




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.semantic_stable_diffusion import SemanticStableDiffusionPipeline as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







class 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Dict:






         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      List[str]:






         """simple docstring"""
         a__  			=							1
         a__  			=							3
         a__  			=							(32, 32)

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

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Union[str, Any]:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Tuple:






         """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 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      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(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[Any]:






         """simple docstring"""
         def extract(*A			:							Dict    ,					**A			:							Dict     ):







             class 		__UpperCAmelCase:





                  """simple docstring"""



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






                      """simple docstring"""
                      a__  			=							torch.ones([0]     )






                  def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict    ,					A			:							Any     )      ->      Dict:






                      """simple docstring"""
                      self.pixel_values.to(A     )
                      return self






             return Out()

         return extract

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Any:






         """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=A    ,					set_alpha_to_one=A    ,					)

         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=A    ,					scheduler=A    ,					vae=A    ,					text_encoder=A    ,					tokenizer=A    ,					safety_checker=A    ,					feature_extractor=self.dummy_extractor    ,					)
         a__  			=							sd_pipe.to(A     )
         sd_pipe.set_progress_bar_config(disable=A     )

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

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

         a__  			=							torch.Generator(device=A     ).manual_seed(0     )
         a__  			=							sd_pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=6.0    ,					num_inference_steps=2    ,					output_type="""np"""    ,					return_dict=A    ,					)[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_3, 0.6_1_1_4, 0.5_0_0_1, 0.5_0_3_4, 0.5_4_7_0, 0.4_7_2_9, 0.4_9_7_1, 0.4_8_6_7, 0.4_8_6_7]     )

         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 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[Any]:






         """simple docstring"""
         a__  			=							"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         a__  			=							self.dummy_cond_unet
         a__  			=							PNDMScheduler(skip_prk_steps=A     )
         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=A    ,					scheduler=A    ,					vae=A    ,					text_encoder=A    ,					tokenizer=A    ,					safety_checker=A    ,					feature_extractor=self.dummy_extractor    ,					)
         a__  			=							sd_pipe.to(A     )
         sd_pipe.set_progress_bar_config(disable=A     )

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

         a__  			=							output.images

         a__  			=							torch.Generator(device=A     ).manual_seed(0     )
         a__  			=							sd_pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=6.0    ,					num_inference_steps=2    ,					output_type="""np"""    ,					return_dict=A    ,					)[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_2, 0.5_7_1_2, 0.4_8_2_5, 0.5_0_5_3, 0.5_6_4_6, 0.4_7_6_9, 0.5_1_7_9, 0.4_8_9_4, 0.4_9_9_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 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      str:






         """simple docstring"""
         a__  			=							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

         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(A     )
             a__  			=							StableDiffusionPipeline.from_pretrained(A     )

         # 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 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_cond_unet
         a__  			=							PNDMScheduler(skip_prk_steps=A     )
         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=A    ,					scheduler=A    ,					vae=A    ,					text_encoder=A    ,					tokenizer=A    ,					safety_checker=A    ,					feature_extractor=self.dummy_extractor    ,					)
         a__  			=							sd_pipe.to(A     )
         sd_pipe.set_progress_bar_config(disable=A     )

         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 		__UpperCAmelCase(		unittest.TestCase			):





     """simple docstring"""



     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      Dict:






         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Any:






         """simple docstring"""
         a__  			=							"""cuda"""
         a__  			=							StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""     )
         a__  			=							pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							"""a photo of a cat"""
         a__  			=							{
             """editing_prompt""": ["""sunglasses"""],
             """reverse_editing_direction""": [False],
             """edit_warmup_steps""": 10,
             """edit_guidance_scale""": 6,
             """edit_threshold""": 0.9_5,
             """edit_momentum_scale""": 0.5,
             """edit_mom_beta""": 0.6,
         }

