






'''simple docstring'''




from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__          =						logging.get_logger(__name__)


lowerCAmelCase__          =						{
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
lowerCAmelCase__          =						111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCAmelCase__          =						0

lowerCAmelCase__          =						0xE_0_0_0
lowerCAmelCase__          =						0xE_0_0_1
lowerCAmelCase__          =						0xE_0_0_2
lowerCAmelCase__          =						0xE_0_0_3
lowerCAmelCase__          =						0xE_0_0_4

# Maps special codepoints to human-readable names.
lowerCAmelCase__          =						{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: '[CLS]',
    SEP: '[SEP]',
    BOS: '[BOS]',
    MASK: '[MASK]',
    PAD: '[PAD]',
    RESERVED: '[RESERVED]',
}

# Maps special codepoint human-readable names to their codepoint values.
lowerCAmelCase__          =						{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}







class       __lowercase					(__lowerCamelCase    ):
 _lowerCamelCase              =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

 def __init__(      self   :						Dict  ,					UpperCAmelCase_   :						Union[str, Any]=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						int=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						int=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						List[Any]=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						Union[str, Any]=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						List[Any]=chr(UpperCAmelCase_)  ,					UpperCAmelCase_   :						int=False  ,					UpperCAmelCase_   :						List[str]=2_048  ,					**UpperCAmelCase_   :						Optional[int]  ,					):
   UpperCamelCase__				:					Optional[int]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else bos_token
   UpperCamelCase__				:					List[str]					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else eos_token
   UpperCamelCase__				:					Tuple					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else sep_token
   UpperCamelCase__				:					Dict					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else cls_token
   UpperCamelCase__				:					int					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else pad_token

   # Mask token behave like a normal word, i.e. include the space before it
   UpperCamelCase__				:					str					=					AddedToken(UpperCAmelCase_  ,					lstrip=UpperCAmelCase_  ,					rstrip=UpperCAmelCase_) if isinstance(UpperCAmelCase_  ,					UpperCAmelCase_) else mask_token

   super().__init__(
       bos_token=UpperCAmelCase_  ,					eos_token=UpperCAmelCase_  ,					sep_token=UpperCAmelCase_  ,					cls_token=UpperCAmelCase_  ,					pad_token=UpperCAmelCase_  ,					mask_token=UpperCAmelCase_  ,					add_prefix_space=UpperCAmelCase_  ,					model_max_length=UpperCAmelCase_  ,					**UpperCAmelCase_  ,					)

   # Creates a mapping for looking up the IDs of special symbols.
   UpperCamelCase__				:					Dict[str, int]					=					{}
   for codepoint, name in SPECIAL_CODEPOINTS.items():
     UpperCamelCase__				:					List[Any]					=					codepoint

   # Creates a mapping for looking up the string forms of special symbol IDs.
   UpperCamelCase__				:					Dict[int, str]					=					{
       codepoint: name for name, codepoint in self._special_codepoints.items()
   }

   UpperCamelCase__				:					Optional[int]					=					UNICODE_VOCAB_SIZE
   UpperCamelCase__				:					Union[str, Any]					=					len(self._special_codepoints)

 @property
 def      __UpperCamelCase							(      self   :						Dict):
   return self._unicode_vocab_size

 def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
   return list(UpperCAmelCase_)

 def      __UpperCamelCase							(      self   :						str  ,					UpperCAmelCase_   :						str):
   try:
     return ord(UpperCAmelCase_)
   except TypeError:
     raise ValueError(F'invalid token: \'{token}\'')

 def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						int):
   try:
     if index in SPECIAL_CODEPOINTS:
       return SPECIAL_CODEPOINTS[index]
     return chr(UpperCAmelCase_)
   except TypeError:
     raise ValueError(F'invalid id: {index}')

 def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[Any]):
   return "".join(UpperCAmelCase_)

 def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
   UpperCamelCase__				:					List[str]					=					[self.sep_token_id]
   UpperCamelCase__				:					Union[str, Any]					=					[self.cls_token_id]

   UpperCamelCase__				:					Dict					=					cls + token_ids_a + sep
   if token_ids_a is not None:
     result += token_ids_a + sep
   return result

 def      __UpperCamelCase							(      self   :						Any  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None  ,					UpperCAmelCase_   :						bool = False):
   if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCAmelCase_  ,					token_ids_a=UpperCAmelCase_  ,					already_has_special_tokens=UpperCAmelCase_)

