


import argparse
import json
import re
import string
import sys
from collections import Counter




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	def remove_articles(__lowerCamelCase:		List[str]  ):
		return re.sub(r"\b(a|an|the)\b"  ,			" "  ,			__lowerCamelCase  )

	def white_space_fix(__lowerCamelCase:		Tuple  ):
		return " ".join(text.split()  )

	def remove_punc(__lowerCamelCase:		Union[str, Any]  ):
		lowercase_			 = set(string.punctuation  )
		return "".join(ch for ch in text if ch not in exclude  )

	def lower(__lowerCamelCase:		Optional[Any]  ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(__lowerCamelCase  )  )  )  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Optional[int]  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_			 = normalize_answer(__lowerCamelCase  ).split()
	lowercase_			 = normalize_answer(__lowerCamelCase  ).split()
	lowercase_			 = Counter(__lowerCamelCase  ) & Counter(__lowerCamelCase  )
	lowercase_			 = sum(common.values()  )
	if num_same == 0:
		return 0
	lowercase_			 = 1.0 * num_same / len(__lowerCamelCase  )
	lowercase_			 = 1.0 * num_same / len(__lowerCamelCase  )
	lowercase_			 = (2 * precision * recall) / (precision + recall)
	return fa




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Union[str, Any]  ,			__lowerCamelCase:		int  ):







	'''simple docstring'''



	return normalize_answer(__lowerCamelCase  ) == normalize_answer(__lowerCamelCase  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		Any  ,			__lowerCamelCase:		str  ,			__lowerCamelCase:		Optional[int]  ):







	'''simple docstring'''



	lowercase_			 = []
	for ground_truth in ground_truths:
		lowercase_			 = metric_fn(__lowerCamelCase  ,			__lowerCamelCase  )
		scores_for_ground_truths.append(__lowerCamelCase  )
	return max(__lowerCamelCase  )




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		str  ,			__lowerCamelCase:		List[str]  ):







	'''simple docstring'''



	lowercase_			 = lowercase_			 = lowercase_			 = 0
	for article in dataset:
		for paragraph in article["paragraphs"]:
			for qa in paragraph["qas"]:
				total += 1
				if qa["id"] not in predictions:
					lowercase_			 = "Unanswered question " + qa["id"] + " will receive score 0."
					print(__lowerCamelCase  ,			file=sys.stderr  )
					continue
				lowercase_			 = [x["text"] for x in qa["answers"]]
				lowercase_			 = predictions[qa["id"]]
				exact_match += metric_max_over_ground_truths(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  )
				fa += metric_max_over_ground_truths(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  )

	lowercase_			 = 100.0 * exact_match / total
	lowercase_			 = 100.0 * fa / total

	return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__       			=				"""1.1"""
							SCREAMING_SNAKE_CASE__       			=				argparse.ArgumentParser(description="""Evaluation for SQuAD """ + expected_version)
							parser.add_argument("""dataset_file""", help="""Dataset file""")
							parser.add_argument("""prediction_file""", help="""Prediction File""")
							SCREAMING_SNAKE_CASE__       			=				parser.parse_args()
							with open(args.dataset_file) as dataset_file:
														SCREAMING_SNAKE_CASE__       			=				json.load(dataset_file)
														if dataset_json["version"] != expected_version:
																					print(
																					    """Evaluation expects v-""" + expected_version + """, but got dataset with v-""" + dataset_json["""version"""],
																					    file=sys.stderr,
																					)
														SCREAMING_SNAKE_CASE__       			=				dataset_json["""data"""]
							with open(args.prediction_file) as prediction_file:
														SCREAMING_SNAKE_CASE__       			=				json.load(prediction_file)
							print(json.dumps(evaluate(dataset, predictions)))





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__":
							SCREAMING_SNAKE_CASE__       			=				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=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)

							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				parser.parse_known_args()

							# overwrite batch size until we have tf_glue.py
							SCREAMING_SNAKE_CASE__       			=				1_6
							SCREAMING_SNAKE_CASE__       			=				1_6

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

							# Load dataset
							SCREAMING_SNAKE_CASE__			,      SCREAMING_SNAKE_CASE__       			=				load_dataset("""imdb""", split=["""train""", """test"""])
							SCREAMING_SNAKE_CASE__       			=				train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
							SCREAMING_SNAKE_CASE__       			=				test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

							# Preprocess train dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

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

							# Preprocess test dataset
							SCREAMING_SNAKE_CASE__       			=				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"""])

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

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

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

