



'''simple docstring'''





import gc
import random
import tempfile
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 							_a							(      unittest.TestCase				):

			def     __snake_case					(self				)					->      Any:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			@property
			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Tuple       			=    1
						UpperCAmelCase_:		Dict       			=    3
						UpperCAmelCase_:		Optional[int]       			=    (32, 32)

						UpperCAmelCase_:		int       			=    floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0				)				).to(SCREAMING_SNAKE_CASE_				)
						return image

			@property
			def     __snake_case					(self				)					->      Optional[Any]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    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,      )
						return model

			@property
			def     __snake_case					(self				)					->      str:
						torch.manual_seed(0				)
						UpperCAmelCase_:		int       			=    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,      )
						return model

			@property
			def     __snake_case					(self				)					->      List[str]:
						torch.manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    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=1000,      )
						return CLIPTextModel(SCREAMING_SNAKE_CASE_				)

			@property
			def     __snake_case					(self				)					->      str:
						def extract(*SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_				):







									class 							_a							:

												def __init__(self				)					->      Any:
															UpperCAmelCase_:		Optional[Any]       			=    torch.ones([0]				)

												def     __snake_case					(self,      SCREAMING_SNAKE_CASE_				)					->      int:
															self.pixel_values.to(SCREAMING_SNAKE_CASE_				)
															return self






									return Out()

						return extract

			def     __snake_case					(self				)					->      List[Any]:
						UpperCAmelCase_:		Any       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Union[str, Any]       			=    DDIMScheduler(
						    beta_start=0.0_0_0_8_5,      beta_end=0.0_1_2,      beta_schedule="""scaled_linear""",      clip_sample=SCREAMING_SNAKE_CASE_,      set_alpha_to_one=SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		str       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""

						UpperCAmelCase_:		Tuple       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Dict       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)
						UpperCAmelCase_:		Tuple       			=    output.images

						UpperCAmelCase_:		List[Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[str]       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		int       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[str]       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Dict       			=    np.array([0.5_7_5_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]				)

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

			def     __snake_case					(self				)					->      Any:
						UpperCAmelCase_:		List[Any]       			=    """cpu"""  # ensure determinism for the device-dependent torch.Generator
						UpperCAmelCase_:		Tuple       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Optional[int]       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    self.dummy_vae
						UpperCAmelCase_:		Any       			=    self.dummy_text_encoder
						UpperCAmelCase_:		List[Any]       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		List[str]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Union[str, Any]       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		List[Any]       			=    sd_pipe([prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np"""				)

						UpperCAmelCase_:		Optional[int]       			=    output.images

						UpperCAmelCase_:		Any       			=    torch.Generator(device=SCREAMING_SNAKE_CASE_				).manual_seed(0				)
						UpperCAmelCase_:		Tuple       			=    sd_pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=6.0,      num_inference_steps=2,      output_type="""np""",      return_dict=SCREAMING_SNAKE_CASE_,      )[0]

						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						UpperCAmelCase_:		Tuple       			=    np.array([0.5_1_2_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_4]				)

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

			def     __snake_case					(self				)					->      List[str]:
						UpperCAmelCase_:		Dict       			=    StableDiffusionPipeline.from_pretrained(
						    """hf-internal-testing/tiny-stable-diffusion-lms-pipe""",      safety_checker=SCREAMING_SNAKE_CASE_				)
						assert isinstance(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_				)
						assert isinstance(pipe.scheduler,      SCREAMING_SNAKE_CASE_				)
						assert pipe.safety_checker is None

						UpperCAmelCase_:		Optional[Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(SCREAMING_SNAKE_CASE_				)
									UpperCAmelCase_:		Optional[int]       			=    StableDiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE_				)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						UpperCAmelCase_:		Union[str, Any]       			=    pipe("""example prompt""",      num_inference_steps=2				).images[0]
						assert image is not None

			@unittest.skipIf(torch_device != """cuda""",      """This test requires a GPU"""				)
			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		Dict       			=    self.dummy_cond_unet
						UpperCAmelCase_:		Any       			=    PNDMScheduler(skip_prk_steps=SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[str]       			=    self.dummy_vae
						UpperCAmelCase_:		Tuple       			=    self.dummy_text_encoder
						UpperCAmelCase_:		Dict       			=    CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

						# put models in fp16
						UpperCAmelCase_:		Dict       			=    unet.half()
						UpperCAmelCase_:		Optional[Any]       			=    vae.half()
						UpperCAmelCase_:		Union[str, Any]       			=    bert.half()

