
"""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 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				1
              lowercase		:Tuple       							=				3
              lowercase		:str       							=				(3_2, 3_2)

              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_channels) + sizes  ,     rng=random.Random(0				)				).to(snake_case__				)
              return image






       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Any       							=				UNetaDConditionModel(
                  block_out_channels=(3_2, 6_4)  ,     layers_per_block=2  ,     sample_size=3_2  ,     in_channels=4  ,     out_channels=4  ,     down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')  ,     up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')  ,     cross_attention_dim=3_2  ,     )
              return model






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:List[str]       							=				AutoencoderKL(
                  block_out_channels=[3_2, 6_4]  ,     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  :    Any				):

              '''simple docstring'''






              torch.manual_seed(0				)
              lowercase		:Optional[Any]       							=				CLIPTextConfig(
                  bos_token_id=0  ,     eos_token_id=2  ,     hidden_size=3_2  ,     intermediate_size=3_7  ,     layer_norm_eps=1e-0_5  ,     num_attention_heads=4  ,     num_hidden_layers=5  ,     pad_token_id=1  ,     vocab_size=1_0_0_0  ,     )
              return CLIPTextModel(snake_case__				)






       @property
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              def extract(*snake_case__  :    Optional[int]  ,     **snake_case__  :    Any				):
                     class 					__magic_name__		:






                            def __init__(   self  :    Any				):

                                   '''simple docstring'''






                                   lowercase		:Tuple       							=				torch.ones([0]				)







                            def  __snake_case						(   self  :    List[Any]  ,     snake_case__  :    List[Any]				):

                                   '''simple docstring'''






                                   self.pixel_values.to(snake_case__				)
                                   return self







                     return Out()

              return extract






       def  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:str       							=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
              lowercase		:int       							=				self.dummy_cond_unet
              lowercase		:List[Any]       							=				DDIMScheduler(
                  beta_start=0.0_00_85  ,     beta_end=0.0_12  ,     beta_schedule='''scaled_linear'''  ,     clip_sample=snake_case__  ,     set_alpha_to_one=snake_case__  ,     )

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

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

              lowercase		:List[str]       							=				'''A painting of a squirrel eating a burger'''

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

              lowercase		:Union[str, Any]       							=				torch.Generator(device=snake_case__				).manual_seed(0				)
              lowercase		:Dict       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=6.0  ,     num_inference_steps=2  ,     output_type='''np'''  ,     return_dict=snake_case__  ,     )[0]

              lowercase		:int       							=				image[0, -3:, -3:, -1]
              lowercase		:Tuple       							=				image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 6_4, 6_4, 3)
              lowercase		:Optional[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  __snake_case						(   self  :    int				):

              '''simple docstring'''






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

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

              lowercase		:int       							=				'''A painting of a squirrel eating a burger'''
              lowercase		:Dict       							=				torch.Generator(device=snake_case__				).manual_seed(0				)
              lowercase		:Dict       							=				sd_pipe([prompt]  ,     generator=snake_case__  ,     guidance_scale=6.0  ,     num_inference_steps=2  ,     output_type='''np'''				)

              lowercase		:Optional[int]       							=				output.images

              lowercase		:Any       							=				torch.Generator(device=snake_case__				).manual_seed(0				)
              lowercase		:Union[str, Any]       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=6.0  ,     num_inference_steps=2  ,     output_type='''np'''  ,     return_dict=snake_case__  ,     )[0]

              lowercase		:Optional[int]       							=				image[0, -3:, -3:, -1]
              lowercase		:Optional[int]       							=				image_from_tuple[0, -3:, -3:, -1]

              assert image.shape == (1, 6_4, 6_4, 3)
              lowercase		:Dict       							=				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  __snake_case						(   self  :    Any				):

              '''simple docstring'''






              lowercase		:List[str]       							=				StableDiffusionPipeline.from_pretrained(
                  '''hf-internal-testing/tiny-stable-diffusion-lms-pipe'''  ,     safety_checker=snake_case__				)
              assert isinstance(snake_case__  ,     snake_case__				)
              assert isinstance(pipe.scheduler  ,     snake_case__				)
              assert pipe.safety_checker is None

              lowercase		: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(snake_case__				)
                     lowercase		:Dict       							=				StableDiffusionPipeline.from_pretrained(snake_case__				)

              # sanity check that the pipeline still works
              assert pipe.safety_checker is None
              lowercase		: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  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:List[Any]       							=				self.dummy_cond_unet
              lowercase		:Optional[int]       							=				PNDMScheduler(skip_prk_steps=snake_case__				)
              lowercase		:str       							=				self.dummy_vae
              lowercase		:Any       							=				self.dummy_text_encoder
              lowercase		:Optional[Any]       							=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

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

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

              lowercase		:Tuple       							=				'''A painting of a squirrel eating a burger'''
              lowercase		:List[str]       							=				sd_pipe([prompt]  ,     num_inference_steps=2  ,     output_type='''np'''				).images

              assert image.shape == (1, 6_4, 6_4, 3)







