






"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 					_UpperCAmelCase       ( _A   ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:				Dict							    =   DDIMPipeline
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					SCREAMING_SNAKE_CASE_		:				str							    =   PipelineTesterMixin.required_optional_params - {
					    "num_images_per_prompt",
					    "latents",
					    "callback",
					    "callback_steps",
					}
					SCREAMING_SNAKE_CASE_		:				int							    =   UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					SCREAMING_SNAKE_CASE_		:				Dict							    =   False




					def   A  (				self     :						Union[str, Any]   )  ->  Union[str, Any]:
							torch.manual_seed(0   )
							lowercase_ : Tuple							       =   UNetaDModel(
							    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,						)
							lowercase_ : Dict							       =   DDIMScheduler()
							lowercase_ : Any							       =   {'''unet''': unet, '''scheduler''': scheduler}
							return components




					def   A  (				self     :						List[Any]    ,						A     :						Optional[Any]    ,						A     :						str=0   )  ->  Optional[Any]:
							if str(A   ).startswith('''mps'''   ):
									lowercase_ : str							       =   torch.manual_seed(A   )
							else:
									lowercase_ : List[Any]							       =   torch.Generator(device=A   ).manual_seed(A   )
							lowercase_ : Union[str, Any]							       =   {
							    '''batch_size''': 1,
							    '''generator''': generator,
							    '''num_inference_steps''': 2,
							    '''output_type''': '''numpy''',
							}
							return inputs




					def   A  (				self     :						int   )  ->  Optional[int]:
							lowercase_ : List[Any]							       =   '''cpu'''

							lowercase_ : Optional[Any]							       =   self.get_dummy_components()
							lowercase_ : str							       =   self.pipeline_class(**A   )
							pipe.to(A   )
							pipe.set_progress_bar_config(disable=A   )

							lowercase_ : List[Any]							       =   self.get_dummy_inputs(A   )
							lowercase_ : List[Any]							       =   pipe(**A   ).images
							lowercase_ : Dict							       =   image[0, -3:, -3:, -1]

							self.assertEqual(image.shape    ,						(1, 32, 32, 3)   )
							lowercase_ : Dict							       =   np.array(
							    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]   )
							lowercase_ : Dict							       =   np.abs(image_slice.flatten() - expected_slice   ).max()
							self.assertLessEqual(A    ,						1e-3   )




					def   A  (				self     :						Optional[int]   )  ->  Optional[Any]:
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3   )




					def   A  (				self     :						Optional[int]   )  ->  Any:
							super().test_save_load_local(expected_max_difference=3e-3   )




					def   A  (				self     :						Tuple   )  ->  str:
							super().test_save_load_optional_components(expected_max_difference=3e-3   )






					def   A  (				self     :						Dict   )  ->  List[str]:
							super().test_inference_batch_single_identical(expected_max_diff=3e-3   )





@slow
@require_torch_gpu
class 					_UpperCAmelCase       ( unittest.TestCase							):




					def   A  (				self     :						List[Any]   )  ->  Any:
							lowercase_ : Optional[Any]							       =   '''google/ddpm-cifar10-32'''

							lowercase_ : Optional[int]							       =   UNetaDModel.from_pretrained(A   )
							lowercase_ : Optional[int]							       =   DDIMScheduler()

							lowercase_ : Optional[int]							       =   DDIMPipeline(unet=A    ,						scheduler=A   )
							ddim.to(A   )
							ddim.set_progress_bar_config(disable=A   )

							lowercase_ : Optional[Any]							       =   torch.manual_seed(0   )
							lowercase_ : Tuple							       =   ddim(generator=A    ,						eta=0.0    ,						output_type='''numpy'''   ).images

							lowercase_ : List[str]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowercase_ : Any							       =   np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2






					def   A  (				self     :						Any   )  ->  Dict:
							lowercase_ : int							       =   '''google/ddpm-ema-bedroom-256'''

							lowercase_ : Tuple							       =   UNetaDModel.from_pretrained(A   )
							lowercase_ : List[Any]							       =   DDIMScheduler.from_pretrained(A   )

							lowercase_ : str							       =   DDIMPipeline(unet=A    ,						scheduler=A   )
							ddpm.to(A   )
							ddpm.set_progress_bar_config(disable=A   )

							lowercase_ : Tuple							       =   torch.manual_seed(0   )
							lowercase_ : Optional[Any]							       =   ddpm(generator=A    ,						output_type='''numpy'''   ).images

							lowercase_ : Optional[Any]							       =   image[0, -3:, -3:, -1]

							assert image.shape == (1, 2_56, 2_56, 3)
							lowercase_ : List[str]							       =   np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2







from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	bool:

		'''simple docstring'''







		__lowercase=			get_failure_array(lowercase__	)

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

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







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			[0]
		__lowercase=			0
		__lowercase=			1
		while j < len(lowercase__	):
				if pattern[i] == pattern[j]:
						i += 1
				elif i > 0:
						__lowercase=			failure[i - 1]
						continue
				j += 1
				failure.append(lowercase__	)
		return failure


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

	# Test 2)
	lowerCAmelCase           =		'''ABABX'''
	lowerCAmelCase           =		'''ABABZABABYABABX'''
	assert kmp(pattern, text)

	# Test 3)
	lowerCAmelCase           =		'''AAAB'''
	lowerCAmelCase           =		'''ABAAAAAB'''
	assert kmp(pattern, text)

	# Test 4)
	lowerCAmelCase           =		'''abcdabcy'''
	lowerCAmelCase           =		'''abcxabcdabxabcdabcdabcy'''
	assert kmp(pattern, text)

	# Test 5)
	lowerCAmelCase           =		'''aabaabaaa'''
	assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]






