'''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       A   (		unittest.TestCase   ):







							def        __lowerCAmelCase      (  self	:  int )      ->							Union[str, Any]:


														"""simple docstring"""


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







							@property
							def        __lowerCAmelCase      (  self	:  List[str] )      ->							Optional[Any]:


														"""simple docstring"""


														_a     					=    1
														_a     					=    3
														_a     					=    (32, 32)

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







							@property
							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							str:


														"""simple docstring"""


														torch.manual_seed(0 )
														_a     					=    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        __lowerCAmelCase      (  self	:  List[Any] )      ->							List[str]:


														"""simple docstring"""


														torch.manual_seed(0 )
														_a     					=    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        __lowerCAmelCase      (  self	:  Optional[Any] )      ->							List[str]:


														"""simple docstring"""


														torch.manual_seed(0 )
														_a     					=    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=10_00							,      )
														return CLIPTextModel(UpperCamelCase__ )







							@property
							def        __lowerCAmelCase      (  self	:  Any )      ->							int:


														"""simple docstring"""


														def extract(*lowerCAmelCase_	:  str							,      **lowerCAmelCase_	:  Union[str, Any] ):

																					class       A   :







																												def __init__(  self	:  Dict )      ->							Dict:


																																			"""simple docstring"""


																																			_a     					=    torch.ones([0] )







																												def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  int )      ->							Any:


																																			"""simple docstring"""


																																			self.pixel_values.to(UpperCamelCase__ )
																																			return self



																					return Out()

														return extract







							def        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


														_a     					=    "cpu"  # ensure determinism for the device-dependent torch.Generator
														_a     					=    self.dummy_cond_unet
														_a     					=    DDIMScheduler(
														    beta_start=0.0_0_0_8_5							,      beta_end=0.0_1_2							,      beta_schedule='''scaled_linear'''							,      clip_sample=UpperCamelCase__							,      set_alpha_to_one=UpperCamelCase__							,      )

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

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

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

														_a     					=    torch.Generator(device=UpperCamelCase__ ).manual_seed(0 )
														_a     					=    sd_pipe([prompt]							,      generator=UpperCamelCase__							,      guidance_scale=6.0							,      num_inference_steps=2							,      output_type='''np''' )
														_a     					=    output.images

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

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

														assert image.shape == (1, 64, 64, 3)
														_a     					=    np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_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        __lowerCAmelCase      (  self	:  List[Any] )      ->							List[str]:


														"""simple docstring"""


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

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

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

														_a     					=    output.images

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

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

														assert image.shape == (1, 64, 64, 3)
														_a     					=    np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3] )

														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        __lowerCAmelCase      (  self	:  List[Any] )      ->							int:


														"""simple docstring"""


														_a     					=    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

														_a     					=    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__ )
																					_a     					=    StableDiffusionPipeline.from_pretrained(UpperCamelCase__ )

														# sanity check that the pipeline still works
														assert pipe.safety_checker is None
														_a     					=    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        __lowerCAmelCase      (  self	:  List[str] )      ->							List[str]:


														"""simple docstring"""


														_a     					=    self.dummy_cond_unet
														_a     					=    PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
														_a     					=    self.dummy_vae
														_a     					=    self.dummy_text_encoder
														_a     					=    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )

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

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

														_a     					=    "A painting of a squirrel eating a burger"
														_a     					=    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        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


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







							def        __lowerCAmelCase      (  self	:  Dict )      ->							Union[str, Any]:


														"""simple docstring"""


														_a     					=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							,      safety_checker=UpperCamelCase__ )
														_a     					=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
														_a     					=    sd_pipe.to(UpperCamelCase__ )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

														_a     					=    (
														    "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     					=    40_03_66_03_46
														_a     					=    7

														# without safety guidance (sld_guidance_scale = 0)
														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=0							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    [0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

														assert image.shape == (1, 5_12, 5_12, 3)

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

														# without safety guidance (strong configuration)
														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=20_00							,      sld_warmup_steps=7							,      sld_threshold=0.0_2_5							,      sld_momentum_scale=0.5							,      sld_mom_beta=0.7							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    [0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

														assert image.shape == (1, 5_12, 5_12, 3)

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







							def        __lowerCAmelCase      (  self	:  str )      ->							Tuple:


														"""simple docstring"""


														_a     					=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5'''							,      safety_checker=UpperCamelCase__ )
														_a     					=    LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config )
														_a     					=    sd_pipe.to(UpperCamelCase__ )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

														_a     					=    "padme amidala taking a bath artwork, safe for work, no nudity"
														_a     					=    27_34_97_17_55
														_a     					=    7

														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=0							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    [0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

														assert image.shape == (1, 5_12, 5_12, 3)

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

														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=20_00							,      sld_warmup_steps=7							,      sld_threshold=0.0_2_5							,      sld_momentum_scale=0.5							,      sld_mom_beta=0.7							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    [0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

														assert image.shape == (1, 5_12, 5_12, 3)

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







							def        __lowerCAmelCase      (  self	:  Optional[int] )      ->							Optional[int]:


														"""simple docstring"""


														_a     					=    StableDiffusionPipeline.from_pretrained('''runwayml/stable-diffusion-v1-5''' )
														_a     					=    sd_pipe.to(UpperCamelCase__ )
														sd_pipe.set_progress_bar_config(disable=UpperCamelCase__ )

														_a     					=    (
														    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
														    " leyendecker"
														)
														_a     					=    10_44_35_52_34
														_a     					=    12

														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=0							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    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_12, 5_12, 3)

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

														_a     					=    torch.manual_seed(UpperCamelCase__ )
														_a     					=    sd_pipe(
														    [prompt]							,      generator=UpperCamelCase__							,      guidance_scale=UpperCamelCase__							,      num_inference_steps=50							,      output_type='''np'''							,      width=5_12							,      height=5_12							,      sld_guidance_scale=20_00							,      sld_warmup_steps=7							,      sld_threshold=0.0_2_5							,      sld_momentum_scale=0.5							,      sld_mom_beta=0.7							,      )

														_a     					=    output.images
														_a     					=    image[0, -3:, -3:, -1]
														_a     					=    np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1] )
														assert image.shape == (1, 5_12, 5_12, 3)

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







'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer

class       A   (		_a   ):







							def __init__(  self	:  str							,      *lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  List[str] )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(*lowerCAmelCase_							,      **lowerCAmelCase_ )
														_a     					=    {}







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  Tuple							,      *lowerCAmelCase_	:  Any							,      **lowerCAmelCase_	:  Tuple )      ->							str:


														"""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        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      *lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  Tuple=1							,      **lowerCAmelCase_	:  int )      ->							Any:


														"""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        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Tuple=False							,      lowerCAmelCase_	:  Optional[int]=1.0 )      ->							Tuple:


														"""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	:  List[str]							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[int]=False							,      lowerCAmelCase_	:  Union[str, Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  int							,      *lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Any=False							,      lowerCAmelCase_	:  List[Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Dict:


														"""simple docstring"""


														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )

