




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: List[str]   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      @property
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						1
             __lowerCamelCase   =						3
             __lowerCamelCase   =						(32, 32)

             __lowerCamelCase   =						floats_tensor((batch_size, num_channels) + sizes    ,       rng=random.Random(0   )   ).to(UpperCamelCase_   )
             return image
      @property
      def 			lowerCAmelCase__						(				self: List[str]   ):
             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 			lowerCAmelCase__						(				self: Optional[Any]   ):
             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 			lowerCAmelCase__						(				self: Any   ):
             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=10_00    ,       )
             return CLIPTextModel(UpperCamelCase_   )
      @property
      def 			lowerCAmelCase__						(				self: Tuple   ):
             def extract(*UpperCamelCase_: Tuple    ,       **UpperCamelCase_: Union[str, Any]   ):





                    class     lowerCamelCase__:
                          def __init__(				self: Dict   ):
                                 __lowerCamelCase   =						torch.ones([0]   )




                          def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: List[Any]   ):
                                 self.pixel_values.to(UpperCamelCase_   )
                                 return self



                    return Out()

             return extract
      def 			lowerCAmelCase__						(				self: Dict   ):
             __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=UpperCamelCase_    ,       set_alpha_to_one=UpperCamelCase_    ,       )

             __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=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

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

             __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(0   )
             __lowerCamelCase   =						sd_pipe(
                 [prompt]    ,       generator=UpperCamelCase_    ,       guidance_scale=6.0    ,       num_inference_steps=2    ,       output_type="""np"""    ,       return_dict=UpperCamelCase_    ,       )[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 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.dummy_cond_unet
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             __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=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

             __lowerCamelCase   =						output.images

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

             __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(UpperCamelCase_   )
                    __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained(UpperCamelCase_   )

             # 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 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						self.dummy_cond_unet
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             __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=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       vae=UpperCamelCase_    ,       text_encoder=UpperCamelCase_    ,       tokenizer=UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       feature_extractor=self.dummy_extractor    ,       )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __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     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: Any   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: Tuple   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,       safety_checker=UpperCamelCase_   )
             __lowerCamelCase   =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __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   =						40_03_66_03_46
             __lowerCamelCase   =						7

             # without safety guidance (sld_guidance_scale = 0)
             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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    ,       )

             __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, 5_12, 5_12, 3)

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

             # without safety guidance (strong configuration)
             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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=0.025    ,       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, 5_12, 5_12, 3)

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      def 			lowerCAmelCase__						(				self: Any   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,       safety_checker=UpperCamelCase_   )
             __lowerCamelCase   =						LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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    ,       )

             __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, 5_12, 5_12, 3)

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

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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=0.025    ,       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, 5_12, 5_12, 3)

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




      def 			lowerCAmelCase__						(				self: Dict   ):
             __lowerCamelCase   =						StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

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

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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    ,       )

             __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, 5_12, 5_12, 3)

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

             __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						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=0.025    ,       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, 5_12, 5_12, 3)

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






'''simple docstring'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
