


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 	lowerCamelCase_       (				lowerCAmelCase__     ,     lowerCAmelCase__     ,     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						)   ->			Union[str, Any]:







								'''simple docstring'''

								return self._get_dummy_components()






					def A      (				self ,       snake_case_ ,       snake_case_=0						)   ->			List[Any]:







								'''simple docstring'''

								if str(snake_case_						).startswith('''mps'''						):
											__lowercase 					=   torch.manual_seed(snake_case_						)
								else:
											__lowercase 					=   torch.Generator(device=snake_case_						).manual_seed(snake_case_						)

								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(snake_case_						)						).to(snake_case_						)
								__lowercase 					=   floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(snake_case_						)						).to(snake_case_						)

								__lowercase 					=   {
								    '''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						)   ->			Any:







								'''simple docstring'''

								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)






					def A      (				self						)   ->			Union[str, Any]:







								'''simple docstring'''

								self._test_save_load_optional_components()






					@unittest.skipIf(torch_device != '''cuda''' ,       reason='''float16 requires CUDA'''						)
					def A      (				self						)   ->			Dict:







								'''simple docstring'''

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






					def A      (				self						)   ->			List[str]:







								'''simple docstring'''

								self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								self._test_save_load_local()






					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a : List[str]		=				{
    '''configuration_table_transformer''': [
        '''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TableTransformerConfig''',
        '''TableTransformerOnnxConfig''',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Union[str, Any]		=				[
	    '''TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TableTransformerForObjectDetection''',
	    '''TableTransformerModel''',
	    '''TableTransformerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_table_transformer import (
	    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TableTransformerConfig,
	    TableTransformerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TableTransformerForObjectDetection,
		    TableTransformerModel,
		    TableTransformerPreTrainedModel,
		)

else:
	import sys

	a : Any		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


