
'''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 						a   (				__lowercase				,__lowercase				,__lowercase				,unittest.TestCase    ):
				_snake_case		    = StableDiffusionInpaintPipeline
				_snake_case		    = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
				_snake_case		    = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				_snake_case		    = frozenset(
				    []    )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				_snake_case		    = frozenset([]    )




				def 	__snake_case							(							self	:				Optional[int]      ):
										torch.manual_seed(0      )
										snake_case       :     List[Any] =    UNetaDConditionModel(
										    block_out_channels=(32, 64),    layers_per_block=2,    sample_size=32,    in_channels=9,    out_channels=4,    down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),    up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),    cross_attention_dim=32,    attention_head_dim=(2, 4),    use_linear_projection=_A,    )
										snake_case       :     Optional[int] =    PNDMScheduler(skip_prk_steps=_A      )
										torch.manual_seed(0      )
										snake_case       :     Union[str, Any] =    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=1_28,    )
										torch.manual_seed(0      )
										snake_case       :     int =    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=10_00,    hidden_act='''gelu''',    projection_dim=5_12,    )
										snake_case       :     List[Any] =    CLIPTextModel(_A      )
										snake_case       :     Tuple =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''      )

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




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Optional[int]=0      ):
										# TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
										snake_case       :     Dict =    floats_tensor((1, 3, 32, 32),    rng=random.Random(_A      )      ).to(_A      )
										snake_case       :     str =    image.cpu().permute(0,    2,    3,    1      )[0]
										snake_case       :     Tuple =    Image.fromarray(np.uinta(_A      )      ).convert('''RGB'''      ).resize((64, 64)      )
										snake_case       :     Optional[Any] =    Image.fromarray(np.uinta(image + 4      )      ).convert('''RGB'''      ).resize((64, 64)      )
										if str(_A      ).startswith('''mps'''      ):
																snake_case       :     Tuple =    torch.manual_seed(_A      )
										else:
																snake_case       :     str =    torch.Generator(device=_A      ).manual_seed(_A      )
										snake_case       :     Dict =    {
										    '''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 	__snake_case							(							self	:				List[Any]      ):
										snake_case       :     List[Any] =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										snake_case       :     List[Any] =    self.get_dummy_components()
										snake_case       :     List[str] =    StableDiffusionInpaintPipeline(**_A      )
										snake_case       :     Dict =    sd_pipe.to(_A      )
										sd_pipe.set_progress_bar_config(disable=_A      )

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

										assert image.shape == (1, 64, 64, 3)
										snake_case       :     Any =    np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]      )

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




				def 	__snake_case							(							self	:				Optional[int]      ):
										super().test_inference_batch_single_identical(expected_max_diff=3e-3      )




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




				def 	__snake_case							(							self	:				List[str]      ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




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

										snake_case       :     Dict =    '''stabilityai/stable-diffusion-2-inpainting'''
										snake_case       :     str =    StableDiffusionInpaintPipeline.from_pretrained(_A,    safety_checker=_A      )
										pipe.to(_A      )
										pipe.set_progress_bar_config(disable=_A      )
										pipe.enable_attention_slicing()

										snake_case       :     Tuple =    '''Face of a yellow cat, high resolution, sitting on a park bench'''

										snake_case       :     List[str] =    torch.manual_seed(0      )
										snake_case       :     Tuple =    pipe(
										    prompt=_A,    image=_A,    mask_image=_A,    generator=_A,    output_type='''np''',    )
										snake_case       :     Tuple =    output.images[0]

										assert image.shape == (5_12, 5_12, 3)
										assert np.abs(expected_image - image      ).max() < 9e-3




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

										snake_case       :     List[Any] =    '''stabilityai/stable-diffusion-2-inpainting'''
										snake_case       :     Dict =    StableDiffusionInpaintPipeline.from_pretrained(
										    _A,    torch_dtype=torch.floataa,    safety_checker=_A,    )
										pipe.to(_A      )
										pipe.set_progress_bar_config(disable=_A      )
										pipe.enable_attention_slicing()

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

										snake_case       :     Tuple =    torch.manual_seed(0      )
										snake_case       :     List[str] =    pipe(
										    prompt=_A,    image=_A,    mask_image=_A,    generator=_A,    output_type='''np''',    )
										snake_case       :     Dict =    output.images[0]

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




				def 	__snake_case							(							self	:				List[Any]      ):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

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

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

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

										snake_case       :     List[str] =    torch.manual_seed(0      )
										snake_case       :     str =    pipe(
										    prompt=_A,    image=_A,    mask_image=_A,    generator=_A,    num_inference_steps=2,    output_type='''np''',    )

										snake_case       :     Optional[int] =    torch.cuda.max_memory_allocated()
										# make sure that less than 2.65 GB is allocated
										assert mem_bytes < 2.65 * 10**9



