


"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class  __snake_case      (snake_case_ ,		snake_case_ ,		snake_case_ ,		unittest.TestCase     ):
							__a				   =					StableDiffusionModelEditingPipeline
							__a				   =					TEXT_TO_IMAGE_PARAMS
							__a				   =					TEXT_TO_IMAGE_BATCH_PARAMS
							__a				   =					TEXT_TO_IMAGE_IMAGE_PARAMS
							__a				   =					TEXT_TO_IMAGE_IMAGE_PARAMS




							def      __a      (    self:						Tuple      ):
										torch.manual_seed(0      )
										__lowerCamelCase        =			UNetaDConditionModel(
										    block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=4    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       )
										__lowerCamelCase        =			DDIMScheduler()
										torch.manual_seed(0      )
										__lowerCamelCase        =			AutoencoderKL(
										    block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       )
										torch.manual_seed(0      )
										__lowerCamelCase        =			CLIPTextConfig(
										    bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1E-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=10_00    ,       )
										__lowerCamelCase        =			CLIPTextModel(A_      )
										__lowerCamelCase        =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

										__lowerCamelCase        =			{
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}
										return components




							def      __a      (    self:						List[Any]    ,       A_:						Optional[Any]    ,       A_:						Any=0      ):
										__lowerCamelCase        =			torch.manual_seed(A_      )
										__lowerCamelCase        =			{
										    """prompt""": """A field of roses""",
										    """generator""": generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    """height""": None,
										    """width""": None,
										    """num_inference_steps""": 2,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}
										return inputs




							def      __a      (    self:						Any      ):
										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										__lowerCamelCase        =			self.get_dummy_components()
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
										__lowerCamelCase        =			sd_pipe.to(A_      )
										sd_pipe.set_progress_bar_config(disable=A_      )

										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
										__lowerCamelCase        =			sd_pipe(**A_      ).images
										__lowerCamelCase        =			image[0, -3:, -3:, -1]
										assert image.shape == (1, 64, 64, 3)

										__lowerCamelCase        =			np.array([0.4_755, 0.5_132, 0.4_976, 0.3_904, 0.3_554, 0.4_765, 0.5_139, 0.5_158, 0.4_889]      )

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




							def      __a      (    self:						int      ):
										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										__lowerCamelCase        =			self.get_dummy_components()
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
										__lowerCamelCase        =			sd_pipe.to(A_      )
										sd_pipe.set_progress_bar_config(disable=A_      )

										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
										__lowerCamelCase        =			"""french fries"""
										__lowerCamelCase        =			sd_pipe(**A_    ,       negative_prompt=A_      )
										__lowerCamelCase        =			output.images
										__lowerCamelCase        =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										__lowerCamelCase        =			np.array([0.4_992, 0.5_101, 0.5_004, 0.3_949, 0.3_604, 0.4_735, 0.5_216, 0.5_204, 0.4_913]      )

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




							def      __a      (    self:						Dict      ):
										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										__lowerCamelCase        =			self.get_dummy_components()
										__lowerCamelCase        =			EulerAncestralDiscreteScheduler(
										    beta_start=0.00_085    ,       beta_end=0.012    ,       beta_schedule="""scaled_linear"""      )
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
										__lowerCamelCase        =			sd_pipe.to(A_      )
										sd_pipe.set_progress_bar_config(disable=A_      )

										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
										__lowerCamelCase        =			sd_pipe(**A_      ).images
										__lowerCamelCase        =			image[0, -3:, -3:, -1]

										assert image.shape == (1, 64, 64, 3)

										__lowerCamelCase        =			np.array([0.4_747, 0.5_372, 0.4_779, 0.4_982, 0.5_543, 0.4_816, 0.5_238, 0.4_904, 0.5_027]      )

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




							def      __a      (    self:						Any      ):
										__lowerCamelCase        =			"""cpu"""  # ensure determinism for the device-dependent torch.Generator
										__lowerCamelCase        =			self.get_dummy_components()
										__lowerCamelCase        =			PNDMScheduler()
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline(**A_      )
										__lowerCamelCase        =			sd_pipe.to(A_      )
										sd_pipe.set_progress_bar_config(disable=A_      )

