"""simple docstring"""




import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


__lowerCamelCase											=  "\\n    Text data.\n    Second line of data."

__lowerCamelCase											=  "file"






@pytest.fixture(scope='session'				)
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							tmp_path_factory.mktemp('data'				) / (FILE_PATH + '.zstd')
							A__	     =							bytes(UpperCamelCase__       ,    'utf-8'				)
							with zstd.open(UpperCamelCase__       ,    'wb'				) as f:
														f.write(UpperCamelCase__				)
							return path






@pytest.fixture
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							with open(os.path.join(tmpfs.local_root_dir       ,    UpperCamelCase__				)       ,    'w'				) as f:
														f.write(UpperCamelCase__				)
							return FILE_PATH






@pytest.mark.parametrize('compression_format'       ,    ['gzip', 'xz', 'zstd']				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							{'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
							A__	     =							input_paths[compression_format]
							A__	     =							tmp_path / 'cache'
							A__	     =							DownloadConfig(cache_dir=UpperCamelCase__       ,    extract_compressed_file=UpperCamelCase__				)
							A__	     =							cached_path(UpperCamelCase__       ,    download_config=UpperCamelCase__				)
							with open(UpperCamelCase__				) as f:
														A__	     =							f.read()
							with open(UpperCamelCase__				) as f:
														A__	     =							f.read()
							assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('default_extracted'       ,    [True, False]				)
@pytest.mark.parametrize('default_cache_dir'       ,    [True, False]				)
def   UpperCAmelCase					(	UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__       ,    UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							'custom_cache'
							A__	     =							'custom_extracted_dir'
							A__	     =							tmp_path / 'custom_extracted_path'
							if default_extracted:
														A__	     =							('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
							else:
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'       ,    UpperCamelCase__				)
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'       ,    str(UpperCamelCase__				)				)
														A__	     =							custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

							A__	     =							xz_file
							A__	     =							(
							    DownloadConfig(extract_compressed_file=UpperCamelCase__				)
							    if default_cache_dir
							    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir       ,    extract_compressed_file=UpperCamelCase__				)
							)
							A__	     =							cached_path(UpperCamelCase__       ,    download_config=UpperCamelCase__				)
							assert Path(UpperCamelCase__				).parent.parts[-2:] == expected






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							str(Path(UpperCamelCase__				).resolve()				)
							assert cached_path(UpperCamelCase__				) == text_file
							# relative path
							A__	     =							str(Path(UpperCamelCase__				).resolve().relative_to(Path(os.getcwd()				)				)				)
							assert cached_path(UpperCamelCase__				) == text_file






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							str(tmp_path.resolve() / '__missing_file__.txt'				)
							with pytest.raises(UpperCamelCase__				):
														cached_path(UpperCamelCase__				)
							# relative path
							A__	     =							'./__missing_file__.txt'
							with pytest.raises(UpperCamelCase__				):
														cached_path(UpperCamelCase__				)






def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							get_from_cache(F'''tmp://{tmpfs_file}'''				)
							with open(UpperCamelCase__				) as f:
														A__	     =							f.read()
							assert output_file_content == FILE_CONTENT






@patch('datasets.config.HF_DATASETS_OFFLINE'       ,    UpperCamelCase__				)
def   UpperCAmelCase					(	):






							"""simple docstring"""





							with pytest.raises(UpperCamelCase__				):
														cached_path('https://huggingface.co'				)






@patch('datasets.config.HF_DATASETS_OFFLINE'       ,    UpperCamelCase__				)
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							tmp_path_factory.mktemp('data'				) / 'file.html'
							with pytest.raises(UpperCamelCase__				):
														http_get('https://huggingface.co'       ,    temp_file=UpperCamelCase__				)
							with pytest.raises(UpperCamelCase__				):
														http_head('https://huggingface.co'				)






@patch('datasets.config.HF_DATASETS_OFFLINE'       ,    UpperCamelCase__				)
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							tmp_path_factory.mktemp('data'				) / 'file.html'
							with pytest.raises(UpperCamelCase__				):
														ftp_get('ftp://huggingface.co'       ,    temp_file=UpperCamelCase__				)
							with pytest.raises(UpperCamelCase__				):
														ftp_head('ftp://huggingface.co'				)






@patch('datasets.config.HF_DATASETS_OFFLINE'       ,    UpperCamelCase__				)
def   UpperCAmelCase					(	UpperCamelCase__				):






							"""simple docstring"""





							A__	     =							tmp_path_factory.mktemp('data'				) / 'file.html'
							with pytest.raises(UpperCamelCase__				):
														fsspec_get('s3://huggingface.co'       ,    temp_file=UpperCamelCase__				)
							with pytest.raises(UpperCamelCase__				):
														fsspec_head('s3://huggingface.co'				)






"""simple docstring"""




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
				import onnxruntime as ort
@nightly
@require_onnxruntime
@require_torch_gpu
class     UpperCamelCase__(			unittest.TestCase ):





						@property
						def 				snake_case__	(       self     )					->				int:
													return (
													    "CUDAExecutionProvider",
													    {
													        "gpu_mem_limit": "15000000000",  # 15GB
													        "arena_extend_strategy": "kSameAsRequested",
													    },
													)





						@property
						def 				snake_case__	(       self     )					->				Union[str, Any]:
													A__	     =							ort.SessionOptions()
													A__	     =							False
													return options





						def 				snake_case__	(       self     )					->				str:
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo.png'     )
													A__	     =							load_image(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/overture-creations-5sI6fQgYIuo_mask.png'     )
													A__	     =							load_numpy(
													    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
													    '/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'     )

													# using the PNDM scheduler by default
													A__	     =							OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
													    'CompVis/stable-diffusion-v1-4'		,revision='onnx'		,safety_checker=__UpperCAmelCase		,feature_extractor=__UpperCAmelCase		,provider=self.gpu_provider		,sess_options=self.gpu_options		,)
													pipe.set_progress_bar_config(disable=__UpperCAmelCase     )

													A__	     =							'A red cat sitting on a park bench'

													A__	     =							np.random.RandomState(0     )
													A__	     =							pipe(
													    prompt=__UpperCAmelCase		,image=__UpperCAmelCase		,mask_image=__UpperCAmelCase		,strength=0.7_5		,guidance_scale=7.5		,num_inference_steps=15		,generator=__UpperCAmelCase		,output_type='np'		,)

													A__	     =							output.images[0]

													assert image.shape == (5_12, 5_12, 3)
													assert np.abs(expected_image - image     ).max() < 1e-2





