


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 		_snake_case  (				unittest.TestCase							):




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




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	1
       a						:List[Any]										=	3
       a						:Dict										=	(32, 32)

       a						:List[str]										=	floats_tensor((batch_size, num_channels) + sizes ,  rng=random.Random(0   )   ).to(__lowercase   )
       return image




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:List[str]										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						: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 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[Any]										=	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(__lowercase   )




 @property
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       def extract(*_lowerCamelCase ,  **_lowerCamelCase   ):







             class 		_snake_case  :




              def __init__(		self   ):
                    a						:Tuple										=	torch.ones([0]   )




              def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase   ):
                    self.pixel_values.to(__lowercase   )
                    return self







             return Out()

       return extract




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:Optional[int]										=	self.dummy_cond_unet
       a						:Optional[Any]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  clip_sample=__lowercase ,  set_alpha_to_one=__lowercase ,  )

       a						:Optional[Any]										=	self.dummy_vae
       a						:Union[str, Any]										=	self.dummy_text_encoder
       a						:Union[str, Any]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

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

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

       a						:int										=	torch.Generator(device=__lowercase   ).manual_seed(0   )
       a						:Union[str, Any]										=	sd_pipe([prompt] ,  generator=__lowercase ,  guidance_scale=6.0 ,  num_inference_steps=2 ,  output_type='''np'''   )
       a						:str										=	output.images

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

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

       assert image.shape == (1, 64, 64, 3)
       a						:Optional[Any]										=	np.array([0.5756, 0.6118, 0.5005, 0.5041, 0.5471, 0.4726, 0.4976, 0.4865, 0.4864]   )

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

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

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

       a						:List[Any]										=	output.images

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

       a						:Any										=	image[0, -3:, -3:, -1]
       a						:Union[str, Any]										=	image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       a						:Optional[int]										=	np.array([0.5125, 0.5716, 0.4828, 0.5060, 0.5650, 0.4768, 0.5185, 0.4895, 0.4993]   )

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

       a						:int										=	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(__lowercase   )
             a						:Any										=	StableDiffusionPipeline.from_pretrained(__lowercase   )

       # sanity check that the pipeline still works
       assert pipe.safety_checker is None
       a						:Tuple										=	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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	self.dummy_cond_unet
       a						:Union[str, Any]										=	PNDMScheduler(skip_prk_steps=__lowercase   )
       a						:int										=	self.dummy_vae
       a						:List[Any]										=	self.dummy_text_encoder
       a						:List[Any]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       # put models in fp16
       a						:str										=	unet.half()
       a						:List[str]										=	vae.half()
       a						:Dict										=	bert.half()

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

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

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







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




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[Any]										=	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' ,  safety_checker=__lowercase   )
       a						:Optional[Any]										=	LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
       a						:Optional[int]										=	sd_pipe.to(__lowercase   )
       sd_pipe.set_progress_bar_config(disable=__lowercase   )

       a						: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 '''
       )
       a						:Tuple										=	40_0366_0346
       a						:str										=	7

       # without safety guidance (sld_guidance_scale = 0)
       a						:Union[str, Any]										=	torch.manual_seed(__lowercase   )
       a						:Dict										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=0 ,  )

       a						:List[str]										=	output.images
       a						:List[Any]										=	image[0, -3:, -3:, -1]
       a						:int										=	[0.2278, 0.2231, 0.2249, 0.2333, 0.2303, 0.1885, 0.2273, 0.2144, 0.2176]

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

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

       # without safety guidance (strong configuration)
       a						:int										=	torch.manual_seed(__lowercase   )
       a						:Union[str, Any]										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=2000 ,  sld_warmup_steps=7 ,  sld_threshold=0.025 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

       a						:int										=	output.images
       a						:Union[str, Any]										=	image[0, -3:, -3:, -1]
       a						:List[str]										=	[0.2383, 0.2276, 0.236, 0.2192, 0.2186, 0.2053, 0.1971, 0.1901, 0.1719]

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

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




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

       a						:Union[str, Any]										=	'''padme amidala taking a bath artwork, safe for work, no nudity'''
       a						:List[Any]										=	27_3497_1755
       a						:Dict										=	7

       a						:Union[str, Any]										=	torch.manual_seed(__lowercase   )
       a						:Union[str, Any]										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=0 ,  )

       a						:List[str]										=	output.images
       a						:Optional[Any]										=	image[0, -3:, -3:, -1]
       a						:Any										=	[0.3502, 0.3622, 0.3396, 0.3642, 0.3478, 0.3318, 0.35, 0.3348, 0.3297]

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

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

       a						:Optional[int]										=	torch.manual_seed(__lowercase   )
       a						:Union[str, Any]										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=2000 ,  sld_warmup_steps=7 ,  sld_threshold=0.025 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

       a						:Union[str, Any]										=	output.images
       a						:Union[str, Any]										=	image[0, -3:, -3:, -1]
       a						:Any										=	[0.5531, 0.5206, 0.4895, 0.5156, 0.5182, 0.4751, 0.4802, 0.4803, 0.4443]

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

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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[Any]										=	StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''   )
       a						:List[str]										=	sd_pipe.to(__lowercase   )
       sd_pipe.set_progress_bar_config(disable=__lowercase   )

       a						:Union[str, Any]										=	(
           '''the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c.'''
           ''' leyendecker'''
       )
       a						:int										=	10_4435_5234
       a						:List[str]										=	12

       a						:str										=	torch.manual_seed(__lowercase   )
       a						:List[str]										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=0 ,  )

       a						:List[str]										=	output.images
       a						:str										=	image[0, -3:, -3:, -1]
       a						:Dict										=	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

       a						:str										=	torch.manual_seed(__lowercase   )
       a						:List[str]										=	sd_pipe(
           [prompt] ,  generator=__lowercase ,  guidance_scale=__lowercase ,  num_inference_steps=50 ,  output_type='''np''' ,  width=512 ,  height=512 ,  sld_guidance_scale=2000 ,  sld_warmup_steps=7 ,  sld_threshold=0.025 ,  sld_momentum_scale=0.5 ,  sld_mom_beta=0.7 ,  )

       a						:Any										=	output.images
       a						:str										=	image[0, -3:, -3:, -1]
       a						:int										=	np.array([0.5818, 0.6285, 0.6835, 0.6019, 0.625, 0.6754, 0.6096, 0.6334, 0.6561]   )
       assert image.shape == (1, 512, 512, 3)

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









def     __lowerCamelCase						(			UpperCAmelCase_						:		str						):




      """simple docstring"""





      if n_term == "":
            return []
      a						:list										=	[]
      for temp in range(int(UpperCAmelCase_						)						):
            series.append(F'''1/{temp + 1}''' if series else '''1'''						)
      return series


if __name__ == "__main__":
     snake_case    :   Tuple	    =					input('''Enter the last number (nth term) of the Harmonic Series''')
     print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
     print(harmonic_series(nth_term))