'''simple docstring'''





import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE

class 						a   (				__magic_name__    ):
				_snake_case		    = ''''''
				_snake_case		    = (
				    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
				)
				_snake_case		    = None  # compression type in fsspec. ex: "gzip"
				_snake_case		    = None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz




				def __init__(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				str = "",    SCREAMING_SNAKE_CASE_	:				Optional[str] = None,    SCREAMING_SNAKE_CASE_	:				Optional[dict] = None,    **SCREAMING_SNAKE_CASE_	:				Union[str, Any]      ):
										super().__init__(self,    **SCREAMING_SNAKE_CASE_      )
										# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
										snake_case       :     List[Any] =    fsspec.open(
										    SCREAMING_SNAKE_CASE_,    mode='''rb''',    protocol=SCREAMING_SNAKE_CASE_,    compression=self.compression,    client_kwargs={
										        '''requote_redirect_url''': False,  # see https://github.com/huggingface/datasets/pull/5459
										        '''trust_env''': True,  # Enable reading proxy env variables.
										        **(target_options or {}).pop('''client_kwargs''',    {}      ),  # To avoid issues if it was already passed.
										    },    **(target_options or {}),    )
										snake_case       :     Tuple =    os.path.basename(self.file.path.split('''::'''      )[0]      )
										snake_case       :     Optional[Any] =    (
										    self.compressed_name[: self.compressed_name.rindex('''.'''      )]
										    if '''.''' in self.compressed_name
										    else self.compressed_name
										)
										snake_case       :     str =    None




				@classmethod
				def 	__snake_case							(							cls	:				Any,    SCREAMING_SNAKE_CASE_	:				int      ):
										# compressed file paths are always relative to the archive root
										return super()._strip_protocol(SCREAMING_SNAKE_CASE_      ).lstrip('''/'''      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										if self.dir_cache is None:
																snake_case       :     Optional[int] =    {**self.file.fs.info(self.file.path      ), '''name''': self.uncompressed_name}
																snake_case       :     Tuple =    {f['''name''']: f}




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str      ):
										return self.file.open().read()




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str = "rb",    SCREAMING_SNAKE_CASE_	:				int=None,    SCREAMING_SNAKE_CASE_	:				Optional[int]=True,    SCREAMING_SNAKE_CASE_	:				List[Any]=None,    **SCREAMING_SNAKE_CASE_	:				Any,    ):
										snake_case       :     int =    self._strip_protocol(SCREAMING_SNAKE_CASE_      )
										if mode != "rb":
																raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'"""      )
										return self.file.open()




class 						a   (				__magic_name__    ):
				_snake_case		    = '''bz2'''
				_snake_case		    = '''bz2'''
				_snake_case		    = '''.bz2'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''gzip'''
				_snake_case		    = '''gzip'''
				_snake_case		    = '''.gz'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''lz4'''
				_snake_case		    = '''lz4'''
				_snake_case		    = '''.lz4'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''xz'''
				_snake_case		    = '''xz'''
				_snake_case		    = '''.xz'''




class 						a   (				__magic_name__    ):
				_snake_case		    = '''zstd'''
				_snake_case		    = '''zstd'''
				_snake_case		    = '''.zst'''




				def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				str = "rb",    SCREAMING_SNAKE_CASE_	:				Optional[str] = None,    SCREAMING_SNAKE_CASE_	:				Optional[dict] = None,    SCREAMING_SNAKE_CASE_	:				int = DEFAULT_BLOCK_SIZE,    **SCREAMING_SNAKE_CASE_	:				Optional[Any],    ):
										super().__init__(
										    fo=SCREAMING_SNAKE_CASE_,    mode=SCREAMING_SNAKE_CASE_,    target_protocol=SCREAMING_SNAKE_CASE_,    target_options=SCREAMING_SNAKE_CASE_,    block_size=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
										#
										# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
										#     out.close = close
										# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
										#
										# see https://github.com/intake/filesystem_spec/issues/725
										snake_case       :     List[Any] =    self.file.__enter__

										class 						a   :




														def __init__(							self	:				int,    SCREAMING_SNAKE_CASE_	:				Dict      ):
																				snake_case       :     Dict =    file_




														def __enter__(							self	:				Optional[Any]      ):
																				self._file.__enter__()
																				return self




														def __exit__(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
																				self._file.__exit__(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




														def __iter__(							self	:				Union[str, Any]      ):
																				return iter(self._file      )




														def 	__snake_case							(							self	:				Union[str, Any]      ):
																				return next(self._file      )




														def __getattr__(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
																				return getattr(self._file,    SCREAMING_SNAKE_CASE_      )

										def fixed_enter(*SCREAMING_SNAKE_CASE_	:				Optional[Any],    **SCREAMING_SNAKE_CASE_	:				List[Any]      ):
																return WrappedFile(_enter(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )      )

										snake_case       :     Union[str, Any] =    fixed_enter

