

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


__lowercase     : Any       =						logging.get_logger(__name__)  # pylint: disable=invalid-name



class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__()
      self.register_modules(
          vae=SCREAMING_SNAKE_CASE_   ,text_encoder=SCREAMING_SNAKE_CASE_   ,tokenizer=SCREAMING_SNAKE_CASE_   ,unet=SCREAMING_SNAKE_CASE_   ,scheduler=SCREAMING_SNAKE_CASE_   ,safety_checker=SCREAMING_SNAKE_CASE_   ,feature_extractor=SCREAMING_SNAKE_CASE_   ,)





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_ = "auto"    ):

      '''simple docstring'''
      if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          snake_case							:		List[str]        =		self.unet.config.attention_head_dim // 2
      self.unet.set_attention_slice(SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self    ):

      '''simple docstring'''
      self.enable_attention_slicing(SCREAMING_SNAKE_CASE_    )





  @torch.no_grad()
  def __call__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = 512   ,SCREAMING_SNAKE_CASE_ = 512   ,SCREAMING_SNAKE_CASE_ = 50   ,SCREAMING_SNAKE_CASE_ = 7.5   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 1   ,SCREAMING_SNAKE_CASE_ = 0.0   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = "pil"   ,SCREAMING_SNAKE_CASE_ = True   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 1   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''

      if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
          snake_case							:		List[Any]        =		1
      elif isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
          snake_case							:		Any        =		len(SCREAMING_SNAKE_CASE_    )
      else:
          raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_    )}"""    )

      if height % 8 != 0 or width % 8 != 0:
          raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

      if (callback_steps is None) or (
          callback_steps is not None and (not isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) or callback_steps <= 0)
      ):
          raise ValueError(
              F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
              F""" {type(SCREAMING_SNAKE_CASE_    )}."""    )

      # get prompt text embeddings
      snake_case							:		Any        =		self.tokenizer(
          SCREAMING_SNAKE_CASE_   ,padding="""max_length"""   ,max_length=self.tokenizer.model_max_length   ,return_tensors="""pt"""   ,)
      snake_case							:		int        =		text_inputs.input_ids

      if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
          snake_case							:		str        =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
          logger.warning(
              """The following part of your input was truncated because CLIP can only handle sequences up to"""
              F""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )
          snake_case							:		Optional[int]        =		text_input_ids[:, : self.tokenizer.model_max_length]

      if text_embeddings is None:
          snake_case							:		List[Any]        =		self.text_encoder(text_input_ids.to(self.device    )    )[0]

      # duplicate text embeddings for each generation per prompt, using mps friendly method
      snake_case      ,		snake_case      ,		snake_case							:		Optional[int]        =		text_embeddings.shape
      snake_case							:		Any        =		text_embeddings.repeat(1   ,SCREAMING_SNAKE_CASE_   ,1    )
      snake_case							:		List[str]        =		text_embeddings.view(bs_embed * num_images_per_prompt   ,SCREAMING_SNAKE_CASE_   ,-1    )

      # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
      # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
      # corresponds to doing no classifier free guidance.
      snake_case							:		int        =		guidance_scale > 1.0
      # get unconditional embeddings for classifier free guidance
      if do_classifier_free_guidance:
          snake_case							:		List[str]
          if negative_prompt is None:
              snake_case							:		int        =		[""""""]
          elif type(SCREAMING_SNAKE_CASE_    ) is not type(SCREAMING_SNAKE_CASE_    ):
              raise TypeError(
                  F"""`negative_prompt` should be the same type to `prompt`, but got {type(SCREAMING_SNAKE_CASE_    )} !="""
                  F""" {type(SCREAMING_SNAKE_CASE_    )}."""    )
          elif isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):
              snake_case							:		Dict        =		[negative_prompt]
          elif batch_size != len(SCREAMING_SNAKE_CASE_    ):
              raise ValueError(
                  F"""`negative_prompt`: {negative_prompt} has batch size {len(SCREAMING_SNAKE_CASE_    )}, but `prompt`:"""
                  F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                  """ the batch size of `prompt`."""    )
          else:
              snake_case							:		Optional[Any]        =		negative_prompt

          snake_case							:		List[str]        =		text_input_ids.shape[-1]
          snake_case							:		List[str]        =		self.tokenizer(
              SCREAMING_SNAKE_CASE_   ,padding="""max_length"""   ,max_length=SCREAMING_SNAKE_CASE_   ,truncation=SCREAMING_SNAKE_CASE_   ,return_tensors="""pt"""   ,)
          snake_case							:		str        =		self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

          # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
          snake_case							:		Optional[Any]        =		uncond_embeddings.shape[1]
          snake_case							:		Union[str, Any]        =		uncond_embeddings.repeat(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,1    )
          snake_case							:		Any        =		uncond_embeddings.view(batch_size * num_images_per_prompt   ,SCREAMING_SNAKE_CASE_   ,-1    )

          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          snake_case							:		List[str]        =		torch.cat([uncond_embeddings, text_embeddings]    )

      # get the initial random noise unless the user supplied it

      # Unlike in other pipelines, latents need to be generated in the target device
      # for 1-to-1 results reproducibility with the CompVis implementation.
      # However this currently doesn't work in `mps`.
      snake_case							:		Union[str, Any]        =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
      snake_case							:		Tuple        =		(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
      snake_case							:		Optional[int]        =		text_embeddings.dtype
      if latents is None:
          if self.device.type == "mps":
              # randn does not exist on mps
              snake_case							:		Optional[int]        =		torch.randn(
                  SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device="""cpu"""   ,dtype=SCREAMING_SNAKE_CASE_    ).to(self.device    )
              snake_case							:		Any        =		torch.randn(SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device="""cpu"""   ,dtype=SCREAMING_SNAKE_CASE_    ).to(
                  self.device    )
          else:
              snake_case							:		List[Any]        =		torch.randn(
                  SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device=self.device   ,dtype=SCREAMING_SNAKE_CASE_    )
              snake_case							:		Optional[int]        =		torch.randn(SCREAMING_SNAKE_CASE_   ,generator=SCREAMING_SNAKE_CASE_   ,device=self.device   ,dtype=SCREAMING_SNAKE_CASE_    )
      else:
          if latents_reference.shape != latents_shape:
              raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
          snake_case							:		str        =		latents_reference.to(self.device    )
          snake_case							:		Optional[int]        =		latents.to(self.device    )

      # This is the key part of the pipeline where we
      # try to ensure that the generated images w/ the same seed
      # but different sizes actually result in similar images
      snake_case							:		Optional[Any]        =		(latents_shape[3] - latents_shape_reference[3]) // 2
      snake_case							:		List[Any]        =		(latents_shape[2] - latents_shape_reference[2]) // 2
      snake_case							:		List[Any]        =		latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
      snake_case							:		Dict        =		latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
      snake_case							:		List[str]        =		0 if dx < 0 else dx
      snake_case							:		Any        =		0 if dy < 0 else dy
      snake_case							:		Dict        =		max(-dx   ,0    )
      snake_case							:		Optional[Any]        =		max(-dy   ,0    )
      # import pdb
      # pdb.set_trace()
      snake_case							:		Optional[Any]        =		latents_reference[:, :, dy : dy + h, dx : dx + w]

      # set timesteps
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_    )

      # Some schedulers like PNDM have timesteps as arrays
      # It's more optimized to move all timesteps to correct device beforehand
      snake_case							:		Union[str, Any]        =		self.scheduler.timesteps.to(self.device    )

      # scale the initial noise by the standard deviation required by the scheduler
      snake_case							:		List[str]        =		latents * self.scheduler.init_noise_sigma

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
      # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
      # and should be between [0, 1]
      snake_case							:		List[str]        =		"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
      snake_case							:		Optional[Any]        =		{}
      if accepts_eta:
          snake_case							:		int        =		eta

      for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_    )    ):
          # expand the latents if we are doing classifier free guidance
          snake_case							:		Optional[Any]        =		torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
          snake_case							:		List[str]        =		self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

          # predict the noise residual
          snake_case							:		str        =		self.unet(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,encoder_hidden_states=SCREAMING_SNAKE_CASE_    ).sample

          # perform guidance
          if do_classifier_free_guidance:
              snake_case      ,		snake_case							:		str        =		noise_pred.chunk(2    )
              snake_case							:		List[Any]        =		noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

          # compute the previous noisy sample x_t -> x_t-1
          snake_case							:		int        =		self.scheduler.step(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_    ).prev_sample

