'''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 			__A (					UpperCamelCase__						,      UpperCamelCase__						,      unittest.TestCase      ):
    a__			:		int			  =    IFImgaImgSuperResolutionPipeline
    a__			:		List[Any]			  =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
    a__			:		List[str]			  =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
    a__			:		Dict			  =    PipelineTesterMixin.required_optional_params - {"""latents"""}



    def    _lowercase		(self       :       Tuple					):
         return self._get_superresolution_dummy_components()



    def    _lowercase		(self       :       Dict  ,     __a       :       str  ,     __a       :       Dict=0					):
         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, 16, 16)  ,     rng=random.Random(__a					)					).to(__a					)

         UpperCAmelCase_			       =  {
             "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    _lowercase		(self       :       Tuple					):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3					)



    def    _lowercase		(self       :       Tuple					):
         self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != "cuda"  ,     reason="float16 requires CUDA"					)
    def    _lowercase		(self       :       Dict					):
         # 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    _lowercase		(self       :       List[Any]					):
         self._test_attention_slicing_forward_pass(expected_max_diff=1E-2					)



    def    _lowercase		(self       :       Tuple					):
         self._test_save_load_local()






    def    _lowercase		(self       :       Dict					):
         self._test_inference_batch_single_identical(
             expected_max_diff=1E-2  ,     )










from __future__ import annotations

from collections.abc import Callable

lowercase__							:	Optional[Any]											=		list[list[float | int]]


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(size + 1				)] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for row in range(_A				):
		for col in range(_A				):
			snake_case_						       =	matrix[row][col]

		snake_case_						       =	vector[row][0]

	snake_case_						       =	0
	snake_case_						       =	0
	while row < size and col < size:
		# pivoting
		snake_case_						       =	max((abs(augmented[rowa][col]				), rowa) for rowa in range(_A	,      _A				)				)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_       , snake_case_						       =	augmented[pivot_row], augmented[row]

		for rowa in range(row + 1	,      _A				):
			snake_case_						       =	augmented[rowa][col] / augmented[row][col]
			snake_case_						       =	0
			for cola in range(col + 1	,      size + 1				):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1	,      _A				):
		for row in range(_A				):
			snake_case_						       =	augmented[row][col] / augmented[col][col]
			for cola in range(_A	,      size + 1				):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]	,      10				)] for row in range(_A				)
	]


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	len(_A				)
	snake_case_						       =	[[0 for _ in range(_A				)] for _ in range(_A				)]
	snake_case_						       =	[[0] for _ in range(_A				)]
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42
	snake_case_						       =	42

	for x_val, y_val in enumerate(_A				):
		for col in range(_A				):
			snake_case_						       =	(x_val + 1) ** (size - col - 1)
		snake_case_						       =	y_val

	snake_case_						       =	solve(_A	,      _A				)

	def interpolated_func(_A				) -> int:
		return sum(
		    round(coeffs[x_val][0]				) * (var ** (size - x_val - 1))
		    for x_val in range(_A				)				)

	return interpolated_func


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)


def 		lowerCamelCase__       (						_A = question_function	,      _A = 10				):







	'''simple docstring'''





	snake_case_						       =	[func(_A				) for x_val in range(1	,      order + 1				)]

	snake_case_						       =	[
	    interpolate(data_points[:max_coeff]				) for max_coeff in range(1	,      order + 1				)
	]

	snake_case_						       =	0
	snake_case_						       =	42
	snake_case_						       =	42

	for poly in polynomials:
		snake_case_						       =	1
		while func(_A				) == poly(_A				):
			x_val += 1

		ret += poly(_A				)

	return ret


if __name__ == "__main__":
		print(f'''{solution() = }''')
