





'''simple docstring'''

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    UpperCAmelCase	(       UpperCamelCase__			,       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				IFInpaintingPipeline
 __lowercase   		=				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
 __lowercase   		=				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowercase   		=				PipelineTesterMixin.required_optional_params - {"""latents"""}
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[Any]:
     return self._get_dummy_components()
 def      UpperCAmelCase_							(				self      :Optional[Any]						,       lowercase_      :int						,       lowercase_      :Tuple=0							)->							Optional[int]:
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)

     A__				=			floats_tensor((1, 3, 32, 32)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     A__				=			floats_tensor((1, 3, 32, 32)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)

     A__				=			{
         "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      UpperCAmelCase_							(				self      :Tuple							)->							int:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)
 def      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != "cuda"						,       reason="float16 requires CUDA"							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							int:
     # 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      UpperCAmelCase_							(				self      :List[Any]							)->							List[str]:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Any:
     self._test_save_load_local()



 def      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2						,       )










'''simple docstring'''

from __future__ import annotations






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :int							)->							None:
     A__				=			order

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

     # x[n-1] ... x[n-k]
     A__				=			[0.0] * self.order
     # y[n-1] ... y[n-k]
     A__				=			[0.0] * self.order
 def      UpperCAmelCase_							(				self      :List[str]						,       lowercase_      :list[float]						,       lowercase_      :list[float]							)->							None:
     if len(lowercase_							) < self.order:
         A__				=			[1.0, *a_coeffs]

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

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

     A__				=			a_coeffs
     A__				=			b_coeffs



 def      UpperCAmelCase_							(				self      :Optional[int]						,       lowercase_      :float							)->							float:
     A__				=			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]
         )

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

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

     A__				=			sample
     A__				=			result

     return result



