


'''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 lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Dict		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     Any		,		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					{
	    '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),
	}
	__magic_name__     ,    __magic_name__	:   Optional[Any]					     =					input_paths_and_base_extractors[compression_format]
	if input_path is None:
		__magic_name__	:   Tuple					     =					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(UpperCAmelCase__		)
	assert base_extractor.is_extractable(UpperCAmelCase__		)
	__magic_name__	:   int					     =					tmp_path / ('extracted' if is_archive else 'extracted.txt')
	base_extractor.extract(UpperCAmelCase__		,		UpperCAmelCase__		)
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			__magic_name__	:   Any					     =					file_path.read_text(encoding='utf-8'		)
	else:
		__magic_name__	:   Union[str, Any]					     =					output_path.read_text(encoding='utf-8'		)
	__magic_name__	:   Optional[Any]					     =					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 lowerCamelCase      (      lowerCAmelCase    :     int		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Optional[Any]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Any		,		lowerCAmelCase    :     Tuple		,		lowerCAmelCase    :     Tuple		,		):





	"""simple docstring"""







	__magic_name__	:   Optional[int]					     =					{
	    '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,
	}
	__magic_name__	:   int					     =					input_paths[compression_format]
	if input_path is None:
		__magic_name__	:   Tuple					     =					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(UpperCAmelCase__		)
	__magic_name__	:   str					     =					Extractor.infer_extractor_format(UpperCAmelCase__		)
	assert extractor_format is not None
	__magic_name__	:   str					     =					tmp_path / ('extracted' if is_archive else 'extracted.txt')
	Extractor.extract(UpperCAmelCase__		,		UpperCAmelCase__		,		UpperCAmelCase__		)
	if is_archive:
		assert output_path.is_dir()
		for file_path in output_path.iterdir():
			assert file_path.name == text_file.name
			__magic_name__	:   Dict					     =					file_path.read_text(encoding='utf-8'		)
	else:
		__magic_name__	:   List[str]					     =					output_path.read_text(encoding='utf-8'		)
	__magic_name__	:   List[str]					     =					text_file.read_text(encoding='utf-8'		)
	assert extracted_file_content == expected_file_content





@pytest.fixture
def lowerCamelCase      (      lowerCAmelCase    :     str		,		lowerCAmelCase    :     List[str]		):





	"""simple docstring"""







	import tarfile

	__magic_name__	:   List[Any]					     =					tmp_path / 'data_dot_dot'
	directory.mkdir()
	__magic_name__	:   int					     =					directory / 'tar_file_with_dot_dot.tar'
	with tarfile.TarFile(UpperCAmelCase__		,		'w'		) as f:
		f.add(UpperCAmelCase__		,		arcname=os.path.join('..'		,		text_file.name		)		)
	return path





@pytest.fixture
def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	import tarfile

	__magic_name__	:   Union[str, Any]					     =					tmp_path / 'data_sym_link'
	directory.mkdir()
	__magic_name__	:   Tuple					     =					directory / 'tar_file_with_sym_link.tar'
	os.symlink('..'		,		directory / 'subdir'		,		target_is_directory=UpperCAmelCase__		)
	with tarfile.TarFile(UpperCAmelCase__		,		'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 lowerCamelCase      (      lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     str		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     Union[str, Any]		,		lowerCAmelCase    :     int		,		lowerCAmelCase    :     Optional[int]		):





	"""simple docstring"""







	__magic_name__	:   List[str]					     =					{
	    'tar_file_with_dot_dot': tar_file_with_dot_dot,
	    'tar_file_with_sym_link': tar_file_with_sym_link,
	}
	__magic_name__	:   Optional[int]					     =					insecure_tar_files[insecure_tar_file]
	__magic_name__	:   Tuple					     =					tmp_path / 'extracted'
	TarExtractor.extract(UpperCAmelCase__		,		UpperCAmelCase__		)
	assert caplog.text
	for record in caplog.records:
		assert record.levelname == "ERROR"
		assert error_log in record.msg





def lowerCamelCase      (      lowerCAmelCase    :     List[Any]		):