						# make sure here that pndm scheduler skips prk
						UpperCAmelCase_:		str       			=    StableDiffusionPipeline(
						    unet=SCREAMING_SNAKE_CASE_,      scheduler=SCREAMING_SNAKE_CASE_,      vae=SCREAMING_SNAKE_CASE_,      text_encoder=SCREAMING_SNAKE_CASE_,      tokenizer=SCREAMING_SNAKE_CASE_,      safety_checker=SCREAMING_SNAKE_CASE_,      feature_extractor=self.dummy_extractor,      )
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe.to(SCREAMING_SNAKE_CASE_				)
						sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		str       			=    """A painting of a squirrel eating a burger"""
						UpperCAmelCase_:		Optional[Any]       			=    sd_pipe([prompt],      num_inference_steps=2,      output_type="""np"""				).images

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







@nightly
@require_torch_gpu
class 							_a							(      unittest.TestCase				):

			def     __snake_case					(self				)					->      Dict:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def     __snake_case					(self				)					->      str:
						UpperCAmelCase_:		Optional[int]       			=    """cuda"""
						UpperCAmelCase_:		List[str]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		int       			=    """a photo of a cat"""
						UpperCAmelCase_:		Tuple       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[str]       			=    3
						UpperCAmelCase_:		str       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.3_4_6_7_3_1_1_3,
						    0.3_8_4_9_2_7_3_3,
						    0.3_7_5_9_7_3_5_2,
						    0.3_4_0_8_6_3_3_5,
						    0.3_5_6_5_0_7_4_8,
						    0.3_5_5_7_9_2_0_5,
						    0.3_3_8_4_7_6_3,
						    0.3_4_3_4_0_2_3_6,
						    0.3_5_7_3_2_7_1,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		Any       			=    output.images
						UpperCAmelCase_:		Dict       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[int]       			=    [
						    0.4_1_8_8_7_8_2_6,
						    0.3_7_7_2_8_7_6_6,
						    0.3_0_1_3_8_2_7_2,
						    0.4_1_4_1_6_3_3_5,
						    0.4_1_6_6_4_9_8_5,
						    0.3_6_2_8_3_3_9_2,
						    0.3_6_1_9_1_2_4_6,
						    0.4_3_3_6_4_4_6_5,
						    0.4_3_0_0_1_7_3_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[Any]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Tuple       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		List[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """an image of a crowded boulevard, realistic, 4k"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": """crowd, crowded, people""",
						    """reverse_editing_direction""": True,
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 8.3,
						    """edit_threshold""": 0.9,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		Dict       			=    9
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		List[Any]       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		str       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Optional[Any]       			=    output.images
						UpperCAmelCase_:		List[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		int       			=    [
						    0.4_3_4_9_7_9_9_8,
						    0.9_1_8_1_4_0_6_5,
						    0.7_5_4_0_7_3_9,
						    0.5_5_5_8_0_2_0_5,
						    0.8_4_6_7_2_6_5,
						    0.5_3_8_9_6_9_1,
						    0.6_2_5_7_4_5_0_6,
						    0.5_8_8_9_7_7_6_3,
						    0.5_0_9_2_6_7_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Optional[int]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		int       			=    output.images
						UpperCAmelCase_:		Any       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		str       			=    [
						    0.3_0_8_9_7_1_9,
						    0.3_0_5_0_0_1_4_4,
						    0.2_9_0_1_6_0_4_2,
						    0.3_0_6_3_0_9_6_4,
						    0.3_2_5_6_8_7,
						    0.2_9_4_1_9_2_2_5,
						    0.2_9_0_8_0_9_1,
						    0.2_8_7_2_3_5_9_8,
						    0.2_7_6_9_6_2_9_4,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Tuple:
						UpperCAmelCase_:		Union[str, Any]       			=    """cuda"""
						UpperCAmelCase_:		Optional[Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""				)
						UpperCAmelCase_:		Optional[Any]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Tuple       			=    """a castle next to a river"""
						UpperCAmelCase_:		Optional[Any]       			=    {
						    """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
						    """reverse_editing_direction""": False,
						    """edit_warmup_steps""": [15, 18],
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": [0.9, 0.8],
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		str       			=    48
						UpperCAmelCase_:		Optional[int]       			=    7

