





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    (    A__			,				A__			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						IFImgaImgSuperResolutionPipeline
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			lowerCAmelCase_							     =						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""} )
			lowerCAmelCase_							     =						PipelineTesterMixin.required_optional_params - {"""latents"""}





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._get_superresolution_dummy_components()





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(__snake_case							).startswith('''mps'''							):
					lowerCamelCase__								=		torch.manual_seed(__snake_case							)
				else:
					lowerCamelCase__								=		torch.Generator(device=__snake_case							).manual_seed(__snake_case							)

				lowerCamelCase__								=		floats_tensor((1, 3, 3_2, 3_2)       ,  rng=random.Random(__snake_case							)							).to(__snake_case							)
				lowerCamelCase__								=		floats_tensor((1, 3, 1_6, 1_6)       ,  rng=random.Random(__snake_case							)							).to(__snake_case							)

				lowerCamelCase__								=		{
				    '''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 __future__ import annotations


def  snake_case_     (					_lowerCAmelCase  :						str   ,      _lowerCAmelCase  :						str  )      ->   bool:
							UpperCAmelCase :       str 							=		get_failure_array(_lowerCAmelCase  )

							# 2) Step through text searching for pattern
							UpperCAmelCase							,		UpperCAmelCase :       Optional[Any] 							=		0, 0  # index into text, pattern
							while i < len(_lowerCAmelCase  ):
														if pattern[j] == text[i]:
																					if j == (len(_lowerCAmelCase  ) - 1):
																												return True
																					j += 1

														# if this is a prefix in our pattern
														# just go back far enough to continue
														elif j > 0:
																					UpperCAmelCase :       Optional[Any] 							=		failure[j - 1]
																					continue
														i += 1
							return False


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   list[int]:
							UpperCAmelCase :       Optional[Any] 							=		[0]
							UpperCAmelCase :       str 							=		0
							UpperCAmelCase :       List[str] 							=		1
							while j < len(_lowerCAmelCase  ):
														if pattern[i] == pattern[j]:
																					i += 1
														elif i > 0:
																					UpperCAmelCase :       Union[str, Any] 							=		failure[i - 1]
																					continue
														j += 1
														failure.append(_lowerCAmelCase  )
							return failure


if __name__ == "__main__":
						# Test 1)
						UpperCamelCase__:   str								=			"abc1abc12"
						UpperCamelCase__:   str								=			"alskfjaldsabc1abc1abc12k23adsfabcabc"
						UpperCamelCase__:   Any								=			"alskfjaldsk23adsfabcabc"
						assert kmp(pattern, texta) and not kmp(pattern, texta)

						# Test 2)
						UpperCamelCase__:   Tuple								=			"ABABX"
						UpperCamelCase__:   Union[str, Any]								=			"ABABZABABYABABX"
						assert kmp(pattern, text)

						# Test 3)
						UpperCamelCase__:   Any								=			"AAAB"
						UpperCamelCase__:   str								=			"ABAAAAAB"
						assert kmp(pattern, text)

						# Test 4)
						UpperCamelCase__:   int								=			"abcdabcy"
						UpperCamelCase__:   Any								=			"abcxabcdabxabcdabcdabcy"
						assert kmp(pattern, text)

						# Test 5)
						UpperCamelCase__:   List[str]								=			"aabaabaaa"
						assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]





