



from __future__ import annotations







class     __lowerCAmelCase					:



			def __init__(		self     :						List[Any]				,	A     :						int)     ->					None:



						"""simple docstring"""






						_UpperCAmelCase	 =     order

						# a_{0} ... a_{k}
						_UpperCAmelCase	 =     [1.0] + [0.0] * order
						# b_{0} ... b_{k}
						_UpperCAmelCase	 =     [1.0] + [0.0] * order

						# x[n-1] ... x[n-k]
						_UpperCAmelCase	 =     [0.0] * self.order
						# y[n-1] ... y[n-k]
						_UpperCAmelCase	 =     [0.0] * self.order



			def 					_lowerCamelCase (		self     :						Any				,	A     :						list[float]				,	A     :						list[float])     ->					None:



						"""simple docstring"""






						if len(A) < self.order:
									_UpperCAmelCase	 =     [1.0, *a_coeffs]

						if len(A) != self.order + 1:
									_UpperCAmelCase	 =     (
									    F"Expected a_coeffs to have {self.order + 1} elements "
									    F"for {self.order}-order filter, got {len(A)}"
									)
									raise ValueError(A)

						if len(A) != self.order + 1:
									_UpperCAmelCase	 =     (
									    F"Expected b_coeffs to have {self.order + 1} elements "
									    F"for {self.order}-order filter, got {len(A)}"
									)
									raise ValueError(A)

						_UpperCAmelCase	 =     a_coeffs
						_UpperCAmelCase	 =     b_coeffs



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						float)     ->					float:



						"""simple docstring"""






						_UpperCAmelCase	 =     0.0

						# Start at index 1 and do index 0 at the end.
						for i in range(1				,	self.order + 1):
									result += (
									    self.b_coeffs[i] * self.input_history[i - 1]
									    - self.a_coeffs[i] * self.output_history[i - 1]
									)

						_UpperCAmelCase	 =     (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

						_UpperCAmelCase	 =     self.input_history[:-1]
						_UpperCAmelCase	 =     self.output_history[:-1]

						_UpperCAmelCase	 =     sample
						_UpperCAmelCase	 =     result

						return result





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				,	)
