
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      _SCREAMING_SNAKE_CASE				(		UpperCAmelCase__,     UpperCAmelCase__,     unittest.TestCase				):
				lowerCamelCase_	  =  IFInpaintingPipeline
				lowerCamelCase_	  =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
				lowerCamelCase_	  =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowerCamelCase_	  =  PipelineTesterMixin.required_optional_params - {'''latents'''}





				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								return self._get_dummy_components()





				def 							_UpperCAmelCase    (		self			:		Union[str, Any]       ,   snake_case_			:		Tuple       ,   snake_case_			:		List[str]=0				):


								"""simple docstring"""

								if str(__lowerCAmelCase				).startswith('''mps'''				):
												A  :	Dict				=					torch.manual_seed(__lowerCAmelCase				)
								else:
												A  :	Optional[int]				=					torch.Generator(device=__lowerCAmelCase				).manual_seed(__lowerCAmelCase				)

								A  :	int				=					floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)
								A  :	Tuple				=					floats_tensor((1, 3, 32, 32)       ,   rng=random.Random(__lowerCAmelCase				)				).to(__lowerCAmelCase				)

								A  :	Optional[int]				=					{
								    '''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 							_UpperCAmelCase    (		self			:		Optional[Any]				):


								"""simple docstring"""

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)





				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								self._test_save_load_optional_components()





				@unittest.skipIf(torch_device != '''cuda'''       ,   reason='''float16 requires CUDA'''				)
				def 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

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





				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)





				def 							_UpperCAmelCase    (		self			:		Tuple				):


								"""simple docstring"""

								self._test_save_load_local()






				def 							_UpperCAmelCase    (		self			:		Any				):


								"""simple docstring"""

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





'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


_lowercase  =			logging.get_logger(__name__)
class 						a_				(			UpperCAmelCase__						):




	def __init__(       self     :  Tuple       ,		*__lowerCAmelCase     :  List[str]       ,		**__lowerCAmelCase     :  List[str]      ):
							warnings.warn(
							    'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
							    ' Please use VideoMAEImageProcessor instead.'       ,		__lowerCAmelCase       ,		)
							super().__init__(*__lowerCAmelCase       ,		**__lowerCAmelCase      )

