

"""simple docstring"""





from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)




@dataclass
class 					_lowerCAmelCase  (	lowercase   ):







 """simple docstring"""







 __UpperCAmelCase	:					Union[List[PIL.Image.Image], np.ndarray]
 __UpperCAmelCase	:					Optional[List[bool]]


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .pipeline_cycle_diffusion import CycleDiffusionPipeline
   from .pipeline_stable_diffusion import StableDiffusionPipeline
   from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
   from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
   from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
   from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
   from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
   from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
   from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
   from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
   from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
   from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
   from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
   from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
   from .pipeline_stable_unclip import StableUnCLIPPipeline
   from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
   from .safety_checker import StableDiffusionSafetyChecker
   from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
   from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
   if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.26.0')):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import (
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionPixaPixZeroPipeline,
   )
else:
   from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
   from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
   from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
   if not (
       is_torch_available()
       and is_transformers_available()
       and is_k_diffusion_available()
       and is_k_diffusion_version('>=', '0.0.12')
   ):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not (is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_onnx_objects import *  # noqa F403
else:
   from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
   from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
   from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
   from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
   from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
   import flax




   @flax.struct.dataclass
   class 					_lowerCAmelCase  (	lowercase   ):







    """simple docstring"""







    __UpperCAmelCase	:					np.ndarray
    __UpperCAmelCase	:					List[bool]







   from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
   from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
   from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
   from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
   from .safety_checker_flax import FlaxStableDiffusionSafetyChecker






"""simple docstring"""




import math
import unittest

def lowercase_  (   _snake_case						):
							assert isinstance(_snake_case   ,_snake_case						) and (
							    number >= 0
							), "'number' must been an int and positive"

							if 1 < number < 4:
														# 2 and 3 are primes
														return True
							elif number < 2 or number % 2 == 0 or number % 3 == 0:
														# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
														return False

							# All primes number are in format of 6k +/- 1
							for i in range(5   ,int(math.sqrt(_snake_case						) + 1						)   ,6						):
														if number % i == 0 or number % (i + 2) == 0:
																					return False
							return True



class 	lowerCAmelCase_						(unittest.TestCase  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




											self.assertTrue(is_prime(2    )    )
											self.assertTrue(is_prime(3    )    )
											self.assertTrue(is_prime(5    )    )
											self.assertTrue(is_prime(7    )    )
											self.assertTrue(is_prime(11    )    )
											self.assertTrue(is_prime(13    )    )
											self.assertTrue(is_prime(17    )    )
											self.assertTrue(is_prime(19    )    )
											self.assertTrue(is_prime(23    )    )
											self.assertTrue(is_prime(29    )    )



				def 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											with self.assertRaises(SCREAMING_SNAKE_CASE__    ):
																		is_prime(-19    )
											self.assertFalse(
											    is_prime(0    )   ,       """Zero doesn't have any positive factors, primes must have exactly two."""   ,       )
											self.assertFalse(
											    is_prime(1    )   ,       """One only has 1 positive factor, primes must have exactly two."""   ,       )
											self.assertFalse(is_prime(2 * 2    )    )
											self.assertFalse(is_prime(2 * 3    )    )
											self.assertFalse(is_prime(3 * 3    )    )
											self.assertFalse(is_prime(3 * 5    )    )
											self.assertFalse(is_prime(3 * 5 * 7    )    )


if __name__ == "__main__":
					unittest.main()




