
'''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     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					1
												_lowerCAmelCase    		=					3
												_lowerCAmelCase    		=					(32, 32)

												_lowerCAmelCase    		=					floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0 ) ).to(_lowercase )
												return image




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												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       ,  )
												return model




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												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       ,  )
												return model




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												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=1_000       ,  )
												return CLIPTextModel(_lowercase )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												def extract(*_lowercase       ,  **_lowercase ):




																		class     UpperCAmelCase_	:






																								'''simple docstring'''






																								def __init__(   self ):

																														"""simple docstring"""



																														_lowerCAmelCase    		=					torch.ones([0] )




																								def    _lowercase      (   self       ,  _lowercase ):

																														"""simple docstring"""



																														self.pixel_values.to(_lowercase )
																														return self






																		return Out()

												return extract




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					DDIMScheduler(
												    beta_start=0.0_0085       ,  beta_end=0.012       ,  beta_schedule="""scaled_linear"""       ,  clip_sample=_lowercase       ,  set_alpha_to_one=_lowercase       ,  )

												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

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

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe([prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np""" )
												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					"""cpu"""  # ensure determinism for the device-dependent torch.Generator
												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					PNDMScheduler(skip_prk_steps=_lowercase )
												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

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

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""
												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe([prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np""" )

												_lowerCAmelCase    		=					output.images

												_lowerCAmelCase    		=					torch.Generator(device=_lowercase ).manual_seed(0 )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type="""np"""       ,  return_dict=_lowercase       ,  )[0]

												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					image_from_tuple[0, -3:, -3:, -1]

												assert image.shape == (1, 64, 64, 3)
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					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

												_lowerCAmelCase    		=					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 )
																		_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained(_lowercase )

												# sanity check that the pipeline still works
												assert pipe.safety_checker is None
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.dummy_cond_unet
												_lowerCAmelCase    		=					PNDMScheduler(skip_prk_steps=_lowercase )
												_lowerCAmelCase    		=					self.dummy_vae
												_lowerCAmelCase    		=					self.dummy_text_encoder
												_lowerCAmelCase    		=					CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

												# put models in fp16
												_lowerCAmelCase    		=					unet.half()
												_lowerCAmelCase    		=					vae.half()
												_lowerCAmelCase    		=					bert.half()

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

												_lowerCAmelCase    		=					"""A painting of a squirrel eating a burger"""
												_lowerCAmelCase    		=					sd_pipe([prompt]       ,  num_inference_steps=2       ,  output_type="""np""" ).images

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






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






						'''simple docstring'''






						def    _lowercase      (   self ):

												"""simple docstring"""



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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_lowercase )
												_lowerCAmelCase    		=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					(
												    """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 """
												)
												_lowerCAmelCase    		=					4_003_660_346
												_lowerCAmelCase    		=					7

												# without safety guidance (sld_guidance_scale = 0)
												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[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)
												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""       ,  safety_checker=_lowercase )
												_lowerCAmelCase    		=					LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					"""padme amidala taking a bath artwork, safe for work, no nudity"""
												_lowerCAmelCase    		=					2_734_971_755
												_lowerCAmelCase    		=					7

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[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

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					[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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5""" )
												_lowerCAmelCase    		=					sd_pipe.to(_lowercase )
												sd_pipe.set_progress_bar_config(disable=_lowercase )

												_lowerCAmelCase    		=					(
												    """the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."""
												    """ leyendecker"""
												)
												_lowerCAmelCase    		=					1_044_355_234
												_lowerCAmelCase    		=					12

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=0       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					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

												_lowerCAmelCase    		=					torch.manual_seed(_lowercase )
												_lowerCAmelCase    		=					sd_pipe(
												    [prompt]       ,  generator=_lowercase       ,  guidance_scale=_lowercase       ,  num_inference_steps=50       ,  output_type="""np"""       ,  width=512       ,  height=512       ,  sld_guidance_scale=2_000       ,  sld_warmup_steps=7       ,  sld_threshold=0.025       ,  sld_momentum_scale=0.5       ,  sld_mom_beta=0.7       ,  )

												_lowerCAmelCase    		=					output.images
												_lowerCAmelCase    		=					image[0, -3:, -3:, -1]
												_lowerCAmelCase    		=					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








'''simple docstring'''



import copy
import random

from transformers import CLIPTokenizer
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, *lowerCamelCase			, **lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         super().__init__(*lowerCamelCase			, **lowerCamelCase   )
         __a					  =     {}




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )				->Dict:



         '''simple docstring'''
         __a					  =     super().add_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
         if num_added_tokens == 0:
            raise ValueError(
                F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                ' `placeholder_token` that is not already in the tokenizer.'   )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=1			, **lowerCamelCase   )				->List[str]:



         '''simple docstring'''
         __a					  =     []
         if num_vec_per_token == 1:
            self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
            output.append(lowerCamelCase   )
         else:
            __a					  =     []
            for i in range(lowerCamelCase   ):
               __a					  =     placeholder_token + F"""_{i}"""
               self.try_adding_tokens(lowerCamelCase			, *lowerCamelCase			, **lowerCamelCase   )
               output.append(lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
         for token in self.token_map:
            if token in placeholder_token:
               raise ValueError(
                   F"""The tokenizer already has placeholder token {token} that can get confused with"""
                   F""" {placeholder_token}keep placeholder tokens independent"""   )
         __a					  =     output




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0   )				->int:



         '''simple docstring'''
         if isinstance(lowerCamelCase			, lowerCamelCase   ):
            __a					  =     []
            for i in range(len(lowerCamelCase   )   ):
               output.append(self.replace_placeholder_tokens_in_text(text[i]			, vector_shuffle=lowerCamelCase   )   )
            return output
         for placeholder_token in self.token_map:
            if placeholder_token in text:
               __a					  =     self.token_map[placeholder_token]
               __a					  =     tokens[: 1 + int(len(lowerCamelCase   ) * prop_tokens_to_load   )]
               if vector_shuffle:
                  __a					  =     copy.copy(lowerCamelCase   )
                  random.shuffle(lowerCamelCase   )
               __a					  =     text.replace(lowerCamelCase			, ' '.join(lowerCamelCase   )   )
         return text




      def __call__(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->Optional[int]:



         '''simple docstring'''
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )






      def 			__UpperCamelCase				(	self			, lowerCamelCase			, *lowerCamelCase			, lowerCamelCase=False			, lowerCamelCase=1.0			, **lowerCamelCase   )				->List[Any]:



         '''simple docstring'''
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 lowerCamelCase			, vector_shuffle=lowerCamelCase			, prop_tokens_to_load=lowerCamelCase   )			, *lowerCamelCase			, **lowerCamelCase			, )