   UpperCamelCase__				:					Optional[int]					=					[1] + ([0] * len(UpperCAmelCase_)) + [1]
   if token_ids_a is not None:
     result += ([0] * len(UpperCAmelCase_)) + [1]
   return result

 def      __UpperCamelCase							(      self   :						List[Any]  ,					UpperCAmelCase_   :						List[int]  ,					UpperCAmelCase_   :						Optional[List[int]] = None):
   UpperCamelCase__				:					Tuple					=					[self.sep_token_id]
   UpperCamelCase__				:					str					=					[self.cls_token_id]

   UpperCamelCase__				:					Tuple					=					len(cls + token_ids_a + sep) * [0]
   if token_ids_a is not None:
     result += len(token_ids_a + sep) * [1]
   return result





 def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						str  ,					UpperCAmelCase_   :						Optional[str] = None):
   return ()












'''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.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu







class       __lowercase					(unittest.TestCase    ):

	def      __UpperCamelCase							(      self   :						List[str]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	@property
	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Union[str, Any]					=					1
			UpperCamelCase__				:					Union[str, Any]					=					3
			UpperCamelCase__				:					Dict					=					(32, 32)

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

	@property
	def      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					Any					=					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      __UpperCamelCase							(      self   :						Any):
			torch.manual_seed(0)
			UpperCamelCase__				:					List[str]					=					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      __UpperCamelCase							(      self   :						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=1_000  ,					)
			return CLIPTextModel(UpperCAmelCase_)

	@property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			def extract(*UpperCAmelCase_   :						List[Any]  ,					**UpperCAmelCase_   :						Dict):







					class       __lowercase					:

						def __init__(      self   :						List[Any]):
								UpperCamelCase__				:					Optional[Any]					=					torch.ones([0])





						def      __UpperCamelCase							(      self   :						Dict  ,					UpperCAmelCase_   :						int):
								self.pixel_values.to(UpperCAmelCase_)
								return self


					return Out()

			return extract

	def      __UpperCamelCase							(      self   :						str):
			UpperCamelCase__				:					Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
			UpperCamelCase__				:					Any					=					self.dummy_cond_unet
			UpperCamelCase__				:					Any					=					DDIMScheduler(
			    beta_start=0.0_00_85  ,					beta_end=0.0_12  ,					beta_schedule='scaled_linear'  ,					clip_sample=UpperCAmelCase_  ,					set_alpha_to_one=UpperCAmelCase_  ,					)

			UpperCamelCase__				:					List[str]					=					self.dummy_vae
			UpperCamelCase__				:					str					=					self.dummy_text_encoder
			UpperCamelCase__				:					Tuple					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

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

			UpperCamelCase__				:					Optional[Any]					=					'A painting of a squirrel eating a burger'

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

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

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

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					np.array([0.57_56, 0.61_18, 0.50_05, 0.50_41, 0.54_71, 0.47_26, 0.49_76, 0.48_65, 0.48_64])

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

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

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

			UpperCamelCase__				:					List[str]					=					output.images

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

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

			assert image.shape == (1, 64, 64, 3)
			UpperCamelCase__				:					List[Any]					=					np.array([0.51_25, 0.57_16, 0.48_28, 0.50_60, 0.56_50, 0.47_68, 0.51_85, 0.48_95, 0.49_93])

			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      __UpperCamelCase							(      self   :						Dict):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained(
			    'hf-internal-testing/tiny-stable-diffusion-lms-pipe'  ,					safety_checker=UpperCAmelCase_)
			assert isinstance(UpperCAmelCase_  ,					UpperCAmelCase_)
			assert isinstance(pipe.scheduler  ,					UpperCAmelCase_)
			assert pipe.safety_checker is None

			UpperCamelCase__				:					List[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(UpperCAmelCase_)
					UpperCamelCase__				:					List[str]					=					StableDiffusionPipeline.from_pretrained(UpperCAmelCase_)

			# sanity check that the pipeline still works
			assert pipe.safety_checker is None
			UpperCamelCase__				:					Optional[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      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Dict					=					self.dummy_cond_unet
			UpperCamelCase__				:					str					=					PNDMScheduler(skip_prk_steps=UpperCAmelCase_)
			UpperCamelCase__				:					Any					=					self.dummy_vae
			UpperCamelCase__				:					Optional[Any]					=					self.dummy_text_encoder
			UpperCamelCase__				:					str					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

