


'''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  lowerCAmelCase				(				a      ,				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[str]			      = IFInpaintingSuperResolutionPipeline
	_lowerCamelCase   :					Tuple			      = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
	_lowerCamelCase   :					Optional[Any]			      = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}							)
	_lowerCamelCase   :					int			      = PipelineTesterMixin.required_optional_params - {"""latents"""}



	def 				lowercase     (					self    ):
		return self._get_superresolution_dummy_components()



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__=0    ):
		if str(snake_case__    ).startswith('mps'    ):
			lowerCAmelCase					:    str      							= torch.manual_seed(snake_case__    )
		else:
			lowerCAmelCase					:    Union[str, Any]      							= torch.Generator(device=snake_case__    ).manual_seed(snake_case__    )

		lowerCAmelCase					:    Any      							= floats_tensor((1, 3, 16, 16) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    str      							= floats_tensor((1, 3, 32, 32) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )
		lowerCAmelCase					:    Dict      							= floats_tensor((1, 3, 32, 32) ,					rng=random.Random(snake_case__    )    ).to(snake_case__    )

		lowerCAmelCase					:    Any      							= {
		    '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 				lowercase     (					self    ):
		self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )



	def 				lowercase     (					self    ):
		self._test_save_load_optional_components()



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



	def 				lowercase     (					self    ):
		self._test_save_load_local()



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




'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowerCAmelCase  :		int		     =	{
    'configuration_nezha': ['NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NezhaConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowerCAmelCase  :		Tuple		     =	[
				    'NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'NezhaForNextSentencePrediction',
				    'NezhaForMaskedLM',
				    'NezhaForPreTraining',
				    'NezhaForMultipleChoice',
				    'NezhaForQuestionAnswering',
				    'NezhaForSequenceClassification',
				    'NezhaForTokenClassification',
				    'NezhaModel',
				    'NezhaPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_nezha import (
								    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    NezhaForMaskedLM,
								    NezhaForMultipleChoice,
								    NezhaForNextSentencePrediction,
								    NezhaForPreTraining,
								    NezhaForQuestionAnswering,
								    NezhaForSequenceClassification,
								    NezhaForTokenClassification,
								    NezhaModel,
								    NezhaPreTrainedModel,
								)


else:
				import sys

				_lowerCAmelCase  :		str		     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