          # call the callback, if provided
          if callback is not None and i % callback_steps == 0:
              callback(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		Optional[Any]        =		1 / 0.1_82_15 * latents
      snake_case							:		Any        =		self.vae.decode(SCREAMING_SNAKE_CASE_    ).sample

      snake_case							:		str        =		(image / 2 + 0.5).clamp(0   ,1    )

      # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
      snake_case							:		Optional[int]        =		image.cpu().permute(0   ,2   ,3   ,1    ).float().numpy()

      if self.safety_checker is not None:
          snake_case							:		Tuple        =		self.feature_extractor(self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )   ,return_tensors="""pt"""    ).to(
              self.device    )
          snake_case      ,		snake_case							:		Optional[Any]        =		self.safety_checker(
              images=SCREAMING_SNAKE_CASE_   ,clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
      else:
          snake_case							:		List[str]        =		None

      if output_type == "pil":
          snake_case							:		Tuple        =		self.numpy_to_pil(SCREAMING_SNAKE_CASE_    )

      if not return_dict:
          return (image, has_nsfw_concept)

      return StableDiffusionPipelineOutput(images=SCREAMING_SNAKE_CASE_   ,nsfw_content_detected=SCREAMING_SNAKE_CASE_    )





'''simple docstring'''
import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


lowercase__						       =      logging.get_logger(__name__)

enable_full_determinism()






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



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






				"""simple docstring"""







				snake_case  :       Any     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       str     			=						(32, 32)

				snake_case  :       Optional[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Tuple     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



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






				"""simple docstring"""







				return (3, 32, 32)



		@property
		def       lowerCAmelCase				(      self				:			str      )						->			Dict:






				"""simple docstring"""







				return (3, 32, 32)



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






				"""simple docstring"""







				snake_case  :       Any     			=						{
				    '''block_out_channels''': (32, 64),
				    '''down_block_types''': ('''DownBlock2D''', '''AttnDownBlock2D'''),
				    '''up_block_types''': ('''AttnUpBlock2D''', '''UpBlock2D'''),
				    '''attention_head_dim''': 3,
				    '''out_channels''': 3,
				    '''in_channels''': 3,
				    '''layers_per_block''': 2,
				    '''sample_size''': 32,
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



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






				"""simple docstring"""







				snake_case  :       str     			=						4
				snake_case  :       Tuple     			=						4
				snake_case  :       str     			=						(32, 32)

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor([10]      ).to(UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



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






				"""simple docstring"""







				return (4, 32, 32)



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






				"""simple docstring"""







				return (4, 32, 32)



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






				"""simple docstring"""







				snake_case  :       List[Any]     			=						{
				    '''sample_size''': 32,
				    '''in_channels''': 4,
				    '''out_channels''': 4,
				    '''layers_per_block''': 2,
				    '''block_out_channels''': (32, 64),
				    '''attention_head_dim''': 32,
				    '''down_block_types''': ('''DownBlock2D''', '''DownBlock2D'''),
				    '''up_block_types''': ('''UpBlock2D''', '''UpBlock2D'''),
				}
				snake_case  :       str     			=						self.dummy_input
				return init_dict, inputs_dict



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )

				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			int      )						->			Dict:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model.to(UpperCamelCase__      )
				snake_case  :       Any     			=						model(**self.dummy_input      ).sample

				assert image is not None, "Make sure output is not None"



		@unittest.skipIf(torch_device != '''cuda'''  ,	'''This test is supposed to run on GPU'''      )
		def       lowerCAmelCase				(      self				:			Any      )						->			str:






				"""simple docstring"""







				snake_case			,snake_case  :       Any     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__      )
				model_accelerate.to(UpperCamelCase__      )
				model_accelerate.eval()

				snake_case  :       Optional[Any]     			=						torch.randn(
				    1  ,	model_accelerate.config.in_channels  ,	model_accelerate.config.sample_size  ,	model_accelerate.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       int     			=						noise.to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				snake_case  :       Union[str, Any]     			=						model_accelerate(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				# two models don't need to stay in the device at the same time
				del model_accelerate
				torch.cuda.empty_cache()
				gc.collect()

				snake_case			,snake_case  :       List[Any]     			=						UNetaDModel.from_pretrained(
				    '''fusing/unet-ldm-dummy-update'''  ,	output_loading_info=UpperCamelCase__  ,	low_cpu_mem_usage=UpperCamelCase__      )
				model_normal_load.to(UpperCamelCase__      )
				model_normal_load.eval()
				snake_case  :       Union[str, Any]     			=						model_normal_load(UpperCamelCase__  ,	UpperCamelCase__      )['''sample''']

				assert torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )



