






"""simple docstring"""
import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


lowerCAmelCase__    						=			'http://www.mocksite.com/file1.txt'
lowerCAmelCase__    						=			'"text": ["foo", "foo"]'
lowerCAmelCase__    						=			'6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'


class _lowerCAmelCase						:
						SCREAMING_SNAKE_CASE_:					List[str] 					=				200
						SCREAMING_SNAKE_CASE_:					Union[str, Any] 					=				{'Content-Length': '100'}
						SCREAMING_SNAKE_CASE_:					int 					=				{}







						def 			A      (    self   ,				**lowerCAmelCase_							)		->							List[str]:
										return [bytes(lowerCAmelCase_   ,				'utf-8'							)]







def 				lowercase__		(					*lowerCamelCase,						**lowerCamelCase       ):
				return MockResponse()





@pytest.mark.parametrize('urls_type',						[str, list, dict]       )
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				import requests

				monkeypatch.setattr(lowerCamelCase,						'request',						lowerCamelCase       )

				_SCREAMING_SNAKE_CASE   :		Dict       		=    URL
				if issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Tuple       		=    url
				elif issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		List[Any]       		=    [url]
				elif issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    {'train': url}
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    'dummy'
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    'downloads'
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tmp_path
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    DownloadConfig(
				    cache_dir=os.path.join(lowerCamelCase,						lowerCamelCase       ),						use_etag=lowerCamelCase,						)
				_SCREAMING_SNAKE_CASE   :		List[Any]       		=    DownloadManager(dataset_name=lowerCamelCase,						download_config=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    dl_manager.download(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Dict       		=    urls
				for downloaded_paths in [downloaded_paths]:
								if isinstance(lowerCamelCase,						lowerCamelCase       ):
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [downloaded_paths]
												_SCREAMING_SNAKE_CASE   :		str       		=    [urls]
								elif isinstance(lowerCamelCase,						lowerCamelCase       ):
												assert "train" in downloaded_paths.keys()
												_SCREAMING_SNAKE_CASE   :		str       		=    downloaded_paths.values()
												_SCREAMING_SNAKE_CASE   :		Any       		=    urls.values()
								assert downloaded_paths
								for downloaded_path, input_url in zip(lowerCamelCase,						lowerCamelCase       ):
												assert downloaded_path == dl_manager.downloaded_paths[input_url]
												_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Path(lowerCamelCase       )
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    downloaded_path.parts
												assert parts[-1] == HASH
												assert parts[-2] == cache_subdir
												assert downloaded_path.exists()
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    downloaded_path.read_text()
												assert content == CONTENT
												_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    downloaded_path.with_suffix('.json'       )
												assert metadata_downloaded_path.exists()
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    json.loads(metadata_downloaded_path.read_text()       )
												assert metadata_content == {"url": URL, "etag": None}





@pytest.mark.parametrize('paths_type',						[str, list, dict]       )
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    str(lowerCamelCase       )
				if issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Any       		=    filename
				elif issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		List[str]       		=    [filename]
				elif issubclass(lowerCamelCase,						lowerCamelCase       ):
								_SCREAMING_SNAKE_CASE   :		Any       		=    {'train': filename}
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    'dummy'
				_SCREAMING_SNAKE_CASE   :		Dict       		=    xz_file.parent
				_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'extracted'
				_SCREAMING_SNAKE_CASE   :		Any       		=    DownloadConfig(
				    cache_dir=lowerCamelCase,						use_etag=lowerCamelCase,						)
				_SCREAMING_SNAKE_CASE   :		Tuple       		=    DownloadManager(dataset_name=lowerCamelCase,						download_config=lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Any       		=    dl_manager.extract(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    paths
				for extracted_paths in [extracted_paths]:
								if isinstance(lowerCamelCase,						lowerCamelCase       ):
												_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    [extracted_paths]
												_SCREAMING_SNAKE_CASE   :		Any       		=    [paths]
								elif isinstance(lowerCamelCase,						lowerCamelCase       ):
												assert "train" in extracted_paths.keys()
												_SCREAMING_SNAKE_CASE   :		Dict       		=    extracted_paths.values()
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    paths.values()
								assert extracted_paths
								for extracted_path, input_path in zip(lowerCamelCase,						lowerCamelCase       ):
												assert extracted_path == dl_manager.extracted_paths[input_path]
												_SCREAMING_SNAKE_CASE   :		List[Any]       		=    Path(lowerCamelCase       )
												_SCREAMING_SNAKE_CASE   :		int       		=    extracted_path.parts
												assert parts[-1] == hash_url_to_filename(lowerCamelCase,						etag=lowerCamelCase       )
												assert parts[-2] == extracted_subdir
												assert extracted_path.exists()
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    extracted_path.read_text()
												_SCREAMING_SNAKE_CASE   :		Tuple       		=    text_file.read_text()
												assert extracted_file_content == expected_file_content