         a__  			=							3
         a__  			=							7

         # no sega enabled
         a__  			=							torch.Generator(A     )
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.3_4_6_7_3_1_1_3,
             0.3_8_4_9_2_7_3_3,
             0.3_7_5_9_7_3_5_2,
             0.3_4_0_8_6_3_3_5,
             0.3_5_6_5_0_7_4_8,
             0.3_5_5_7_9_2_0_5,
             0.3_3_8_4_7_6_3,
             0.3_4_3_4_0_2_3_6,
             0.3_5_7_3_2_7_1,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					**A    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.4_1_8_8_7_8_2_6,
             0.3_7_7_2_8_7_6_6,
             0.3_0_1_3_8_2_7_2,
             0.4_1_4_1_6_3_3_5,
             0.4_1_6_6_4_9_8_5,
             0.3_6_2_8_3_3_9_2,
             0.3_6_1_9_1_2_4_6,
             0.4_3_3_6_4_4_6_5,
             0.4_3_0_0_1_7_3_2,
         ]

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

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Any:






         """simple docstring"""
         a__  			=							"""cuda"""
         a__  			=							StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""     )
         a__  			=							pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							"""an image of a crowded boulevard, realistic, 4k"""
         a__  			=							{
             """editing_prompt""": """crowd, crowded, people""",
             """reverse_editing_direction""": True,
             """edit_warmup_steps""": 10,
             """edit_guidance_scale""": 8.3,
             """edit_threshold""": 0.9,
             """edit_momentum_scale""": 0.5,
             """edit_mom_beta""": 0.6,
         }

         a__  			=							9
         a__  			=							7

         # no sega enabled
         a__  			=							torch.Generator(A     )
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.4_3_4_9_7_9_9_8,
             0.9_1_8_1_4_0_6_5,
             0.7_5_4_0_7_3_9,
             0.5_5_5_8_0_2_0_5,
             0.8_4_6_7_2_6_5,
             0.5_3_8_9_6_9_1,
             0.6_2_5_7_4_5_0_6,
             0.5_8_8_9_7_7_6_3,
             0.5_0_9_2_6_7_5_7,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					**A    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.3_0_8_9_7_1_9,
             0.3_0_5_0_0_1_4_4,
             0.2_9_0_1_6_0_4_2,
             0.3_0_6_3_0_9_6_4,
             0.3_2_5_6_8_7,
             0.2_9_4_1_9_2_2_5,
             0.2_9_0_8_0_9_1,
             0.2_8_7_2_3_5_9_8,
             0.2_7_6_9_6_2_9_4,
         ]

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

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

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Any     )      ->      int:






         """simple docstring"""
         a__  			=							"""cuda"""
         a__  			=							StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""     )
         a__  			=							pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							"""a castle next to a river"""
         a__  			=							{
             """editing_prompt""": ["""boat on a river, boat""", """monet, impression, sunrise"""],
             """reverse_editing_direction""": False,
             """edit_warmup_steps""": [15, 18],
             """edit_guidance_scale""": 6,
             """edit_threshold""": [0.9, 0.8],
             """edit_momentum_scale""": 0.5,
             """edit_mom_beta""": 0.6,
         }

         a__  			=							48
         a__  			=							7

         # no sega enabled
         a__  			=							torch.Generator(A     )
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.7_5_1_6_3_5_5_5,
             0.7_6_0_3_7_1_4_5,
             0.6_1_7_8_5,
             0.9_1_8_9_6_7_3,
             0.8_6_2_7_7_0_1,
             0.8_5_1_8_9_6_9_4,
             0.8_5_1_2_8_1_3,
             0.8_7_0_1_2_0_7_6,
             0.8_3_1_2_8_5_7,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					**A    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.7_3_5_5_3_3_6_5,
             0.7_5_3_7_2_7_1,
             0.7_4_3_4_1_9_0_5,
             0.6_6_4_8_0_3_5_6,
             0.6_4_7_2_9_2_5,
             0.6_3_0_3_9_4_1_6,
             0.6_4_8_1_2_9_0_5,
             0.6_7_4_9_7_1_7,
             0.6_5_1_7_1_0_2,
         ]