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






       def  __snake_case						(   self  :    Tuple				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''  ,     safety_checker=snake_case__				)
              lowercase		:int       							=				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config				)
              lowercase		:int       							=				sd_pipe.to(snake_case__				)
              sd_pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:int       							=				(
                  '''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 '''
              )
              lowercase		:Dict       							=				4_0_0_3_6_6_0_3_4_6
              lowercase		:Union[str, Any]       							=				7

              # without safety guidance (sld_guidance_scale = 0)
              lowercase		:List[Any]       							=				torch.manual_seed(snake_case__				)
              lowercase		:Optional[Any]       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=0  ,     )

              lowercase		:str       							=				output.images
              lowercase		:Dict       							=				image[0, -3:, -3:, -1]
              lowercase		:List[Any]       							=				[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, 5_1_2, 5_1_2, 3)

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

              # without safety guidance (strong configuration)
              lowercase		:Optional[Any]       							=				torch.manual_seed(snake_case__				)
              lowercase		:Optional[int]       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=2_0_0_0  ,     sld_warmup_steps=7  ,     sld_threshold=0.0_25  ,     sld_momentum_scale=0.5  ,     sld_mom_beta=0.7  ,     )

              lowercase		:int       							=				output.images
              lowercase		:List[Any]       							=				image[0, -3:, -3:, -1]
              lowercase		:Dict       							=				[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, 5_1_2, 5_1_2, 3)

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






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:str       							=				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''  ,     safety_checker=snake_case__				)
              lowercase		:Union[str, Any]       							=				LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config				)
              lowercase		:int       							=				sd_pipe.to(snake_case__				)
              sd_pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:Tuple       							=				'''padme amidala taking a bath artwork, safe for work, no nudity'''
              lowercase		:List[str]       							=				2_7_3_4_9_7_1_7_5_5
              lowercase		:Union[str, Any]       							=				7

              lowercase		:Any       							=				torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=0  ,     )

              lowercase		:Dict       							=				output.images
              lowercase		:str       							=				image[0, -3:, -3:, -1]
              lowercase		:List[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, 5_1_2, 5_1_2, 3)

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

              lowercase		:Optional[int]       							=				torch.manual_seed(snake_case__				)
              lowercase		:int       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=2_0_0_0  ,     sld_warmup_steps=7  ,     sld_threshold=0.0_25  ,     sld_momentum_scale=0.5  ,     sld_mom_beta=0.7  ,     )

              lowercase		:Dict       							=				output.images
              lowercase		:str       							=				image[0, -3:, -3:, -1]
              lowercase		:str       							=				[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, 5_1_2, 5_1_2, 3)

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







       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''				)
              lowercase		:str       							=				sd_pipe.to(snake_case__				)
              sd_pipe.set_progress_bar_config(disable=snake_case__				)

              lowercase		:Tuple       							=				(
                  '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
                  ''' leyendecker'''
              )
              lowercase		:int       							=				1_0_4_4_3_5_5_2_3_4
              lowercase		:int       							=				1_2

              lowercase		:int       							=				torch.manual_seed(snake_case__				)
              lowercase		:List[str]       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=0  ,     )

              lowercase		:str       							=				output.images
              lowercase		:Optional[int]       							=				image[0, -3:, -3:, -1]
              lowercase		:Tuple       							=				np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]				)

              assert image.shape == (1, 5_1_2, 5_1_2, 3)

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

              lowercase		:Union[str, Any]       							=				torch.manual_seed(snake_case__				)
              lowercase		:Tuple       							=				sd_pipe(
                  [prompt]  ,     generator=snake_case__  ,     guidance_scale=snake_case__  ,     num_inference_steps=5_0  ,     output_type='''np'''  ,     width=5_1_2  ,     height=5_1_2  ,     sld_guidance_scale=2_0_0_0  ,     sld_warmup_steps=7  ,     sld_threshold=0.0_25  ,     sld_momentum_scale=0.5  ,     sld_mom_beta=0.7  ,     )

              lowercase		:List[str]       							=				output.images
              lowercase		:Optional[int]       							=				image[0, -3:, -3:, -1]
              lowercase		: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, 5_1_2, 5_1_2, 3)

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





"""simple docstring"""



import math






def       lowerCamelCase					(a_						:int)     ->							bool:
       assert isinstance(a_				,				a_) 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 not number % 2:
              # Negatives, 0, 1 and all even numbers are not primes
              return False

       lowercase		:Any       							=				range(3				,				int(math.sqrt(a_) + 1)				,				2)
       return not any(not number % i for i in odd_numbers)






def       lowerCamelCase					(a_						:Optional[int]				,				a_						:Optional[int]=1				,				**a_						:List[str])     ->							Any:
       lowercase		:str       							=				factor * value
       lowercase		:int       							=				value

       while not is_prime(a_):
              value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

       if value == first_value_val:
              return next_prime(value + 1				,				**a_)
       return value

