"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class 						__UpperCAmelCase    (__A							,   __A							,   unittest.TestCase     ):







			'''simple docstring'''
			_UpperCamelCase  :						Optional[int]					     =				IFImgaImgSuperResolutionPipeline
			_UpperCamelCase  :						Optional[int]					     =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
			_UpperCamelCase  :						Dict					     =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}     )
			_UpperCamelCase  :						Tuple					     =				PipelineTesterMixin.required_optional_params - {'latents'}





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										return self._get_superresolution_dummy_components()





			def 		lowerCamelCase					(			self   ,      snake_case_   ,      snake_case_=0							):







										'''simple docstring'''


										if str(snake_case_							).startswith("""mps"""							):
																	A__       :     Optional[int]								=			torch.manual_seed(snake_case_							)
										else:
																	A__       :     List[Any]								=			torch.Generator(device=snake_case_							).manual_seed(snake_case_							)

										A__       :     int								=			floats_tensor((1, 3, 32, 32)   ,      rng=random.Random(snake_case_							)							).to(snake_case_							)
										A__       :     Optional[int]								=			floats_tensor((1, 3, 16, 16)   ,      rng=random.Random(snake_case_							)							).to(snake_case_							)

										A__       :     Optional[Any]								=			{
										    """prompt""": """A painting of a squirrel eating a burger""",
										    """image""": image,
										    """original_image""": original_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							):







										'''simple docstring'''


										self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self._test_save_load_optional_components()





			@unittest.skipIf(torch_device != """cuda"""   ,      reason="""float16 requires CUDA"""							)
			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self._test_attention_slicing_forward_pass(expected_max_diff=1E-2							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										self._test_save_load_local()





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


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






"""simple docstring"""



from collections.abc import Sequence

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							return sum(c * (x**i) for i, c in enumerate(lowerCAmelCase							)							)

def  _A(		lowerCAmelCase			,  lowerCAmelCase							):
							A__       :     str								=			0.0
							for coeff in reversed(lowerCAmelCase							):
														A__       :     Optional[int]								=			result * x + coeff
							return result


if __name__ == "__main__":
				_UpperCamelCase								=		(0.0, 0.0, 5.0, 9.3, 7.0)
				_UpperCamelCase								=		10.0
				print(evaluate_poly(poly, x))
				print(horner(poly, x))





