
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       _snake_case					(     snake_case				,						snake_case				,						unittest.TestCase				):
			UpperCamelCase__          =    IFImgaImgSuperResolutionPipeline
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
			UpperCamelCase__          =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}				)
			UpperCamelCase__          =    PipelineTesterMixin.required_optional_params - {'latents'}







			def 			SCREAMING_SNAKE_CASE    (				self		):
				return self._get_superresolution_dummy_components()







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a=0		):
				if str(_a		).startswith("mps"		):
					__magic_name__		:		Dict	   =     torch.manual_seed(_a		)
				else:
					__magic_name__		:		Any	   =     torch.Generator(device=_a		).manual_seed(_a		)

				__magic_name__		:		Union[str, Any]	   =     floats_tensor((1, 3, 32, 32)						,   rng=random.Random(_a		)		).to(_a		)
				__magic_name__		:		Dict	   =     floats_tensor((1, 3, 16, 16)						,   rng=random.Random(_a		)		).to(_a		)

				__magic_name__		:		List[str]	   =     {
				    "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 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_save_load_optional_components()







			@unittest.skipIf(torch_device != "cuda"						,   reason="float16 requires CUDA"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				# 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 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2		)







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_save_load_local()







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2						,   )


def   lowerCAmelCase_						(					_snake_case      :  list[list[int | float]]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     len(matrix[0]   )
	__magic_name__		:		Union[str, Any]	   =     min(_snake_case      ,   _snake_case   )

	for row in range(_snake_case   ):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     matrix[col][row] / matrix[row][row]
				for i in range(_snake_case      ,   _snake_case   ):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			__magic_name__		:		str	   =     True
			for i in range(row + 1      ,   _snake_case   ):
				if matrix[i][row] != 0:
					__magic_name__   ,    __magic_name__		:		List[str]	   =     matrix[i], matrix[row]
					__magic_name__		:		Union[str, Any]	   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_snake_case   ):
					__magic_name__		:		Any	   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()
