
"""simple docstring"""




import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


snake_case			       =		(3_0, 4_8_7)
snake_case			       =		(3_6, 1_0_2_4)
snake_case			       =		(1_0_0, 1_0_0)
snake_case			       =		1_0_0

snake_case			       =		datasets.Features(
    {'text': ArrayaD(SHAPE_TEST_1, dtype='float32'), 'image': ArrayaD(SHAPE_TEST_2, dtype='float32')}
)

snake_case       ,      snake_case			       =		os.path.split(__file__)
snake_case			       =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					with ArrowWriter(features=SCREAMING_SNAKE_CASE_,					path=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      )      ) as writer:
										for key, record in dummy_data:
															SCREAMING_SNAKE_CASE           =     my_features.encode_example(SCREAMING_SNAKE_CASE_      )
															writer.write(SCREAMING_SNAKE_CASE_      )
										SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     writer.finalize()

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					for _ in dataset:
										pass

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					dataset.set_format('numpy'      )
					for _ in dataset:
										pass

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     1_0
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     dataset[i : i + batch_size]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     1_0
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					dataset.set_format('numpy'      )
					for i in range(0,					len(SCREAMING_SNAKE_CASE_      ),					SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     dataset[i : i + batch_size]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					for col in feats:
										SCREAMING_SNAKE_CASE           =     dataset[col]

@get_duration
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     datasets.Dataset.from_file(
					    filename=os.path.join(SCREAMING_SNAKE_CASE_,					'beta.arrow'      ),					info=datasets.DatasetInfo(features=SCREAMING_SNAKE_CASE_      )      )
					dataset.set_format('numpy'      )
					for col in feats:
										SCREAMING_SNAKE_CASE           =     dataset[col]

def   UpperCamelCase_  (		):
					SCREAMING_SNAKE_CASE           =     {}
					SCREAMING_SNAKE_CASE           =     (
					    read_unformated,
					    read_formatted_as_numpy,
					    read_batch_unformated,
					    read_batch_formatted_as_numpy,
					    read_col_unformated,
					    read_col_formatted_as_numpy,
					)
					with tempfile.TemporaryDirectory() as tmp_dir:
										SCREAMING_SNAKE_CASE           =     datasets.Features({'image': ArrayaD(SCREAMING_SNAKE_CASE_,					dtype='float32'      )}      )
										SCREAMING_SNAKE_CASE           =     generate_examples(features=SCREAMING_SNAKE_CASE_,					num_examples=SCREAMING_SNAKE_CASE_      )
										SCREAMING_SNAKE_CASE           =     write(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										for read_func in read_functions:
															SCREAMING_SNAKE_CASE           =     read_func(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
										# )
										SCREAMING_SNAKE_CASE           =     datasets.Features({'image': datasets.Sequence(datasets.Sequence(datasets.Value('float32'      )      )      )}      )
										SCREAMING_SNAKE_CASE           =     generate_examples(
										    features=SCREAMING_SNAKE_CASE_,					num_examples=SCREAMING_SNAKE_CASE_,					seq_shapes={'image': SPEED_TEST_SHAPE}      )
										SCREAMING_SNAKE_CASE           =     write(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										for read_func in read_functions:
															SCREAMING_SNAKE_CASE           =     read_func(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					with tempfile.TemporaryDirectory() as tmp_dir:
										# don't use fixed length for fair comparison
										# feats = datasets.Features(
										#     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
										# )
										SCREAMING_SNAKE_CASE           =     datasets.Features({'image': datasets.Sequence(datasets.Value('float32'      )      )}      )
										SCREAMING_SNAKE_CASE           =     generate_examples(
										    features=SCREAMING_SNAKE_CASE_,					num_examples=SCREAMING_SNAKE_CASE_,					seq_shapes={'image': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]},					)
										SCREAMING_SNAKE_CASE           =     write(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										for read_func in read_functions:
															SCREAMING_SNAKE_CASE           =     read_func(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )

					with open(SCREAMING_SNAKE_CASE_,					'wb'      ) as f:
										f.write(json.dumps(SCREAMING_SNAKE_CASE_      ).encode('utf-8'      )      )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_array_xd()


"""simple docstring"""




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

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_      ):

					def remove_articles(SCREAMING_SNAKE_CASE_      ):
										return re.sub(R'\b(a|an|the)\b',					' ',					SCREAMING_SNAKE_CASE_      )

					def white_space_fix(SCREAMING_SNAKE_CASE_      ):
										return " ".join(text.split()      )

					def remove_punc(SCREAMING_SNAKE_CASE_      ):
										SCREAMING_SNAKE_CASE           =     set(string.punctuation      )
										return "".join(ch for ch in text if ch not in exclude      )

					def lower(SCREAMING_SNAKE_CASE_      ):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE_      )      )      )      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     normalize_answer(SCREAMING_SNAKE_CASE_      ).split()
					SCREAMING_SNAKE_CASE           =     Counter(SCREAMING_SNAKE_CASE_      ) & Counter(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     sum(common.values()      )
					if num_same == 0:
										return 0
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     1.0 * num_same / len(SCREAMING_SNAKE_CASE_      )
					SCREAMING_SNAKE_CASE           =     (2 * precision * recall) / (precision + recall)
					return fa

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					return normalize_answer(SCREAMING_SNAKE_CASE_      ) == normalize_answer(SCREAMING_SNAKE_CASE_      )

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      ):
					SCREAMING_SNAKE_CASE           =     []
					for ground_truth in ground_truths:
										SCREAMING_SNAKE_CASE           =     metric_fn(SCREAMING_SNAKE_CASE_,					SCREAMING_SNAKE_CASE_      )
										scores_for_ground_truths.append(SCREAMING_SNAKE_CASE_      )
					return max(SCREAMING_SNAKE_CASE_      )

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

					SCREAMING_SNAKE_CASE           =     100.0 * exact_match / total
					SCREAMING_SNAKE_CASE           =     100.0 * fa / total

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


if __name__ == "__main__":
	snake_case			       =		'1.1'
	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')
	snake_case			       =		parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		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,
			)
		snake_case			       =		dataset_json['data']
	with open(args.prediction_file) as prediction_file:
		snake_case			       =		json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))
