

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 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      )					->   int:






    '''simple docstring'''
    __UpperCAmelCase     =	{
        '''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),
    }
    __UpperCAmelCase     =	input_paths_and_base_extractors[compression_format]
    if input_path is None:
        __UpperCAmelCase     =	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(_snake_case					)
    assert base_extractor.is_extractable(_snake_case					)
    __UpperCAmelCase     =	tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
    base_extractor.extract(_snake_case				,      _snake_case					)
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            __UpperCAmelCase     =	file_path.read_text(encoding='''utf-8'''					)
    else:
        __UpperCAmelCase     =	output_path.read_text(encoding='''utf-8'''					)
    __UpperCAmelCase     =	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 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      )					->   Any:






    '''simple docstring'''
    __UpperCAmelCase     =	{
        '''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,
    }
    __UpperCAmelCase     =	input_paths[compression_format]
    if input_path is None:
        __UpperCAmelCase     =	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(_snake_case					)
    __UpperCAmelCase     =	Extractor.infer_extractor_format(_snake_case					)
    assert extractor_format is not None
    __UpperCAmelCase     =	tmp_path / ('''extracted''' if is_archive else '''extracted.txt''')
    Extractor.extract(_snake_case				,      _snake_case				,      _snake_case					)
    if is_archive:
        assert output_path.is_dir()
        for file_path in output_path.iterdir():
            assert file_path.name == text_file.name
            __UpperCAmelCase     =	file_path.read_text(encoding='''utf-8'''					)
    else:
        __UpperCAmelCase     =	output_path.read_text(encoding='''utf-8'''					)
    __UpperCAmelCase     =	text_file.read_text(encoding='''utf-8'''					)
    assert extracted_file_content == expected_file_content


@pytest.fixture
def 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   str:






    '''simple docstring'''
    import tarfile

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


@pytest.fixture
def 					__a				(      SCREAMING_SNAKE_CASE					)					->   List[Any]:






    '''simple docstring'''
    import tarfile

    __UpperCAmelCase     =	tmp_path / '''data_sym_link'''
    directory.mkdir()
    __UpperCAmelCase     =	directory / '''tar_file_with_sym_link.tar'''
    os.symlink('''..'''				,      directory / '''subdir'''				,      target_is_directory=_snake_case					)
    with tarfile.TarFile(_snake_case				,      '''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 					__a				(      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE					)					->   List[str]:






    '''simple docstring'''
    __UpperCAmelCase     =	{
        '''tar_file_with_dot_dot''': tar_file_with_dot_dot,
        '''tar_file_with_sym_link''': tar_file_with_sym_link,
    }
    __UpperCAmelCase     =	insecure_tar_files[insecure_tar_file]
    __UpperCAmelCase     =	tmp_path / '''extracted'''
    TarExtractor.extract(_snake_case				,      _snake_case					)
    assert caplog.text
    for record in caplog.records:
        assert record.levelname == "ERROR"
        assert error_log in record.msg


def 					__a				(      SCREAMING_SNAKE_CASE					)					->   Union[str, Any]:






    '''simple docstring'''
    # We should have less false positives than zipfile.is_zipfile
    # We do that by checking only the magic number
    __UpperCAmelCase     =	tmpdir / '''not_a_zip_file'''
    # From: https://github.com/python/cpython/pull/5053
    __UpperCAmelCase     =	(
        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(_snake_case					)
    assert zipfile.is_zipfile(str(_snake_case					)					)  # is a false positive for `zipfile`
    assert not ZipExtractor.is_extractable(_snake_case					)  # but we're right








"""simple docstring"""





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import BridgeTowerImageProcessor



class 						snake_case_(				unittest.TestCase	):






     def __init__(		self     :    Union[str, Any]    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Dict[str, int] = None    , UpperCamelCase_     :    int = 3_2    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Union[int, float] = 1 / 2_5_5    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = [0.48_145_466, 0.4_578_275, 0.40_821_073]    , UpperCamelCase_     :    Optional[Union[float, List[float]]] = [0.26_862_954, 0.26_130_258, 0.27_577_711]    , UpperCamelCase_     :    bool = True    , UpperCamelCase_     :    Optional[int]=7    , UpperCamelCase_     :    int=3_0    , UpperCamelCase_     :    str=4_0_0    , UpperCamelCase_     :    List[Any]=3    , ):
           lowerCAmelCase    :		Union[str, Any]				=      parent
           lowerCAmelCase    :		Union[str, Any]				=      do_resize
           lowerCAmelCase    :		List[str]				=      size if size is not None else {'''shortest_edge''': 2_8_8}
           lowerCAmelCase    :		int				=      size_divisor
           lowerCAmelCase    :		List[str]				=      do_rescale
           lowerCAmelCase    :		Optional[Any]				=      rescale_factor
           lowerCAmelCase    :		Dict				=      do_normalize
           lowerCAmelCase    :		Any				=      do_center_crop
           lowerCAmelCase    :		Union[str, Any]				=      image_mean
           lowerCAmelCase    :		Optional[Any]				=      image_std
           lowerCAmelCase    :		Union[str, Any]				=      do_pad
           lowerCAmelCase    :		Union[str, Any]				=      batch_size
           lowerCAmelCase    :		Any				=      num_channels
           lowerCAmelCase    :		Union[str, Any]				=      min_resolution
           lowerCAmelCase    :		int				=      max_resolution






     def    lowerCamelCase__   (		self     :    Dict ):
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
               "size_divisor": self.size_divisor,
           }






