




'''simple docstring'''


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 						a_				(			UpperCAmelCase__     ,						UpperCAmelCase__     ,						unittest.TestCase						):
	lowercase_       :     Union[str, Any]			  =      IFInpaintingPipeline
	lowercase_       :     List[Any]			  =      TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
	lowercase_       :     Dict			  =      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	lowercase_       :     Union[str, Any]			  =      PipelineTesterMixin.required_optional_params - {'''latents'''}




	def   lowercase__   (       self     :  Optional[int]      ):
							return self._get_dummy_components()




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]=0      ):
							if str(__lowerCAmelCase      ).startswith('mps'      ):
													__snake_case						 =   torch.manual_seed(__lowerCAmelCase      )
							else:
													__snake_case						 =   torch.Generator(device=__lowerCAmelCase      ).manual_seed(__lowerCAmelCase      )

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

							__snake_case						 =   {
							    '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   lowercase__   (       self     :  Optional[Any]      ):
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )




	def   lowercase__   (       self     :  Tuple      ):
							self._test_save_load_optional_components()




	@unittest.skipIf(torch_device != 'cuda'       ,		reason='float16 requires CUDA'      )
	def   lowercase__   (       self     :  Optional[int]      ):
							# 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   lowercase__   (       self     :  Any      ):
							self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )




	def   lowercase__   (       self     :  Tuple      ):
							self._test_save_load_local()




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







'''simple docstring'''


from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

