


"""simple docstring"""
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 			_UpperCAmelCase (							__snake_case,				__snake_case,				unittest.TestCase				):






		'''simple docstring'''

		lowerCamelCase__				=IFInpaintingSuperResolutionPipeline
		lowerCamelCase__				=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'width', 'height'}
		lowerCamelCase__				=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'original_image'}				)
		lowerCamelCase__				=PipelineTesterMixin.required_optional_params - {'latents'}



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self._get_superresolution_dummy_components()



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_=0					):





			'''simple docstring'''

			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	:						Union[str, Any]						=				floats_tensor((1, 3, 16, 16)				,       rng=random.Random(a_					)					).to(a_					)
			__snake_case	:						str						=				floats_tensor((1, 3, 32, 32)				,       rng=random.Random(a_					)					).to(a_					)
			__snake_case	:						Union[str, Any]						=				floats_tensor((1, 3, 32, 32)				,       rng=random.Random(a_					)					).to(a_					)

			__snake_case	:						List[str]						=				{
			    '''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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != '''cuda'''				,       reason='''float16 requires CUDA'''					)
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			super().test_save_load_floataa(expected_max_diff=1E-1					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			self._test_save_load_local()



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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









"""simple docstring"""
import pytest

import datasets


# Import fixture modules as plugins
SCREAMING_SNAKE_CASE :    List[Any]									=				["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]






def 		lowercase							(     _snake_case						:      Optional[int]     ,   _snake_case						:      Optional[int]     )     ->Tuple:
	"""simple docstring"""





	for item in items:
		if any(marker in item.keywords for marker in ['''integration''', '''unit''']     ):
			continue
		item.add_marker(pytest.mark.unit     )






def 		lowercase							(     _snake_case						:      List[str]     )     ->Optional[int]:
	"""simple docstring"""





	config.addinivalue_line('''markers'''     ,   '''torchaudio_latest: mark test to run with torchaudio>=0.12'''     )






@pytest.fixture(autouse=_snake_case     )
def 		lowercase							(     _snake_case						:      Optional[Any]     ,   _snake_case						:      Dict     )     ->Any:
	"""simple docstring"""





	__snake_case	:						List[Any]						=				tmp_path_factory.getbasetemp() / '''cache'''
	__snake_case	:						int						=				test_hf_cache_home / '''datasets'''
	__snake_case	:						Tuple						=				test_hf_cache_home / '''metrics'''
	__snake_case	:						List[str]						=				test_hf_cache_home / '''modules'''
	monkeypatch.setattr('''datasets.config.HF_DATASETS_CACHE'''     ,   str(_snake_case     )     )
	monkeypatch.setattr('''datasets.config.HF_METRICS_CACHE'''     ,   str(_snake_case     )     )
	monkeypatch.setattr('''datasets.config.HF_MODULES_CACHE'''     ,   str(_snake_case     )     )
	__snake_case	:						Optional[int]						=				test_hf_datasets_cache / '''downloads'''
	monkeypatch.setattr('''datasets.config.DOWNLOADED_DATASETS_PATH'''     ,   str(_snake_case     )     )
	__snake_case	:						Tuple						=				test_hf_datasets_cache / '''downloads''' / '''extracted'''
	monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''     ,   str(_snake_case     )     )






@pytest.fixture(autouse=_snake_case     ,   scope='''session'''     )
def 		lowercase							(     )     ->Any:
	"""simple docstring"""





	datasets.disable_progress_bar()






@pytest.fixture(autouse=_snake_case     )
def 		lowercase							(     _snake_case						:      Tuple     )     ->Union[str, Any]:
	"""simple docstring"""





	monkeypatch.setattr('''datasets.config.HF_UPDATE_DOWNLOAD_COUNTS'''     ,   _snake_case     )






@pytest.fixture
def 		lowercase							(     _snake_case						:      Any     )     ->Optional[Any]:
	"""simple docstring"""





	monkeypatch.setattr('''sqlalchemy.util.deprecations.SILENCE_UBER_WARNING'''     ,   _snake_case     )