			# put models in fp16
			UpperCamelCase__				:					Any					=					unet.half()
			UpperCamelCase__				:					Tuple					=					vae.half()
			UpperCamelCase__				:					Optional[int]					=					bert.half()

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

			UpperCamelCase__				:					Any					=					'A painting of a squirrel eating a burger'
			UpperCamelCase__				:					int					=					sd_pipe([prompt]  ,					num_inference_steps=2  ,					output_type='np').images

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







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

	def      __UpperCamelCase							(      self   :						Optional[int]):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

	def      __UpperCamelCase							(      self   :						List[Any]):
			UpperCamelCase__				:					Optional[int]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					List[Any]					=					(
			    'portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle'
			    ' coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with'
			    ' anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and'
			    ' children from bahnhof zoo, detailed '
			)
			UpperCamelCase__				:					Any					=					4_003_660_346
			UpperCamelCase__				:					Any					=					7

			# without safety guidance (sld_guidance_scale = 0)
			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Optional[int]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					str					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[0.22_78, 0.22_31, 0.22_49, 0.23_33, 0.23_03, 0.18_85, 0.22_73, 0.21_44, 0.21_76]

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

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

			# without safety guidance (strong configuration)
			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Dict					=					output.images
			UpperCamelCase__				:					str					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Tuple					=					[0.23_83, 0.22_76, 0.2_36, 0.21_92, 0.21_86, 0.20_53, 0.19_71, 0.19_01, 0.17_19]

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

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

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5'  ,					safety_checker=UpperCAmelCase_)
			UpperCamelCase__				:					str					=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
			UpperCamelCase__				:					Dict					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					str					=					'padme amidala taking a bath artwork, safe for work, no nudity'
			UpperCamelCase__				:					Tuple					=					2_734_971_755
			UpperCamelCase__				:					Tuple					=					7

			UpperCamelCase__				:					Tuple					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					int					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					int					=					output.images
			UpperCamelCase__				:					Union[str, Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Any					=					[0.35_02, 0.36_22, 0.33_96, 0.36_42, 0.34_78, 0.33_18, 0.35, 0.33_48, 0.32_97]

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

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

			UpperCamelCase__				:					List[str]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					Union[str, Any]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Tuple					=					output.images
			UpperCamelCase__				:					List[str]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					Union[str, Any]					=					[0.55_31, 0.52_06, 0.48_95, 0.51_56, 0.51_82, 0.47_51, 0.48_02, 0.48_03, 0.44_43]

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

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





	def      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Optional[Any]					=					StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5')
			UpperCamelCase__				:					Optional[Any]					=					sd_pipe.to(UpperCAmelCase_)
			sd_pipe.set_progress_bar_config(disable=UpperCAmelCase_)

			UpperCamelCase__				:					int					=					(
			    'the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'
			    ' leyendecker'
			)
			UpperCamelCase__				:					Any					=					1_044_355_234
			UpperCamelCase__				:					Optional[int]					=					12

			UpperCamelCase__				:					Optional[int]					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					str					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=0  ,					)

			UpperCamelCase__				:					List[str]					=					output.images
			UpperCamelCase__				:					Any					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])

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

			assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-7

			UpperCamelCase__				:					int					=					torch.manual_seed(UpperCAmelCase_)
			UpperCamelCase__				:					List[str]					=					sd_pipe(
			    [prompt]  ,					generator=UpperCAmelCase_  ,					guidance_scale=UpperCAmelCase_  ,					num_inference_steps=50  ,					output_type='np'  ,					width=512  ,					height=512  ,					sld_guidance_scale=2_000  ,					sld_warmup_steps=7  ,					sld_threshold=0.0_25  ,					sld_momentum_scale=0.5  ,					sld_mom_beta=0.7  ,					)

			UpperCamelCase__				:					Optional[Any]					=					output.images
			UpperCamelCase__				:					List[Any]					=					image[0, -3:, -3:, -1]
			UpperCamelCase__				:					str					=					np.array([0.58_18, 0.62_85, 0.68_35, 0.60_19, 0.6_25, 0.67_54, 0.60_96, 0.63_34, 0.65_61])
			assert image.shape == (1, 512, 512, 3)

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




