





import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


UpperCAmelCase__  :     Optional[int]   			=importlib.util.find_spec('''s3fs''') is not None

if _has_safs:
							from .safilesystem import SaFileSystem  # noqa: F401

UpperCAmelCase__  :     List[compression.BaseCompressedFileFileSystem]   			=[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
							if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
														warnings.warn(F"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
							fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)





def        _lowercase   (  _UpperCAmelCase			)  ->					Any:



				if "://" in dataset_path:
								lowerCamelCase			=dataset_path.split("""://"""			)[1]
				return dataset_path





def        _lowercase   (  _UpperCAmelCase			)  ->					Optional[int]:



				if fs is not None and fs.protocol != "file":
								return True
				else:
								return False





def        _lowercase   (  _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase			)  ->					Optional[int]:
				lowerCamelCase			=not is_remote_filesystem(a__			)
				if is_local:
								# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
								shutil.move(fs._strip_protocol(a__			)     , fs._strip_protocol(a__			)			)
				else:
								fs.mv(a__     , a__     , recursive=a__			)





def        _lowercase   (  )  ->					Tuple:



				if hasattr(fsspec.asyn     , """reset_lock"""			):
								# for future fsspec>2022.05.0
								fsspec.asyn.reset_lock()
				else:
								lowerCamelCase			=None
								lowerCamelCase			=None
								lowerCamelCase			=threading.Lock()












import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class  __A       (  a      ,   a      ,   unittest.TestCase      ):
				__A								=			IFInpaintingSuperResolutionPipeline
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__A								=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}      )
				__A								=			PipelineTesterMixin.required_optional_params - {"""latents"""}


				def _snake_case    (						self							):
								return self._get_superresolution_dummy_components()


				def _snake_case    (						self				,	UpperCAmelCase_				,	UpperCAmelCase_=0							):
								if str(UpperCAmelCase_							).startswith("""mps"""							):
												lowerCamelCase			=torch.manual_seed(UpperCAmelCase_							)
								else:
												lowerCamelCase			=torch.Generator(device=UpperCAmelCase_							).manual_seed(UpperCAmelCase_							)

								lowerCamelCase			=floats_tensor((1, 3, 16, 16)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)
								lowerCamelCase			=floats_tensor((1, 3, 32, 32)				,	rng=random.Random(UpperCAmelCase_							)							).to(UpperCAmelCase_							)

								lowerCamelCase			={
								    """prompt""": """A painting of a squirrel eating a burger""",
								    """image""": image,
								    """original_image""": original_image,
								    """mask_image""": mask_image,
								    """generator""": generator,
								    """num_inference_steps""": 2,
								    """output_type""": """numpy""",
								}

								return inputs


				@unittest.skipIf(
				    torch_device != """cuda""" or not is_xformers_available()				,	reason="""XFormers attention is only available with CUDA and `xformers` installed"""				,	)
				def _snake_case    (						self							):
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)


				def _snake_case    (						self							):
								self._test_save_load_optional_components()


				@unittest.skipIf(torch_device != """cuda"""				,	reason="""float16 requires CUDA"""							)
				def _snake_case    (						self							):
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1							)


				def _snake_case    (						self							):
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)


				def _snake_case    (						self							):
								self._test_save_load_local()


				def _snake_case    (						self							):
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2				,	)





