






'''simple docstring'''







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__lowerCAmelCase       		=      logging.get_logger(__name__)





class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				"upernet"






			def __init__( self      :			Dict  ,						__SCREAMING_SNAKE_CASE      :			Dict=None  ,						__SCREAMING_SNAKE_CASE      :			int=512  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=0.02  ,						__SCREAMING_SNAKE_CASE      :			Dict=[1, 2, 3, 6]  ,						__SCREAMING_SNAKE_CASE      :			List[str]=True  ,						__SCREAMING_SNAKE_CASE      :			Tuple=0.4  ,						__SCREAMING_SNAKE_CASE      :			Optional[int]=384  ,						__SCREAMING_SNAKE_CASE      :			str=256  ,						__SCREAMING_SNAKE_CASE      :			Dict=1  ,						__SCREAMING_SNAKE_CASE      :			Tuple=False  ,						__SCREAMING_SNAKE_CASE      :			List[Any]=255  ,						**__SCREAMING_SNAKE_CASE      :			Optional[int]  ,						)			->			str:
							super().__init__(**UpperCamelCase__					)

							if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
											a_		:  Dict											=      CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']					)
							elif isinstance(UpperCamelCase__  ,						UpperCamelCase__					):
											a_		:  Any											=      backbone_config.get('''model_type'''					)
											a_		:  Union[str, Any]											=      CONFIG_MAPPING[backbone_model_type]
											a_		:  Optional[Any]											=      config_class.from_dict(UpperCamelCase__					)

							a_		:  int											=      backbone_config
							a_		:  Optional[int]											=      hidden_size
							a_		:  str											=      initializer_range
							a_		:  Dict											=      pool_scales
							a_		:  Optional[int]											=      use_auxiliary_head
							a_		:  Any											=      auxiliary_loss_weight
							a_		:  Tuple											=      auxiliary_in_channels
							a_		:  int											=      auxiliary_channels
							a_		:  List[str]											=      auxiliary_num_convs
							a_		:  Optional[Any]											=      auxiliary_concat_input
							a_		:  List[Any]											=      loss_ignore_index







			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			List[Any]:
							a_		:  int											=      copy.deepcopy(self.__dict__					)
							a_		:  Dict											=      self.backbone_config.to_dict()
							a_		:  Tuple											=      self.__class__.model_type
							return output












'''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 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_	,   SCREAMING_SNAKE_CASE_	,   unittest.TestCase       ):
			snake_case__  =				IFInpaintingSuperResolutionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
			snake_case__  =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}       )
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}






			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			List[str]:
							return self._get_superresolution_dummy_components()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]  ,						__SCREAMING_SNAKE_CASE      :			Optional[Any]  ,						__SCREAMING_SNAKE_CASE      :			Dict=0					)			->			List[Any]:
							if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
											a_		:  Optional[int]											=      torch.manual_seed(__SCREAMING_SNAKE_CASE					)
							else:
											a_		:  str											=      torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)

							a_		:  Dict											=      floats_tensor((1, 3, 16, 16)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Union[str, Any]											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
							a_		:  Tuple											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)

							a_		:  Union[str, 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 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[str]:
							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			int					)			->			int:
							self._test_save_load_optional_components()






			@unittest.skipIf(torch_device != '''cuda'''  ,						reason='''float16 requires CUDA'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Tuple:
							# 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 			SCREAMING_SNAKE_CASE      ( self      :			List[str]					)			->			Optional[int]:
							self._test_attention_slicing_forward_pass(expected_max_diff=1e-2					)






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Optional[Any]:
							self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Optional[Any]:
							self._test_inference_batch_single_identical(
							    expected_max_diff=1e-2  ,						)




