
'''simple docstring'''







import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard







@pytest.mark.parametrize(
    'compression_format, is_archive'    ,       [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]    ,       )
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       ) ->			str:
	_snake_case     		=	{
	    '7z': (seven_zip_file, SevenZipExtractor),
	    'bz2': (bza_file, BzipaExtractor),
	    'gzip': (gz_file, GzipExtractor),
	    'lz4': (lza_file, LzaExtractor),
	    'tar': (tar_file, TarExtractor),
	    'xz': (xz_file, XzExtractor),
	    'zip': (zip_file, ZipExtractor),
	    'zstd': (zstd_file, ZstdExtractor),
	}
	_snake_case		,				_snake_case     		=	input_paths_and_base_extractors[compression_format]
	if input_path is None:
		_snake_case     		=	F'for \'{compression_format}\' compression_format, '
		if compression_format == "7z":
			reason += require_pyazr.kwargs["reason"]
		elif compression_format == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_format == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(__A			)
	assert base_extractor.is_extractable(__A			)
	_snake_case     		=	tmp_path / ('extracted' if is_archive else 'extracted.txt')
	base_extractor.extract(__A    ,       __A			)
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			_snake_case     		=	file_path.read_text(encoding='utf-8'			)
	else:
		_snake_case     		=	output_path.read_text(encoding='utf-8'			)
	_snake_case     		=	text_file.read_text(encoding='utf-8'			)
	assert extracted_file_content == expected_file_content







@pytest.mark.parametrize(
    'compression_format, is_archive'    ,       [
        ('7z', True),
        ('bz2', False),
        ('gzip', False),
        ('lz4', False),
        ('tar', True),
        ('xz', False),
        ('zip', True),
        ('zstd', False),
    ]    ,       )
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       ) ->			Union[str, Any]:
	_snake_case     		=	{
	    '7z': seven_zip_file,
	    'bz2': bza_file,
	    'gzip': gz_file,
	    'lz4': lza_file,
	    'tar': tar_file,
	    'xz': xz_file,
	    'zip': zip_file,
	    'zstd': zstd_file,
	}
	_snake_case     		=	input_paths[compression_format]
	if input_path is None:
		_snake_case     		=	F'for \'{compression_format}\' compression_format, '
		if compression_format == "7z":
			reason += require_pyazr.kwargs["reason"]
		elif compression_format == "lz4":
			reason += require_lza.kwargs["reason"]
		elif compression_format == "zstd":
			reason += require_zstandard.kwargs["reason"]
		pytest.skip(__A			)
	_snake_case     		=	Extractor.infer_extractor_format(__A			)
	assert extractor_format is not None
	_snake_case     		=	tmp_path / ('extracted' if is_archive else 'extracted.txt')
	Extractor.extract(__A    ,       __A    ,       __A			)
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			_snake_case     		=	file_path.read_text(encoding='utf-8'			)
	else:
		_snake_case     		=	output_path.read_text(encoding='utf-8'			)
	_snake_case     		=	text_file.read_text(encoding='utf-8'			)
	assert extracted_file_content == expected_file_content







@pytest.fixture
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Any:
	import tarfile

	_snake_case     		=	tmp_path / 'data_dot_dot'
	directory.mkdir()
	_snake_case     		=	directory / 'tar_file_with_dot_dot.tar'
	with tarfile.TarFile(__A    ,       'w'			) as f:
		f.add(__A    ,       arcname=os.path.join('..'    ,       text_file.name			)			)
	return path







@pytest.fixture
def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	import tarfile

	_snake_case     		=	tmp_path / 'data_sym_link'
	directory.mkdir()
	_snake_case     		=	directory / 'tar_file_with_sym_link.tar'
	os.symlink('..'    ,       directory / 'subdir'    ,       target_is_directory=__A			)
	with tarfile.TarFile(__A    ,       'w'			) as f:
		f.add(str(directory / 'subdir'			)    ,       arcname='subdir'			)  # str required by os.readlink on Windows and Python < 3.8
	return path







@pytest.mark.parametrize(
    'insecure_tar_file, error_log'    ,       [('tar_file_with_dot_dot', 'illegal path'), ('tar_file_with_sym_link', 'Symlink')]    ,       )
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A    ,       __A    ,       __A			) ->			Optional[int]:
	_snake_case     		=	{
	    'tar_file_with_dot_dot': tar_file_with_dot_dot,
	    'tar_file_with_sym_link': tar_file_with_sym_link,
	}
	_snake_case     		=	insecure_tar_files[insecure_tar_file]
	_snake_case     		=	tmp_path / 'extracted'
	TarExtractor.extract(__A    ,       __A			)
	assert caplog.text
	for record in caplog.records:
		assert record.levelname == "ERROR"
		assert error_log in record.msg







