


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  ):



  """simple docstring"""



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

  @property
  def    _UpperCamelCase( self			:  List[Any]       ):
        a__	:  Any     =   1
        a__	:  Optional[int]     =   3
        a__	:  Optional[int]     =   (32, 32)

        a__	:  Union[str, Any]     =   floats_tensor((batch_size, num_channels) + sizes				,				rng=random.Random(0       )       ).to(lowerCamelCase__       )
        return image

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        torch.manual_seed(0       )
        a__	:  Dict     =   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    _UpperCamelCase( self			:  Optional[Any]       ):
        torch.manual_seed(0       )
        a__	:  Union[str, Any]     =   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    _UpperCamelCase( self			:  List[str]       ):
        torch.manual_seed(0       )
        a__	:  Dict     =   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(lowerCamelCase__       )

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        def extract(*lowerCamelCase__			:  List[Any]				,				**lowerCamelCase__			:  int       ):



              class A__     :



                """simple docstring"""



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

                def    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Tuple       ):
                      self.pixel_values.to(lowerCamelCase__       )
                      return self




              return Out()

        return extract

  def    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Any     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__	:  Dict     =   self.dummy_cond_unet
        a__	:  Optional[Any]     =   DDIMScheduler(
            beta_start=0.0_0085				,				beta_end=0.012				,				beta_schedule="scaled_linear"				,				clip_sample=lowerCamelCase__				,				set_alpha_to_one=lowerCamelCase__				,				)

        a__	:  Tuple     =   self.dummy_vae
        a__	:  Optional[int]     =   self.dummy_text_encoder
        a__	:  Any     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

        a__	:  Tuple     =   "A painting of a squirrel eating a burger"

        a__	:  Union[str, Any]     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  List[Any]     =   sd_pipe([prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"       )
        a__	:  List[Any]     =   output.images

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

        a__	:  Tuple     =   image[0, -3:, -3:, -1]
        a__	:  Dict     =   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    _UpperCamelCase( self			:  Union[str, Any]       ):
        a__	:  str     =   "cpu"  # ensure determinism for the device-dependent torch.Generator
        a__	:  Optional[Any]     =   self.dummy_cond_unet
        a__	:  List[Any]     =   PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
        a__	:  Any     =   self.dummy_vae
        a__	:  List[Any]     =   self.dummy_text_encoder
        a__	:  List[Any]     =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"       )

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

        a__	:  Dict     =   "A painting of a squirrel eating a burger"
        a__	:  int     =   torch.Generator(device=lowerCamelCase__       ).manual_seed(0       )
        a__	:  Optional[int]     =   sd_pipe([prompt]				,				generator=lowerCamelCase__				,				guidance_scale=6.0				,				num_inference_steps=2				,				output_type="np"       )

        a__	:  int     =   output.images

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

        a__	:  str     =   image[0, -3:, -3:, -1]
        a__	:  Optional[Any]     =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        a__	:  Tuple     =   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    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Union[str, Any]     =   StableDiffusionPipeline.from_pretrained(
            "hf-internal-testing/tiny-stable-diffusion-lms-pipe"				,				safety_checker=lowerCamelCase__       )
        assert isinstance(lowerCamelCase__				,				lowerCamelCase__       )
        assert isinstance(pipe.scheduler				,				lowerCamelCase__       )
        assert pipe.safety_checker is None

        a__	:  Optional[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(lowerCamelCase__       )
              a__	:  Union[str, Any]     =   StableDiffusionPipeline.from_pretrained(lowerCamelCase__       )

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

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

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

        a__	:  List[str]     =   "A painting of a squirrel eating a burger"
        a__	:  Optional[Any]     =   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  ):



  """simple docstring"""



  def    _UpperCamelCase( self			:  Optional[Any]       ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def    _UpperCamelCase( self			:  Any       ):
        a__	:  Tuple     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,				safety_checker=lowerCamelCase__       )
        a__	:  Optional[Any]     =   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
        a__	:  Optional[Any]     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Union[str, Any]     =   (
            "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__	:  Optional[Any]     =   4_003_660_346
        a__	:  List[str]     =   7

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

        a__	:  str     =   output.images
        a__	:  List[Any]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   [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__	:  List[Any]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Optional[Any]     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				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				,				)

        a__	:  Dict     =   output.images
        a__	:  Union[str, Any]     =   image[0, -3:, -3:, -1]
        a__	:  Dict     =   [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    _UpperCamelCase( self			:  Tuple       ):
        a__	:  Tuple     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"				,				safety_checker=lowerCamelCase__       )
        a__	:  Optional[int]     =   LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config       )
        a__	:  Optional[int]     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

        a__	:  Optional[int]     =   "padme amidala taking a bath artwork, safe for work, no nudity"
        a__	:  Tuple     =   2_734_971_755
        a__	:  Union[str, Any]     =   7

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

        a__	:  Tuple     =   output.images
        a__	:  List[str]     =   image[0, -3:, -3:, -1]
        a__	:  Optional[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[Any]     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  List[str]     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				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				,				)

        a__	:  List[Any]     =   output.images
        a__	:  Optional[Any]     =   image[0, -3:, -3:, -1]
        a__	:  str     =   [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    _UpperCamelCase( self			:  Optional[Any]       ):
        a__	:  Any     =   StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"       )
        a__	:  Dict     =   sd_pipe.to(lowerCamelCase__       )
        sd_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

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

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

        a__	:  int     =   output.images
        a__	:  Optional[int]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   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__	:  Dict     =   torch.manual_seed(lowerCamelCase__       )
        a__	:  Tuple     =   sd_pipe(
            [prompt]				,				generator=lowerCamelCase__				,				guidance_scale=lowerCamelCase__				,				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				,				)

        a__	:  Dict     =   output.images
        a__	:  Union[str, Any]     =   image[0, -3:, -3:, -1]
        a__	:  Tuple     =   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 random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class 		lowerCAmelCase__  ( a					,   a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = IFInpaintingPipeline
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 lowerCAmelCase__       = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 lowerCAmelCase__       = PipelineTesterMixin.required_optional_params - {"latents"}
 def  UpperCAmelCase__						(						self   :			Optional[int]     )    ->					Dict:


      """simple docstring"""




      return self._get_dummy_components()
 def  UpperCAmelCase__						(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict=0     )    ->					Any:


      """simple docstring"""




      if str(__SCREAMING_SNAKE_CASE     ).startswith("""mps"""     ):
           __SCREAMING_SNAKE_CASE      = torch.manual_seed(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = torch.Generator(device=__SCREAMING_SNAKE_CASE     ).manual_seed(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__SCREAMING_SNAKE_CASE     )     ).to(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """mask_image""": mask_image,
          """generator""": generator,
          """num_inference_steps""": 2,
          """output_type""": """numpy""",
      }

      return inputs
 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available()   ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""   ,				)
 def  UpperCAmelCase__						(						self   :			str     )    ->					Optional[int]:


      """simple docstring"""




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )
 def  UpperCAmelCase__						(						self   :			str     )    ->					List[str]:


      """simple docstring"""




      self._test_save_load_optional_components()
 @unittest.skipIf(torch_device != """cuda"""   ,				reason="""float16 requires CUDA"""     )
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					int:


      """simple docstring"""




      super().test_save_load_floataa(expected_max_diff=1E-1     )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					List[Any]:


      """simple docstring"""




      self._test_save_load_local()


 def  UpperCAmelCase__						(						self   :			Any     )    ->					List[Any]:


      """simple docstring"""




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2   ,				)