	"""simple docstring"""







	__magic_name__	:   str					     =					tmpdir / 'not_a_zip_file'
	# From: https://github.com/python/cpython/pull/5053
	__magic_name__	:   Any					     =					(
	    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(UpperCAmelCase__		)
	assert zipfile.is_zipfile(str(UpperCAmelCase__		)		)  # is a false positive for `zipfile`
	assert not ZipExtractor.is_extractable(UpperCAmelCase__		)  # but we're right






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class _lowerCamelCase	(						lowercase__		,					lowercase__		,					unittest.TestCase							):



		'''simple docstring'''







		A_     :      int				   =    StableDiffusionXLImgaImgPipeline
		A_     :      str				   =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
		A_     :      Optional[Any]				   =    PipelineTesterMixin.required_optional_params - {"""latents"""}
		A_     :      Any				   =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		A_     :      Any				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS
		A_     :      Optional[int]				   =    IMAGE_TO_IMAGE_IMAGE_PARAMS







		def        __lowerCAmelCase		( self		:						Dict      )  ->			Optional[int]:
			torch.manual_seed(0      )
			__magic_name__	:   int					     =					UNetaDConditionModel(
			    block_out_channels=(32, 64)  ,					layers_per_block=2  ,					sample_size=32  ,					in_channels=4  ,					out_channels=4  ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,					attention_head_dim=(2, 4)  ,					use_linear_projection=_A  ,					addition_embed_type='text_time'  ,					addition_time_embed_dim=8  ,					transformer_layers_per_block=(1, 2)  ,					projection_class_embeddings_input_dim=80  ,					cross_attention_dim=64  ,					)
			__magic_name__	:   str					     =					EulerDiscreteScheduler(
			    beta_start=0.0_0085  ,					beta_end=0.012  ,					steps_offset=1  ,					beta_schedule='scaled_linear'  ,					timestep_spacing='leading'  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   str					     =					AutoencoderKL(
			    block_out_channels=[32, 64]  ,					in_channels=3  ,					out_channels=3  ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,					latent_channels=4  ,					sample_size=128  ,					)
			torch.manual_seed(0      )
			__magic_name__	:   Any					     =					CLIPTextConfig(
			    bos_token_id=0  ,					eos_token_id=2  ,					hidden_size=32  ,					intermediate_size=37  ,					layer_norm_eps=1E-05  ,					num_attention_heads=4  ,					num_hidden_layers=5  ,					pad_token_id=1  ,					vocab_size=1000  ,					hidden_act='gelu'  ,					projection_dim=32  ,					)
			__magic_name__	:   Dict					     =					CLIPTextModel(_A      )
			__magic_name__	:   List[str]					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   Optional[Any]					     =					CLIPTextModelWithProjection(_A      )
			__magic_name__	:   Dict					     =					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  ,					local_files_only=_A      )

			__magic_name__	:   List[Any]					     =					{
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'text_encoder_2': text_encoder_a,
			    'tokenizer_2': tokenizer_a,
			    # "safety_checker": None,
			    # "feature_extractor": None,
			}
			return components







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Any=0      )  ->			Union[str, Any]:
			__magic_name__	:   Any					     =					floats_tensor((1, 3, 32, 32)  ,					rng=random.Random(_A      )      ).to(_A      )
			__magic_name__	:   Dict					     =					image / 2 + 0.5
			if str(_A      ).startswith('mps'      ):
				__magic_name__	:   Any					     =					torch.manual_seed(_A      )
			else:
				__magic_name__	:   int					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   List[Any]					     =					{
			    'prompt': 'A painting of a squirrel eating a burger',
			    'image': image,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'guidance_scale': 5.0,
			    'output_type': 'numpy',
			    'strength': 0.75,
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Optional[Any]      )  ->			List[Any]:
			__magic_name__	:   str					     =					'cpu'  # ensure determinism for the device-dependent torch.Generator
			__magic_name__	:   str					     =					self.get_dummy_components()
			__magic_name__	:   Any					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Any					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Optional[int]					     =					sd_pipe(**_A      ).images
			__magic_name__	:   str					     =					image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)