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

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






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






         """simple docstring"""
         a__  			=							"""cuda"""
         a__  			=							StableDiffusionPipeline.from_pretrained("""runwayml/stable-diffusion-v1-5"""    ,					torch_dtype=torch.floataa     )
         a__  			=							pipe.to(A     )
         pipe.set_progress_bar_config(disable=A     )

         a__  			=							"""a photo of a cat"""
         a__  			=							{
             """editing_prompt""": ["""sunglasses"""],
             """reverse_editing_direction""": [False],
             """edit_warmup_steps""": 10,
             """edit_guidance_scale""": 6,
             """edit_threshold""": 0.9_5,
             """edit_momentum_scale""": 0.5,
             """edit_mom_beta""": 0.6,
         }

         a__  			=							3
         a__  			=							7

         # no sega enabled
         a__  			=							torch.Generator(A     )
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.3_4_8_8_7_6_9_5,
             0.3_8_7_6_9_5_3,
             0.3_7_5,
             0.3_4_4_2_3_8_2_8,
             0.3_5_8_1_5_4_3,
             0.3_5_7_1_7_7_7_3,
             0.3_3_8_3_7_8_9,
             0.3_4_5_7_0_3_1_2,
             0.3_5_9_3_7_5,
         ]

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

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

         # with sega enabled
         # generator = torch.manual_seed(seed)
         generator.manual_seed(A     )
         a__  			=							pipe(
             [prompt]    ,					generator=A    ,					guidance_scale=A    ,					num_inference_steps=50    ,					output_type="""np"""    ,					width=5_12    ,					height=5_12    ,					**A    ,					)

         a__  			=							output.images
         a__  			=							image[0, -3:, -3:, -1]
         a__  			=							[
             0.4_2_2_8_5_1_5_6,
             0.3_6_9_1_4_0_6_2,
             0.2_9_0_7_7_1_4_8,
             0.4_2_0_4_1_0_1_6,
             0.4_1_9_1_8_9_4_5,
             0.3_5_4_9_8_0_4_7,
             0.3_6_1_8_1_6_4,
             0.4_4_2_3_8_2_8,
             0.4_3_1_1_5_2_3_4,
         ]

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

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












import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class 		__UpperCAmelCase(		lowercase__							,						unittest.TestCase			):





     """simple docstring"""



     __UpperCAmelCase						:			Optional[int]							=     KandinskyVaaPriorEmbaEmbPipeline
     __UpperCAmelCase						:			str							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Dict							=     ["""prompt""", """image"""]
     __UpperCAmelCase						:			Optional[Any]							=     [
         """num_images_per_prompt""",
         """strength""",
         """generator""",
         """num_inference_steps""",
         """latents""",
         """negative_prompt""",
         """guidance_scale""",
         """output_type""",
         """return_dict""",
     ]
     __UpperCAmelCase						:			Dict							=     False

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






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[Any]     )      ->      Any:






         """simple docstring"""
         return 32

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      int:






         """simple docstring"""
         return self.time_input_dim

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[Any]:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]     )      ->      Tuple:






         """simple docstring"""
         return 1_00

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Union[str, Any]     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
         return tokenizer

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






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPTextConfig(
             bos_token_id=0    ,					eos_token_id=2    ,					hidden_size=self.text_embedder_hidden_size    ,					projection_dim=self.text_embedder_hidden_size    ,					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 CLIPTextModelWithProjection(A     )

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      List[Any]:






         """simple docstring"""
         torch.manual_seed(0     )

         a__  			=							{
             """num_attention_heads""": 2,
             """attention_head_dim""": 12,
             """embedding_dim""": self.text_embedder_hidden_size,
             """num_layers""": 1,
         }

         a__  			=							PriorTransformer(**A     )
         # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
         a__  			=							nn.Parameter(torch.ones(model.clip_std.shape     )     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         torch.manual_seed(0     )
         a__  			=							CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size    ,					image_size=2_24    ,					projection_dim=self.text_embedder_hidden_size    ,					intermediate_size=37    ,					num_attention_heads=4    ,					num_channels=3    ,					num_hidden_layers=5    ,					patch_size=14    ,					)

         a__  			=							CLIPVisionModelWithProjection(A     )
         return model

     @property
     def 					__SCREAMING_SNAKE_CASE		(   self			:							int     )      ->      List[str]:






