





'''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 		lowerCamelCase  (      unittest.TestCase       ):



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



	@property
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   1
			lowerCAmelCase     :    Optional[Any]						=   3
			lowerCAmelCase     :    List[str]						=   (32, 32)

			lowerCAmelCase     :    Optional[int]						=   floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0	)	).to(a_	)
			return image



	@property
	def   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    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   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self	):
			torch.manual_seed(0	)
			lowerCAmelCase     :    Union[str, Any]						=   CLIPTextConfig(
			    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1e-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1_000		,      )
			return CLIPTextModel(a_	)



	@property
	def   _lowerCamelCase    (   self	):
			def extract(*a_		,      **a_	):
					class 		lowerCamelCase  :



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





						def   _lowerCamelCase    (   self		,      a_	):
								self.pixel_values.to(a_	)
								return self






					return Out()

			return extract



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    str						=   self.dummy_cond_unet
			lowerCAmelCase     :    Tuple						=   DDIMScheduler(
			    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="scaled_linear"		,      clip_sample=a_		,      set_alpha_to_one=a_		,      )

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

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

			lowerCAmelCase     :    Dict						=   "A painting of a squirrel eating a burger"

			lowerCAmelCase     :    Tuple						=   torch.Generator(device=a_	).manual_seed(0	)
			lowerCAmelCase     :    Any						=   sd_pipe([prompt]		,      generator=a_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type="np"	)
			lowerCAmelCase     :    str						=   output.images

			lowerCAmelCase     :    Optional[int]						=   torch.Generator(device=a_	).manual_seed(0	)
			lowerCAmelCase     :    Optional[int]						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type="np"		,      return_dict=a_		,      )[0]

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

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase     :    List[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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[Any]						=   "cpu"  # ensure determinism for the device-dependent torch.Generator
			lowerCAmelCase     :    List[Any]						=   self.dummy_cond_unet
			lowerCAmelCase     :    Optional[Any]						=   PNDMScheduler(skip_prk_steps=a_	)
			lowerCAmelCase     :    int						=   self.dummy_vae
			lowerCAmelCase     :    List[Any]						=   self.dummy_text_encoder
			lowerCAmelCase     :    List[Any]						=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)

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

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

			lowerCAmelCase     :    int						=   output.images

			lowerCAmelCase     :    Optional[Any]						=   torch.Generator(device=a_	).manual_seed(0	)
			lowerCAmelCase     :    Optional[int]						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=6.0		,      num_inference_steps=2		,      output_type="np"		,      return_dict=a_		,      )[0]

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

			assert image.shape == (1, 64, 64, 3)
			lowerCAmelCase     :    Optional[Any]						=   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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   StableDiffusionPipeline.from_pretrained(
			    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"		,      safety_checker=a_	)
			assert isinstance(a_		,      a_	)
			assert isinstance(pipe.scheduler		,      a_	)
			assert pipe.safety_checker is None

			lowerCAmelCase     :    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(a_	)
					lowerCAmelCase     :    List[Any]						=   StableDiffusionPipeline.from_pretrained(a_	)

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

			# put models in fp16
			lowerCAmelCase     :    Any						=   unet.half()
			lowerCAmelCase     :    Union[str, Any]						=   vae.half()
			lowerCAmelCase     :    Union[str, Any]						=   bert.half()

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

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

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






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



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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,      safety_checker=a_	)
			lowerCAmelCase     :    Dict						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
			lowerCAmelCase     :    Optional[int]						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    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 "
			)
			lowerCAmelCase     :    List[Any]						=   4_003_660_346
			lowerCAmelCase     :    List[str]						=   7

			# without safety guidance (sld_guidance_scale = 0)
			lowerCAmelCase     :    int						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    List[str]						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=a_		,      num_inference_steps=50		,      output_type="np"		,      width=512		,      height=512		,      sld_guidance_scale=0		,      )

			lowerCAmelCase     :    Tuple						=   output.images
			lowerCAmelCase     :    List[str]						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    List[Any]						=   [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     :    List[Any]						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    Dict						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=a_		,      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     :    Dict						=   output.images
			lowerCAmelCase     :    int						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    List[Any]						=   [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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		,      safety_checker=a_	)
			lowerCAmelCase     :    int						=   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config	)
			lowerCAmelCase     :    Tuple						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

			lowerCAmelCase     :    Optional[Any]						=   "padme amidala taking a bath artwork, safe for work, no nudity"
			lowerCAmelCase     :    Optional[int]						=   2_734_971_755
			lowerCAmelCase     :    Dict						=   7

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

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

			lowerCAmelCase     :    str						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    List[str]						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=a_		,      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     :    Optional[Any]						=   output.images
			lowerCAmelCase     :    str						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    Optional[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   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"	)
			lowerCAmelCase     :    Optional[int]						=   sd_pipe.to(a_	)
			sd_pipe.set_progress_bar_config(disable=a_	)

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

			lowerCAmelCase     :    Any						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    str						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=a_		,      num_inference_steps=50		,      output_type="np"		,      width=512		,      height=512		,      sld_guidance_scale=0		,      )

			lowerCAmelCase     :    List[str]						=   output.images
			lowerCAmelCase     :    List[Any]						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    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

			lowerCAmelCase     :    List[Any]						=   torch.manual_seed(a_	)
			lowerCAmelCase     :    Dict						=   sd_pipe(
			    [prompt]		,      generator=a_		,      guidance_scale=a_		,      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     :    str						=   output.images
			lowerCAmelCase     :    Any						=   image[0, -3:, -3:, -1]
			lowerCAmelCase     :    Union[str, Any]						=   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 warnings

from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor


lowerCAmelCase       		=      logging.get_logger(__name__)
class 		lowerCamelCase  (      _A       ):



	def __init__(   self		,      *a_		,      **a_	):
			warnings.warn(
			    "The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
			    " Please use ChineseCLIPImageProcessor instead."		,      a_		,      )
			super().__init__(*a_		,      **a_	)



