



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							=       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							=       parser.parse_known_args()

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

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

	# Load dataset
	UpperCAmelCase			,							UpperCAmelCase							=       load_dataset("""imdb""", split=["""train""", """test"""])
	UpperCAmelCase							=       train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
	UpperCAmelCase							=       test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

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

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

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

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






from collections import defaultdict

def       A_   (						__a  :					str		):

				"""simple docstring"""
				a__					   =		defaultdict(__a		)
				# Creating a dictionary containing count of each word
				for word in sentence.split():
								occurrence[word] += 1
				return occurrence


if __name__ == "__main__":
	for word, count in word_occurrence("""INPUT STRING""").items():
		print(f"""{word}: {count}""")