         """simple docstring"""
         a__  			=							CLIPImageProcessor(
             crop_size=2_24    ,					do_center_crop=A    ,					do_normalize=A    ,					do_resize=A    ,					image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,					image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,					resample=3    ,					size=2_24    ,					)

         return image_processor

     def 					__SCREAMING_SNAKE_CASE		(   self			:							List[Any]     )      ->      Tuple:






         """simple docstring"""
         a__  			=							self.dummy_prior
         a__  			=							self.dummy_image_encoder
         a__  			=							self.dummy_text_encoder
         a__  			=							self.dummy_tokenizer
         a__  			=							self.dummy_image_processor

         a__  			=							UnCLIPScheduler(
             variance_type="""fixed_small_log"""    ,					prediction_type="""sample"""    ,					num_train_timesteps=10_00    ,					clip_sample=A    ,					clip_sample_range=1_0.0    ,					)

         a__  			=							{
             """prior""": prior,
             """image_encoder""": image_encoder,
             """text_encoder""": text_encoder,
             """tokenizer""": tokenizer,
             """scheduler""": scheduler,
             """image_processor""": image_processor,
         }

         return components

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Optional[int]    ,					A			:							Tuple    ,					A			:							Optional[Any]=0     )      ->      Any:






         """simple docstring"""
         if str(A     ).startswith("""mps"""     ):
             a__  			=							torch.manual_seed(A     )
         else:
             a__  			=							torch.Generator(device=A     ).manual_seed(A     )

         a__  			=							floats_tensor((1, 3, 64, 64)    ,					rng=random.Random(A     )     ).to(A     )
         a__  			=							image.cpu().permute(0    ,					2    ,					3    ,					1     )[0]
         a__  			=							Image.fromarray(np.uinta(A     )     ).convert("""RGB"""     ).resize((2_56, 2_56)     )

         a__  			=							{
             """prompt""": """horse""",
             """image""": init_image,
             """strength""": 0.5,
             """generator""": generator,
             """guidance_scale""": 4.0,
             """num_inference_steps""": 2,
             """output_type""": """np""",
         }
         return inputs

     def 					__SCREAMING_SNAKE_CASE		(   self			:							Dict     )      ->      Optional[int]:






         """simple docstring"""
         a__  			=							"""cpu"""

         a__  			=							self.get_dummy_components()

         a__  			=							self.pipeline_class(**A     )
         a__  			=							pipe.to(A     )

         pipe.set_progress_bar_config(disable=A     )

         a__  			=							pipe(**self.get_dummy_inputs(A     )     )
         a__  			=							output.image_embeds

         a__  			=							pipe(
             **self.get_dummy_inputs(A     )    ,					return_dict=A    ,					)[0]

         a__  			=							image[0, -10:]
         a__  			=							image_from_tuple[0, -10:]

         assert image.shape == (1, 32)

         a__  			=							np.array(
             [
                 0.1_0_7_1_2_8_4,
                 1.3_3_3_0_2_7_1,
                 0.6_1_2_6_0_2_2_3,
                 -0.6_6_9_1_0_6_5,
                 -0.3_8_4_6_8_5_2,
                 -1.0_3_0_3_6_6_1,
                 0.2_2_7_1_6_1_1_1,
                 0.0_3_3_4_8_9_0_1,
                 0.3_0_0_4_0_6_7_5,
                 -0.2_4_8_0_5_0_2_9,
             ]     )

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

     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							str     )      ->      Optional[Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							True
         a__  			=							False

         self._test_inference_batch_single_identical(
             test_max_difference=A    ,					relax_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






     @skip_mps
     def 					__SCREAMING_SNAKE_CASE		(   self			:							Tuple     )      ->      Union[str, Any]:






         """simple docstring"""
         a__  			=							torch_device == """cpu"""
         a__  			=							False

         self._test_attention_slicing_forward_pass(
             test_max_difference=A    ,					test_mean_pixel_difference=A    ,					)