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			List[str]:
	# We should have less false positives than zipfile.is_zipfile
	# We do that by checking only the magic number
	_snake_case     		=	tmpdir / 'not_a_zip_file'
	# From: https://github.com/python/cpython/pull/5053
	_snake_case     		=	(
	    b'\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00'
	    b'\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6\'\x00\x00\x00\x15I'
	    b'DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07'
	    b'\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82'
	)
	with not_a_zip_file.open('wb'			) as f:
		f.write(__A			)
	assert zipfile.is_zipfile(str(__A			)			)  # is a false positive for `zipfile`
	assert not ZipExtractor.is_extractable(__A			)  # but we're right



'''simple docstring'''







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:


					class 		__UpperCAmelCase   :


												@staticmethod
												def 				lowerCamelCase       ( *lowerCAmelCase_					,     **lowerCAmelCase_						):





													"""simple docstring"""






													pass


@is_pipeline_test
@require_vision
@require_torch
class 		__UpperCAmelCase   (      unittest.TestCase		):
							__lowercase   	= MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	pipeline(
								    'zero-shot-object-detection'					,     model='hf-internal-testing/tiny-random-owlvit-object-detection'						)

								_snake_case     		=	[
								    {
								        'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								        'candidate_labels': ['cat', 'remote', 'couch'],
								    }
								]
								return object_detector, examples


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	object_detector(examples[0]					,     threshold=0.0						)

								_snake_case     		=	len(lowerCAmelCase_						)
								self.assertGreater(lowerCAmelCase_					,     0						)
								self.assertEqual(
								    lowerCAmelCase_					,     [
								        {
								            'score': ANY(lowerCAmelCase_						),
								            'label': ANY(lowerCAmelCase_						),
								            'box': {'xmin': ANY(lowerCAmelCase_						), 'ymin': ANY(lowerCAmelCase_						), 'xmax': ANY(lowerCAmelCase_						), 'ymax': ANY(lowerCAmelCase_						)},
								        }
								        for i in range(lowerCAmelCase_						)
								    ]					,     )


							@require_tf
							@unittest.skip('Zero Shot Object Detection not implemented in TF'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@require_torch
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	pipeline(
								    'zero-shot-object-detection'					,     model='hf-internal-testing/tiny-random-owlvit-object-detection'						)

								_snake_case     		=	object_detector(
								    './tests/fixtures/tests_samples/COCO/000000039769.png'					,     candidate_labels=['cat', 'remote', 'couch']					,     threshold=0.64					,     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								        {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								        {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								        {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								    ]					,     )

								_snake_case     		=	object_detector(
								    [
								        {
								            'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        }
								    ]					,     threshold=0.64					,     )

								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        [
								            {'score': 0.7235, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7218, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.7184, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
								            {'score': 0.6748, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6656, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6614, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
								            {'score': 0.6456, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								            {'score': 0.642, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
								            {'score': 0.6419, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
								        ]
								    ]					,     )


							@require_torch
							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	pipeline('zero-shot-object-detection'						)

								_snake_case     		=	object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,     candidate_labels=['cat', 'remote', 'couch']					,     )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								        {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								        {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								    ]					,     )

								_snake_case     		=	object_detector(
								    [
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								        {
								            'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
								            'candidate_labels': ['cat', 'remote', 'couch'],
								        },
								    ]					,     )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        [
								            {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								        [
								            {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								            {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								            {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								            {'score': 0.1474, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
								            {'score': 0.1208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
								        ],
								    ]					,     )


							@require_tf
							@unittest.skip('Zero Shot Object Detection not implemented in TF'						)
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								pass


							@require_torch
							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	0.2
								_snake_case     		=	pipeline('zero-shot-object-detection'						)

								_snake_case     		=	object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,     candidate_labels=['cat', 'remote', 'couch']					,     threshold=lowerCAmelCase_					,     )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								        {'score': 0.2537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
								    ]					,     )




							@require_torch
							@slow
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	2
								_snake_case     		=	pipeline('zero-shot-object-detection'						)

								_snake_case     		=	object_detector(
								    'http://images.cocodataset.org/val2017/000000039769.jpg'					,     candidate_labels=['cat', 'remote', 'couch']					,     top_k=lowerCAmelCase_					,     )
								self.assertEqual(
								    nested_simplify(lowerCAmelCase_					,     decimals=4						)					,     [
								        {'score': 0.2868, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
								        {'score': 0.277, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
								    ]					,     )