										__lowerCamelCase        =			self.get_dummy_inputs(A_      )
										# the pipeline does not expect pndm so test if it raises error.
										with self.assertRaises(A_      ):
													__lowerCamelCase        =			sd_pipe(**A_      ).images




							def      __a      (    self:						List[str]      ):
										super().test_inference_batch_single_identical(expected_max_diff=5E-3      )




							def      __a      (    self:						Dict      ):
										super().test_attention_slicing_forward_pass(expected_max_diff=5E-3      )




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




							def      __a      (    self:						Any      ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




							def      __a      (    self:						Tuple    ,       A_:						int=0      ):
										__lowerCamelCase        =			torch.manual_seed(A_      )
										__lowerCamelCase        =			{
										    """prompt""": """A field of roses""",
										    """generator""": generator,
										    """num_inference_steps""": 3,
										    """guidance_scale""": 7.5,
										    """output_type""": """numpy""",
										}
										return inputs




							def      __a      (    self:						Optional[Any]      ):
										__lowerCamelCase        =			"""CompVis/stable-diffusion-v1-4"""
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline.from_pretrained(A_    ,       safety_checker=A_      )
										pipe.to(A_      )
										pipe.set_progress_bar_config(disable=A_      )
										pipe.enable_attention_slicing()

										__lowerCamelCase        =			self.get_inputs()
										__lowerCamelCase        =			pipe(**A_      ).images
										__lowerCamelCase        =			image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										__lowerCamelCase        =			np.array(
										    [0.6_749_496, 0.6_386_453, 0.51_443_267, 0.66_094_905, 0.61_921_215, 0.5_491_332, 0.5_744_417, 0.58_075_106, 0.5_174_658]      )

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

										# make sure image changes after editing
										pipe.edit_model("""A pack of roses"""    ,       """A pack of blue roses"""      )

										__lowerCamelCase        =			pipe(**A_      ).images
										__lowerCamelCase        =			image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_12, 5_12, 3)

										assert np.abs(expected_slice - image_slice      ).max() > 1E-1




							def      __a      (    self:						Optional[int]      ):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										__lowerCamelCase        =			"""CompVis/stable-diffusion-v1-4"""
										__lowerCamelCase        =			DDIMScheduler.from_pretrained(A_    ,       subfolder="""scheduler"""      )
										__lowerCamelCase        =			StableDiffusionModelEditingPipeline.from_pretrained(
										    A_    ,       scheduler=A_    ,       safety_checker=A_      )
										__lowerCamelCase        =			pipe.to(A_      )
										pipe.set_progress_bar_config(disable=A_      )
										pipe.enable_attention_slicing(1      )
										pipe.enable_sequential_cpu_offload()

										__lowerCamelCase        =			self.get_inputs()
										__lowerCamelCase        =			pipe(**A_      )

										__lowerCamelCase        =			torch.cuda.max_memory_allocated()
										# make sure that less than 4.4 GB is allocated
										assert mem_bytes < 4.4 * 10**9








"""simple docstring"""




def 			a_   (  lowercase__   :str, lowercase__   :int = 13    ):
			__lowerCamelCase        =			""""""
			for c in s:
						if "A" <= c <= "Z":
									out += chr(ord("""A"""    ) + (ord(lowercase__    ) - ord("""A"""    ) + n) % 26    )
						elif "a" <= c <= "z":
									out += chr(ord("""a"""    ) + (ord(lowercase__    ) - ord("""a"""    ) + n) % 26    )
						else:
									out += c
			return out





def 			a_   (  ):
			__lowerCamelCase        =			input("""Enter message: """    )

			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
			print("""Encryption:""", lowercase__    )

			__lowerCamelCase        =			dencrypt(lowercase__, 13    )
			print("""Decryption: """, lowercase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()