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    int    , UpperCamelCase_     :    List[str]=False ):
           if not batched:
                 lowerCAmelCase    :		Dict				=      self.size['''shortest_edge''']
                 lowerCAmelCase    :		Dict				=      image_inputs[0]
                 if isinstance(UpperCamelCase_    , Image.Image ):
                       lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      image.size
                 else:
                       lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      image.shape[1], image.shape[2]
                 lowerCAmelCase    :		Union[str, Any]				=      size / min(UpperCamelCase_    , UpperCamelCase_ )
                 if h < w:
                       lowerCAmelCase,					lowerCAmelCase    :		Dict				=      size, scale * w
                 else:
                       lowerCAmelCase,					lowerCAmelCase    :		Optional[int]				=      scale * h, size

                 lowerCAmelCase    :		List[Any]				=      int((1_3_3_3 / 8_0_0) * size )
                 if max(UpperCamelCase_    , UpperCamelCase_ ) > max_size:
                       lowerCAmelCase    :		int				=      max_size / max(UpperCamelCase_    , UpperCamelCase_ )
                       lowerCAmelCase    :		str				=      newh * scale
                       lowerCAmelCase    :		Tuple				=      neww * scale

                 lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      int(newh + 0.5 ), int(neww + 0.5 )
                 lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      (
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

           else:
                 lowerCAmelCase    :		Optional[int]				=      []
                 for image in image_inputs:
                       lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      self.get_expected_values([image] )
                       expected_values.append((expected_height, expected_width) )
                 lowerCAmelCase    :		Union[str, Any]				=      max(UpperCamelCase_    , key=lambda UpperCamelCase_ : item[0] )[0]
                 lowerCAmelCase    :		Union[str, Any]				=      max(UpperCamelCase_    , key=lambda UpperCamelCase_ : item[1] )[1]

           return expected_height, expected_width



@require_torch
@require_vision
class 						snake_case_(				a__							,    unittest.TestCase	):
     __UpperCamelCase     =    BridgeTowerImageProcessor if is_vision_available() else None






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           lowerCAmelCase    :		Optional[int]				=      BridgeTowerImageProcessingTester(self )






     @property
     def    lowerCamelCase__   (		self     :    List[str] ):
           return self.image_processor_tester.prepare_image_processor_dict()






     def    lowerCamelCase__   (		self     :    List[str] ):
           lowerCAmelCase    :		Optional[Any]				=      self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(UpperCamelCase_    , '''image_mean''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''image_std''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_normalize''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''do_resize''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''size''' ) )
           self.assertTrue(hasattr(UpperCamelCase_    , '''size_divisor''' ) )






     def    lowerCamelCase__   (		self     :    int ):
           pass






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           # Initialize image processor
           lowerCAmelCase    :		str				=      self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           lowerCAmelCase    :		Optional[int]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , Image.Image )

           # Test not batched input
           lowerCAmelCase    :		Optional[int]				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		List[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		Dict				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		int				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           # Initialize image processor
           lowerCAmelCase    :		Tuple				=      self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           lowerCAmelCase    :		List[Any]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , numpify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , np.ndarray )

           # Test not batched input
           lowerCAmelCase    :		Any				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		Optional[Any]				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		Tuple				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		str				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )






     def    lowerCamelCase__   (		self     :    Optional[int] ):
           # Initialize image processor
           lowerCAmelCase    :		Union[str, Any]				=      self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           lowerCAmelCase    :		List[str]				=      prepare_image_inputs(self.image_processor_tester    , equal_resolution=UpperCamelCase_    , torchify=UpperCamelCase_ )
           for image in image_inputs:
                 self.assertIsInstance(UpperCamelCase_    , torch.Tensor )

           # Test not batched input
           lowerCAmelCase    :		Any				=      image_processing(image_inputs[0]    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		Tuple				=      self.image_processor_tester.get_expected_values(UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (1, self.image_processor_tester.num_channels, expected_height, expected_width)    , )

           # Test batched
           lowerCAmelCase    :		str				=      image_processing(UpperCamelCase_    , return_tensors='''pt''' ).pixel_values

           lowerCAmelCase,					lowerCAmelCase    :		str				=      self.image_processor_tester.get_expected_values(UpperCamelCase_    , batched=UpperCamelCase_ )
           self.assertEqual(
               encoded_images.shape    , (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )    , )


