
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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				str:

		"""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    ,	_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(lowerCAmelCase )
		assert base_extractor.is_extractable(lowerCAmelCase )
		_UpperCamelCase						    =	tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
		base_extractor.extract(lowerCAmelCase						,  lowerCAmelCase )
		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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  )		->				Dict:

		"""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(lowerCAmelCase )
		_UpperCamelCase						    =	Extractor.infer_extractor_format(lowerCAmelCase )
		assert extractor_format is not None
		_UpperCamelCase						    =	tmp_path / ("""extracted""" if is_archive else """extracted.txt""")
		Extractor.extract(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		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(lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""simple docstring"""






		import tarfile

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


@pytest.fixture
def 							__A(lowerCAmelCase )		->				Dict:

		"""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=lowerCAmelCase )
		with tarfile.TarFile(lowerCAmelCase						,  """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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Union[str, Any]:

		"""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(lowerCAmelCase						,  lowerCAmelCase )
		assert caplog.text
		for record in caplog.records:
				assert record.levelname == "ERROR"
				assert error_log in record.msg







def 							__A(lowerCAmelCase )		->				str:

		"""simple docstring"""






		_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(lowerCAmelCase )
		assert zipfile.is_zipfile(str(lowerCAmelCase ) )  # is a false positive for `zipfile`
		assert not ZipExtractor.is_extractable(lowerCAmelCase )  # but we're right





import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel

from ...utils import logging


lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		_UpperCamelCase						    =	nn.functional.normalize(lowerCAmelCase )
		return torch.mm(lowerCAmelCase						,  normalized_text_embeds.t() )







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		CLIPConfig

						UpperCamelCase_							: str	=		["CLIPEncoderLayer"]

						def __init__(			self       ,							a				)					->      Optional[Any]:


								'''simple docstring'''

								super().__init__(a				)

								_UpperCamelCase						    =	CLIPVisionModel(config.vision_config				)
								_UpperCamelCase						    =	nn.Linear(config.vision_config.hidden_size       ,							config.projection_dim       ,							bias=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17       ,							config.projection_dim				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3       ,							config.projection_dim				)       ,							requires_grad=a				)

								_UpperCamelCase						    =	nn.Parameter(torch.ones(17				)       ,							requires_grad=a				)
								_UpperCamelCase						    =	nn.Parameter(torch.ones(3				)       ,							requires_grad=a				)

						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      str:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				).cpu().float().numpy()
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				).cpu().float().numpy()

								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	image_embeds.shape[0]
								for i in range(a				):
										_UpperCamelCase						    =	{"""special_scores""": {}, """special_care""": [], """concept_scores""": {}, """bad_concepts""": []}

										# increase this value to create a stronger `nfsw` filter
										# at the cost of increasing the possibility of filtering benign images
										_UpperCamelCase						    =	0.0

										for concept_idx in range(len(special_cos_dist[0]				)				):
												_UpperCamelCase						    =	special_cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.special_care_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["special_scores"][concept_idx] > 0:
														result_img["special_care"].append({concept_idx, result_img["""special_scores"""][concept_idx]}				)
														_UpperCamelCase						    =	0.01

										for concept_idx in range(len(cos_dist[0]				)				):
												_UpperCamelCase						    =	cos_dist[i][concept_idx]
												_UpperCamelCase						    =	self.concept_embeds_weights[concept_idx].item()
												_UpperCamelCase						    =	round(concept_cos - concept_threshold + adjustment       ,							3				)
												if result_img["concept_scores"][concept_idx] > 0:
														result_img["bad_concepts"].append(a				)

										result.append(a				)

								_UpperCamelCase						    =	[len(res["""bad_concepts"""]				) > 0 for res in result]

								return images, has_nsfw_concepts







						@torch.no_grad()
						def  A_				(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

								_UpperCamelCase						    =	self.vision_model(a				)[1]  # pooled_output
								_UpperCamelCase						    =	self.visual_projection(a				)

								_UpperCamelCase						    =	cosine_distance(a       ,							self.special_care_embeds				)
								_UpperCamelCase						    =	cosine_distance(a       ,							self.concept_embeds				)

								# increase this value to create a stronger `nsfw` filter
								# at the cost of increasing the possibility of filtering benign images
								_UpperCamelCase						    =	0.0

								_UpperCamelCase						    =	special_cos_dist - self.special_care_embeds_weights + adjustment
								# special_scores = special_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(special_scores > 0       ,							dim=1				)
								_UpperCamelCase						    =	special_care * 0.01
								_UpperCamelCase						    =	special_adjustment.unsqueeze(1				).expand(-1       ,							cos_dist.shape[1]				)

								_UpperCamelCase						    =	(cos_dist - self.concept_embeds_weights) + special_adjustment
								# concept_scores = concept_scores.round(decimals=3)
								_UpperCamelCase						    =	torch.any(concept_scores > 0       ,							dim=1				)

								return images, has_nsfw_concepts