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				assert path.endswith('.jsonl'       )
				for num_items, line in enumerate(lowerCamelCase,						start=1       ):
								_SCREAMING_SNAKE_CASE   :		int       		=    json.loads(line.decode('utf-8'       )       )
								assert item.keys() == {"col_1", "col_2", "col_3"}
				assert num_items == 4





@pytest.mark.parametrize('archive_jsonl',						['tar_jsonl_path', 'zip_jsonl_path']       )
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Any       		=    request.getfixturevalue(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    DownloadManager()
				for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase       ),						start=1       ):
								_test_jsonl(lowerCamelCase,						lowerCamelCase       )
				assert num_jsonl == 2





@pytest.mark.parametrize('archive_nested_jsonl',						['tar_nested_jsonl_path', 'zip_nested_jsonl_path']       )
def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		List[str]       		=    request.getfixturevalue(lowerCamelCase       )
				_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    DownloadManager()
				for num_tar, (path, file) in enumerate(dl_manager.iter_archive(lowerCamelCase       ),						start=1       ):
								for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(lowerCamelCase       ),						start=1       ):
												_test_jsonl(lowerCamelCase,						lowerCamelCase       )
				assert num_tar == 1
				assert num_jsonl == 2






def 				lowercase__		(					lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    DownloadManager()
				for num_file, file in enumerate(dl_manager.iter_files(lowerCamelCase       ),						start=1       ):
								assert os.path.basename(lowerCamelCase       ) == ("test.txt" if num_file == 1 else "train.txt")
				assert num_file == 2









"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class _lowerCAmelCase						(    __UpperCAmelCase   ,  __UpperCAmelCase   ,  __UpperCAmelCase   ,  unittest.TestCase       ):
						SCREAMING_SNAKE_CASE_:					Optional[Any] 					=				StableDiffusionInpaintPipeline
						SCREAMING_SNAKE_CASE_:					List[str] 					=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
						SCREAMING_SNAKE_CASE_:					Dict 					=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
						SCREAMING_SNAKE_CASE_:					Any 					=				frozenset(
						    []       )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
						SCREAMING_SNAKE_CASE_:					Any 					=				frozenset([]       )







						def 			A      (    self							)		->							Optional[Any]:
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)   ,				layers_per_block=2   ,				sample_size=3_2   ,				in_channels=9   ,				out_channels=4   ,				down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,				up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,				cross_attention_dim=3_2   ,				attention_head_dim=(2, 4)   ,				use_linear_projection=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    PNDMScheduler(skip_prk_steps=lowerCAmelCase_							)
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    AutoencoderKL(
										    block_out_channels=[3_2, 6_4]   ,				in_channels=3   ,				out_channels=3   ,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,				latent_channels=4   ,				sample_size=1_2_8   ,				)
										torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    CLIPTextConfig(
										    bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=3_2   ,				intermediate_size=3_7   ,				layer_norm_eps=1e-05   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1_0_0_0   ,				hidden_act='gelu'   ,				projection_dim=5_1_2   ,				)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    CLIPTextModel(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    {
										    'unet': unet,
										    'scheduler': scheduler,
										    'vae': vae,
										    'text_encoder': text_encoder,
										    'tokenizer': tokenizer,
										    'safety_checker': None,
										    'feature_extractor': None,
										}
										return components







