



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase__     = logging.get_logger(__name__)

UpperCAmelCase__     = {
    "microsoft/focalnet-tiny": "https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json",
}







class     __lowerCAmelCase					(    A       ,		A		):
			UpperCamelCase           =  '''focalnet'''



			def __init__(		self     :						str				,	A     :						Any=2_24				,	A     :						Optional[Any]=4				,	A     :						List[Any]=3				,	A     :						int=96				,	A     :						Any=False				,	A     :						Union[str, Any]=[1_92, 3_84, 7_68, 7_68]				,	A     :						Optional[int]=[2, 2, 6, 2]				,	A     :						Any=[2, 2, 2, 2]				,	A     :						Dict=[3, 3, 3, 3]				,	A     :						List[str]="gelu"				,	A     :						Dict=4.0				,	A     :						List[str]=0.0				,	A     :						List[Any]=0.1				,	A     :						Optional[int]=False				,	A     :						Union[str, Any]=1E-4				,	A     :						Any=False				,	A     :						Dict=False				,	A     :						Dict=False				,	A     :						List[Any]=0.0_2				,	A     :						Union[str, Any]=1E-5				,	A     :						Optional[Any]=32				,	A     :						Union[str, Any]=None				,	A     :						Dict=None				,	**A     :						Optional[Any]				,	)     ->					int:



						"""simple docstring"""






						super().__init__(**A)

						_UpperCAmelCase	 =     image_size
						_UpperCAmelCase	 =     patch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     embed_dim
						_UpperCAmelCase	 =     use_conv_embed
						_UpperCAmelCase	 =     hidden_sizes
						_UpperCAmelCase	 =     depths
						_UpperCAmelCase	 =     focal_levels
						_UpperCAmelCase	 =     focal_windows
						_UpperCAmelCase	 =     hidden_act
						_UpperCAmelCase	 =     mlp_ratio
						_UpperCAmelCase	 =     hidden_dropout_prob
						_UpperCAmelCase	 =     drop_path_rate
						_UpperCAmelCase	 =     use_layerscale
						_UpperCAmelCase	 =     layerscale_value
						_UpperCAmelCase	 =     use_post_layernorm
						_UpperCAmelCase	 =     use_post_layernorm_in_modulation
						_UpperCAmelCase	 =     normalize_modulator
						_UpperCAmelCase	 =     initializer_range
						_UpperCAmelCase	 =     layer_norm_eps
						_UpperCAmelCase	 =     encoder_stride
						_UpperCAmelCase	 =     ['stem'] + [F"stage{idx}" for idx in range(1				,	len(self.depths) + 1)]
						_UpperCAmelCase ,  _UpperCAmelCase	 =     get_aligned_output_features_output_indices(
						    out_features=A				,	out_indices=A				,	stage_names=self.stage_names)





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



			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						return self._get_dummy_components()



			def 					_lowerCamelCase (		self     :						Any				,	A     :						int				,	A     :						Dict=0)     ->					Tuple:



						"""simple docstring"""






						if str(A).startswith('mps'):
									_UpperCAmelCase	 =     torch.manual_seed(A)
						else:
									_UpperCAmelCase	 =     torch.Generator(device=A).manual_seed(A)

						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)
						_UpperCAmelCase	 =     floats_tensor((1, 3, 32, 32)				,	rng=random.Random(A)).to(A)

						_UpperCAmelCase	 =     {
						    '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 					_lowerCamelCase (		self     :						List[str])     ->					Union[str, Any]:



						"""simple docstring"""






						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Optional[int]:



						"""simple docstring"""






						self._test_save_load_optional_components()



			@unittest.skipIf(torch_device != 'cuda'				,	reason='float16 requires CUDA')
			def 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






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



			def 					_lowerCamelCase (		self     :						Optional[int])     ->					Tuple:



						"""simple docstring"""






						self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)



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



						"""simple docstring"""






						self._test_save_load_local()



			def 					_lowerCamelCase (		self     :						int)     ->					Tuple:



						"""simple docstring"""






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