			__magic_name__	:   Any					     =					np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]      )

			assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2







		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			Optional[Any]:
			super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			int:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Union[str, Any]:
			pass







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Optional[int]:
			__magic_name__	:   Dict					     =					self.get_dummy_components()
			__magic_name__	:   Optional[Any]					     =					StableDiffusionXLImgaImgPipeline(**_A      )
			__magic_name__	:   List[Any]					     =					sd_pipe.to(_A      )
			__magic_name__	:   str					     =					sd_pipe.to(_A      )
			sd_pipe.set_progress_bar_config(disable=_A      )

			# forward without prompt embeds
			__magic_name__	:   Union[str, Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					negative_prompt
			__magic_name__	:   int					     =					3 * [inputs['prompt']]

			__magic_name__	:   Tuple					     =					sd_pipe(**_A      )
			__magic_name__	:   str					     =					output.images[0, -3:, -3:, -1]

			# forward with prompt embeds
			__magic_name__	:   Optional[Any]					     =					self.get_dummy_inputs(_A      )
			__magic_name__	:   Tuple					     =					3 * ['this is a negative prompt']
			__magic_name__	:   List[str]					     =					3 * [inputs.pop('prompt'      )]

			(
			    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    (
			    __magic_name__
			)     ,    
			)	:   List[Any]					     =					sd_pipe.encode_prompt(_A  ,					negative_prompt=_A      )

			__magic_name__	:   Tuple					     =					sd_pipe(
			    **_A  ,					prompt_embeds=_A  ,					negative_prompt_embeds=_A  ,					pooled_prompt_embeds=_A  ,					negative_pooled_prompt_embeds=_A  ,					)
			__magic_name__	:   int					     =					output.images[0, -3:, -3:, -1]

			# make sure that it's equal
			assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4







@slow
@require_torch_gpu
class _lowerCamelCase	(						unittest.TestCase							):



		'''simple docstring'''







		def        __lowerCAmelCase		( self		:						List[Any]      )  ->			Tuple:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						Optional[int]  ,					_A		:						Optional[Any]="cpu"  ,					_A		:						List[str]=torch.floataa  ,					_A		:						Any=0      )  ->			str:
			__magic_name__	:   List[str]					     =					torch.Generator(device=_A      ).manual_seed(_A      )
			__magic_name__	:   Optional[Any]					     =					np.random.RandomState(_A      ).standard_normal((1, 4, 64, 64)      )
			__magic_name__	:   Union[str, Any]					     =					torch.from_numpy(_A      ).to(device=_A  ,					dtype=_A      )
			__magic_name__	:   Optional[int]					     =					{
			    'prompt': 'a photograph of an astronaut riding a horse',
			    'latents': latents,
			    'generator': generator,
			    'num_inference_steps': 3,
			    'guidance_scale': 7.5,
			    'output_type': 'numpy',
			}
			return inputs







		def        __lowerCAmelCase		( self		:						Union[str, Any]      )  ->			Dict:
			__magic_name__	:   str					     =					DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
			pipe.to(_A      )
			pipe.set_progress_bar_config(disable=_A      )

			__magic_name__	:   Optional[int]					     =					self.get_inputs(_A      )
			__magic_name__	:   Union[str, Any]					     =					pipe(**_A      ).images
			__magic_name__	:   Any					     =					image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			__magic_name__	:   List[Any]					     =					np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]      )
			assert np.abs(image_slice - expected_slice      ).max() < 7E-3