





'''simple docstring'''







import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						str:
						UpperCAmelCase_      :   Tuple      =	tmp_path / '''text.txt'''
						UpperCAmelCase_      :   Optional[int]      =	textwrap.dedent(
						    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''' )
						with open(SCREAMING_SNAKE_CASE__,      '''w''',      encoding='''utf-8''' ) as f:
												f.write(SCREAMING_SNAKE_CASE__ )
						return str(SCREAMING_SNAKE_CASE__ )





@pytest.fixture
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Tuple,      SCREAMING_SNAKE_CASE__   :      List[str] )			->						Any:
						UpperCAmelCase_      :   Tuple      =	tmp_path / '''text_with_image.txt'''
						with open(SCREAMING_SNAKE_CASE__,      '''w''',      encoding='''utf-8''' ) as f:
												f.write(SCREAMING_SNAKE_CASE__ )
						return str(SCREAMING_SNAKE_CASE__ )





@pytest.mark.parametrize('''keep_linebreaks''',      [True, False] )
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Dict,      SCREAMING_SNAKE_CASE__   :      str )			->						int:
						with open(SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' ) as f:
												UpperCAmelCase_      :   Tuple      =	f.read().splitlines(keepends=SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	Text(keep_linebreaks=SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' )
						UpperCAmelCase_      :   Optional[Any]      =	text._generate_tables([[text_file]] )
						UpperCAmelCase_      :   Optional[Any]      =	pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
						assert generated_content == expected_content





@require_pil
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[Any] )			->						str:
						with open(SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' ) as f:
												UpperCAmelCase_      :   Tuple      =	f.read().splitlines()[0]
						UpperCAmelCase_      :   Optional[int]      =	Text(encoding='''utf-8''',      features=Features({'''image''': Image()} ) )
						UpperCAmelCase_      :   Optional[int]      =	text._generate_tables([[text_file_with_image]] )
						UpperCAmelCase_      :   List[Any]      =	pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field('''image''' ).type == Image()()
						UpperCAmelCase_      :   Tuple      =	pa_table.to_pydict()['''image''']
						assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by''',      ['''line''', '''paragraph''', '''document'''] )
def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[int],      SCREAMING_SNAKE_CASE__   :      Optional[int] )			->						Optional[int]:
						with open(SCREAMING_SNAKE_CASE__,      encoding='''utf-8''' ) as f:
												UpperCAmelCase_      :   List[Any]      =	f.read()
						if sample_by == "line":
												UpperCAmelCase_      :   Union[str, Any]      =	expected_content.splitlines()
						elif sample_by == "paragraph":
												UpperCAmelCase_      :   Optional[int]      =	expected_content.split('''\n\n''' )
						elif sample_by == "document":
												UpperCAmelCase_      :   List[str]      =	[expected_content]
						UpperCAmelCase_      :   Optional[int]      =	Text(sample_by=SCREAMING_SNAKE_CASE__,      encoding='''utf-8''',      chunksize=100 )
						UpperCAmelCase_      :   Dict      =	text._generate_tables([[text_file]] )
						UpperCAmelCase_      :   Union[str, Any]      =	pa.concat_tables([table for _, table in generator] ).to_pydict()['''text''']
						assert generated_content == expected_content












'''simple docstring'''







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





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						str:

						def remove_articles(SCREAMING_SNAKE_CASE__   :      Union[str, Any] ):
												return re.sub(R'''\b(a|an|the)\b''',      ''' ''',      SCREAMING_SNAKE_CASE__ )

						def white_space_fix(SCREAMING_SNAKE_CASE__   :      Dict ):
												return " ".join(text.split() )

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

						def lower(SCREAMING_SNAKE_CASE__   :      str ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__ ) ) ) )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Tuple:
						UpperCAmelCase_      :   List[Any]      =	normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
						UpperCAmelCase_      :   Optional[Any]      =	normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
						UpperCAmelCase_      :   Any      =	Counter(SCREAMING_SNAKE_CASE__ ) & Counter(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Tuple      =	sum(common.values() )
						if num_same == 0:
												return 0
						UpperCAmelCase_      :   Union[str, Any]      =	1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	(2 * precision * recall) / (precision + recall)
						return fa





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int )			->						Any:
						return normalize_answer(SCREAMING_SNAKE_CASE__ ) == normalize_answer(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[int]:
						UpperCAmelCase_      :   Dict      =	[]
						for ground_truth in ground_truths:
												UpperCAmelCase_      :   str      =	metric_fn(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__ )
						return max(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Any:
						UpperCAmelCase_      :   str      =	0
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														UpperCAmelCase_      :   Union[str, Any]      =	'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																														print(SCREAMING_SNAKE_CASE__,      file=sys.stderr )
																														continue
																								UpperCAmelCase_      :   Optional[Any]      =	[x['''text'''] for x in qa['''answers''']]
																								UpperCAmelCase_      :   int      =	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__ )

						UpperCAmelCase_      :   Any      =	1_00.0 * exact_match / total
						UpperCAmelCase_      :   Optional[int]      =	1_00.0 * fa / total

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


if __name__ == "__main__":
					snake_case_  :							Any            =	"1.1"
					snake_case_  :							Any            =	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_  :							int            =	parser.parse_args()
					with open(args.dataset_file) as dataset_file:
										snake_case_  :							List[Any]            =	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_  :							Tuple            =	dataset_json["data"]
					with open(args.prediction_file) as prediction_file:
										snake_case_  :							Tuple            =	json.load(prediction_file)
					print(json.dumps(evaluate(dataset, predictions)))





