

import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
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     snake_case_			(				_a			, _a			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =IFInpaintingPipeline
							__UpperCAmelCase       =TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
							__UpperCAmelCase       =TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__UpperCAmelCase       =PipelineTesterMixin.required_optional_params - {"""latents"""}




							def        A__  ( self							):
												return self._get_dummy_components()




							def        A__  ( self					,						_A					,						_A=0							):
												if str(_A							).startswith('mps'							):
																	__lowerCAmelCase					=							torch.manual_seed(_A							)
												else:
																	__lowerCAmelCase					=							torch.Generator(device=_A							).manual_seed(_A							)

												__lowerCAmelCase					=							floats_tensor((1, 3, 3_2, 3_2)					,						rng=random.Random(_A							)							).to(_A							)
												__lowerCAmelCase					=							floats_tensor((1, 3, 3_2, 3_2)					,						rng=random.Random(_A							)							).to(_A							)

												__lowerCAmelCase					=							{
												    'prompt': 'A painting of a squirrel eating a burger',
												    'image': 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        A__  ( self							):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3							)




							def        A__  ( self							):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != 'cuda'					,						reason='float16 requires CUDA'							)
							def        A__  ( 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        A__  ( self							):
												self._test_attention_slicing_forward_pass(expected_max_diff=1e-2							)




							def        A__  ( self							):
												self._test_save_load_local()




							def        A__  ( self							):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1e-2					,						)









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowerCamelCase      				=     logging.get_logger(__name__)





class     snake_case_			(				_a							):


							"""simple docstring"""





							def __init__( self					,						*_A					,						**_A							):
												warnings.warn(
												    'The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
												    ' Please use PoolFormerImageProcessor instead.'					,						_A					,						)
												super().__init__(*_A					,						**_A							)