						def 			A      (    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_=0							)		->							Optional[int]:
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    floats_tensor((1, 3, 3_2, 3_2)   ,				rng=random.Random(lowerCAmelCase_							)							).to(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    image.cpu().permute(0   ,				2   ,				3   ,				1							)[0]
										_SCREAMING_SNAKE_CASE   :		Any       		=    Image.fromarray(np.uinta(lowerCAmelCase_							)							).convert('RGB'							).resize((6_4, 6_4)							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Image.fromarray(np.uinta(image + 4							)							).convert('RGB'							).resize((6_4, 6_4)							)
										if str(lowerCAmelCase_							).startswith('mps'							):
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    torch.manual_seed(lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.Generator(device=lowerCAmelCase_							).manual_seed(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    {
										    'prompt': 'A painting of a squirrel eating a burger',
										    'image': init_image,
										    'mask_image': mask_image,
										    'generator': generator,
										    'num_inference_steps': 2,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs







						def 			A      (    self							)		->							str:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'cpu'  # ensure determinism for the device-dependent torch.Generator
										_SCREAMING_SNAKE_CASE   :		str       		=    self.get_dummy_components()
										_SCREAMING_SNAKE_CASE   :		Any       		=    StableDiffusionInpaintPipeline(**lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    sd_pipe.to(lowerCAmelCase_							)
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_							)

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

										assert image.shape == (1, 6_4, 6_4, 3)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476]							)

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







						def 			A      (    self							)		->							str:
										super().test_inference_batch_single_identical(expected_max_diff=3e-3							)







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







						def 			A      (    self							)		->							Any:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







						def 			A      (    self							)		->							Optional[int]:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
										    '/yellow_cat_sitting_on_a_park_bench.npy'							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    StableDiffusionInpaintPipeline.from_pretrained(lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_							)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		int       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 9e-3







						def 			A      (    self							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)
										_SCREAMING_SNAKE_CASE   :		int       		=    load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
										    '/yellow_cat_sitting_on_a_park_bench_fp16.npy'							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		str       		=    StableDiffusionInpaintPipeline.from_pretrained(
										    lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				safety_checker=lowerCAmelCase_   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				output_type='np'   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    output.images[0]

										assert image.shape == (5_1_2, 5_1_2, 3)
										assert np.abs(expected_image - image							).max() < 5e-1







						def 			A      (    self							)		->							List[str]:
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										_SCREAMING_SNAKE_CASE   :		str       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/sd2-inpaint/init_image.png'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    load_image(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'							)

										_SCREAMING_SNAKE_CASE   :		str       		=    'stabilityai/stable-diffusion-2-inpainting'
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    PNDMScheduler.from_pretrained(lowerCAmelCase_   ,				subfolder='scheduler'							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    StableDiffusionInpaintPipeline.from_pretrained(
										    lowerCAmelCase_   ,				safety_checker=lowerCAmelCase_   ,				scheduler=lowerCAmelCase_   ,				torch_dtype=torch.floataa   ,				)
										pipe.to(lowerCAmelCase_							)
										pipe.set_progress_bar_config(disable=lowerCAmelCase_							)
										pipe.enable_attention_slicing(1							)
										pipe.enable_sequential_cpu_offload()

										_SCREAMING_SNAKE_CASE   :		str       		=    'Face of a yellow cat, high resolution, sitting on a park bench'

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.manual_seed(0							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    pipe(
										    prompt=lowerCAmelCase_   ,				image=lowerCAmelCase_   ,				mask_image=lowerCAmelCase_   ,				generator=lowerCAmelCase_   ,				num_inference_steps=2   ,				output_type='np'   ,				)

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 1_0**9