						# no sega enabled
						UpperCAmelCase_:		int       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Union[str, Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		Tuple       			=    output.images
						UpperCAmelCase_:		Tuple       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Tuple       			=    [
						    0.7_5_1_6_3_5_5_5,
						    0.7_6_0_3_7_1_4_5,
						    0.6_1_7_8_5,
						    0.9_1_8_9_6_7_3,
						    0.8_6_2_7_7_0_1,
						    0.8_5_1_8_9_6_9_4,
						    0.8_5_1_2_8_1_3,
						    0.8_7_0_1_2_0_7_6,
						    0.8_3_1_2_8_5_7,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Dict       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[str]       			=    output.images
						UpperCAmelCase_:		Optional[int]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		Optional[Any]       			=    [
						    0.7_3_5_5_3_3_6_5,
						    0.7_5_3_7_2_7_1,
						    0.7_4_3_4_1_9_0_5,
						    0.6_6_4_8_0_3_5_6,
						    0.6_4_7_2_9_2_5,
						    0.6_3_0_3_9_4_1_6,
						    0.6_4_8_1_2_9_0_5,
						    0.6_7_4_9_7_1_7,
						    0.6_5_1_7_1_0_2,
						]

						assert image.shape == (1, 512, 512, 3)

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

			def     __snake_case					(self				)					->      Optional[int]:
						UpperCAmelCase_:		List[Any]       			=    """cuda"""
						UpperCAmelCase_:		Union[str, Any]       			=    StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""",      torch_dtype=torch.floataa				)
						UpperCAmelCase_:		Optional[int]       			=    pipe.to(SCREAMING_SNAKE_CASE_				)
						pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_				)

						UpperCAmelCase_:		Dict       			=    """a photo of a cat"""
						UpperCAmelCase_:		Dict       			=    {
						    """editing_prompt""": ["""sunglasses"""],
						    """reverse_editing_direction""": [False],
						    """edit_warmup_steps""": 10,
						    """edit_guidance_scale""": 6,
						    """edit_threshold""": 0.9_5,
						    """edit_momentum_scale""": 0.5,
						    """edit_mom_beta""": 0.6,
						}

						UpperCAmelCase_:		List[Any]       			=    3
						UpperCAmelCase_:		Dict       			=    7

						# no sega enabled
						UpperCAmelCase_:		Tuple       			=    torch.Generator(SCREAMING_SNAKE_CASE_				)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		List[Any]       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      )

						UpperCAmelCase_:		str       			=    output.images
						UpperCAmelCase_:		Optional[Any]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.3_4_8_8_7_6_9_5,
						    0.3_8_7_6_9_5_3,
						    0.3_7_5,
						    0.3_4_4_2_3_8_2_8,
						    0.3_5_8_1_5_4_3,
						    0.3_5_7_1_7_7_7_3,
						    0.3_3_8_3_7_8_9,
						    0.3_4_5_7_0_3_1_2,
						    0.3_5_9_3_7_5,
						]

						assert image.shape == (1, 512, 512, 3)

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

						# with sega enabled
						# generator = torch.manual_seed(seed)
						generator.manual_seed(SCREAMING_SNAKE_CASE_				)
						UpperCAmelCase_:		Any       			=    pipe(
						    [prompt],      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=SCREAMING_SNAKE_CASE_,      num_inference_steps=50,      output_type="""np""",      width=512,      height=512,      **SCREAMING_SNAKE_CASE_,      )

						UpperCAmelCase_:		List[Any]       			=    output.images
						UpperCAmelCase_:		List[str]       			=    image[0, -3:, -3:, -1]
						UpperCAmelCase_:		List[Any]       			=    [
						    0.4_2_2_8_5_1_5_6,
						    0.3_6_9_1_4_0_6_2,
						    0.2_9_0_7_7_1_4_8,
						    0.4_2_0_4_1_0_1_6,
						    0.4_1_9_1_8_9_4_5,
						    0.3_5_4_9_8_0_4_7,
						    0.3_6_1_8_1_6_4,
						    0.4_4_2_3_8_2_8,
						    0.4_3_1_1_5_2_3_4,
						]

						assert image.shape == (1, 512, 512, 3)

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











import numpy as np







def    lowerCAmelCase_  (lowerCAmelCase__:		Optional[Any]     ):





			"""simple docstring"""


			UpperCAmelCase_:		Optional[int]       			=    np.exp(lowerCAmelCase__     )

			# Add up the all the exponentials
			UpperCAmelCase_:		Tuple       			=    np.sum(lowerCAmelCase__     )

			# Divide every exponent by the sum of all exponents
			UpperCAmelCase_:		str       			=    exponent_vector / sum_of_exponents

			return softmax_vector


if __name__ == "__main__":
	print(softmax((0,)))






