCamelCase			:					str         =							model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
					UpperCamelCase			:					Dict         =							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'''







from __future__ import annotations





def 		snake_case_				(_a      :				list[list[int]]       ,  _a      :				list[list[int]]	):
    assert len(_a	) == len(_a	)

    UpperCAmelCase     			=						len(_a	)
    UpperCAmelCase     			=						list(range(_a	)	)
    UpperCAmelCase     			=						[-1] * n  # who the donor has donated to
    UpperCAmelCase     			=						[-1] * n  # who the recipient has received from
    UpperCAmelCase     			=						[0] * n

    while unmatched_donors:
        UpperCAmelCase     			=						unmatched_donors[0]
        UpperCAmelCase     			=						donor_pref[donor]
        UpperCAmelCase     			=						donor_preference[num_donations[donor]]
        num_donations[donor] += 1
        UpperCAmelCase     			=						recipient_pref[recipient]
        UpperCAmelCase     			=						rec_record[recipient]

        if prev_donor != -1:
            if rec_preference.index(_a	) > rec_preference.index(_a	):
                UpperCAmelCase     			=						donor
                UpperCAmelCase     			=						recipient
                unmatched_donors.append(_a	)
                unmatched_donors.remove(_a	)
        else:
            UpperCAmelCase     			=						donor
            UpperCAmelCase     			=						recipient
            unmatched_donors.remove(_a	)
    return donor_record