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






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''fusing/unet-ldm-dummy-update'''      )
				model.eval()
				model.to(UpperCamelCase__      )

				snake_case  :       Any     			=						torch.randn(
				    1  ,	model.config.in_channels  ,	model.config.sample_size  ,	model.config.sample_size  ,	generator=torch.manual_seed(0      )  ,	)
				snake_case  :       Dict     			=						noise.to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor([10] * noise.shape[0]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Union[str, Any]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       int     			=						output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				snake_case  :       Tuple     			=						torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-3      )      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE    ,					unittest.TestCase					):







		"""simple docstring"""

		lowerCamelCase     							=						UNetaDModel
		lowerCamelCase     							=						"""sample"""



		@property
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[Any]=(32, 32)      )						->			Any:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						4
				snake_case  :       Tuple     			=						3

				snake_case  :       List[Any]     			=						floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       str     			=						torch.tensor(batch_size * [10]      ).to(dtype=torch.intaa  ,	device=UpperCamelCase__      )

				return {"sample": noise, "timestep": time_step}



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






				"""simple docstring"""







				return (3, 32, 32)



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






				"""simple docstring"""







				return (3, 32, 32)



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






				"""simple docstring"""







				snake_case  :       Dict     			=						{
				    '''block_out_channels''': [32, 64, 64, 64],
				    '''in_channels''': 3,
				    '''layers_per_block''': 1,
				    '''out_channels''': 3,
				    '''time_embedding_type''': '''fourier''',
				    '''norm_eps''': 1e-6,
				    '''mid_block_scale_factor''': math.sqrt(2.0      ),
				    '''norm_num_groups''': None,
				    '''down_block_types''': [
				        '''SkipDownBlock2D''',
				        '''AttnSkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				        '''SkipDownBlock2D''',
				    ],
				    '''up_block_types''': [
				        '''SkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				        '''AttnSkipUpBlock2D''',
				        '''SkipUpBlock2D''',
				    ],
				}
				snake_case  :       Dict     			=						self.dummy_input
				return init_dict, inputs_dict



		@slow
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			int:






				"""simple docstring"""







				snake_case			,snake_case  :       str     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''  ,	output_loading_info=UpperCamelCase__      )
				self.assertIsNotNone(UpperCamelCase__      )
				self.assertEqual(len(loading_info['''missing_keys''']      )  ,	0      )

				model.to(UpperCamelCase__      )
				snake_case  :       str     			=						self.dummy_input
				snake_case  :       Dict     			=						floats_tensor((4, 3) + (256, 256)      ).to(UpperCamelCase__      )
				snake_case  :       Union[str, Any]     			=						noise
				snake_case  :       List[Any]     			=						model(**UpperCamelCase__      )

				assert image is not None, "Make sure output is not None"



		@slow
		def       lowerCAmelCase				(      self				:			Union[str, Any]      )						->			str:






				"""simple docstring"""







				snake_case  :       int     			=						UNetaDModel.from_pretrained('''google/ncsnpp-celebahq-256'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Dict     			=						4
				snake_case  :       Optional[int]     			=						3
				snake_case  :       Tuple     			=						(256, 256)

				snake_case  :       Optional[int]     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       List[str]     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Dict     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       Optional[int]     			=						torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



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






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						UNetaDModel.from_pretrained('''fusing/ncsnpp-ffhq-ve-dummy-update'''      )
				model.to(UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						4
				snake_case  :       List[Any]     			=						3
				snake_case  :       Union[str, Any]     			=						(32, 32)

				snake_case  :       Any     			=						torch.ones((batch_size, num_channels) + sizes      ).to(UpperCamelCase__      )
				snake_case  :       Any     			=						torch.tensor(batch_size * [1e-4]      ).to(UpperCamelCase__      )

				with torch.no_grad():
						snake_case  :       Dict     			=						model(UpperCamelCase__  ,	UpperCamelCase__      ).sample

				snake_case  :       Optional[Any]     			=						output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				snake_case  :       int     			=						torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256]      )
				# fmt: on

				self.assertTrue(torch_all_close(UpperCamelCase__  ,	UpperCamelCase__  ,	rtol=1e-2      )      )



		def       lowerCAmelCase				(      self				:			List[str]      )						->			List[Any]:






				"""simple docstring"""







				pass


