
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


snake_case       :					List[str]        =  logging.get_logger(__name__)  # pylint: disable=invalid-name

snake_case       :					str        =  '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[Any]				,						UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Any=8		):




      """simple docstring"""





      a		:Optional[int]											= height // scale_factor**2
      if height % scale_factor**2 != 0:
            new_height += 1
      a		:Any											= width // scale_factor**2
      if width % scale_factor**2 != 0:
            new_width += 1
      return new_height * scale_factor, new_width * scale_factor
def        __lowerCamelCase		(				UpperCAmelCase_		:	Dict				,						UpperCAmelCase_		:	str=512				,						UpperCAmelCase_		:	Tuple=512		):




      """simple docstring"""





      a		:int											= pil_image.resize((w, h)				,						resample=Image.BICUBIC				,						reducing_gap=1		)
      a		:Any											= np.array(pil_image.convert('''RGB'''		)		)
      a		:List[Any]											= arr.astype(np.floataa		) / 127.5 - 1
      a		:str											= np.transpose(UpperCAmelCase_				,						[2, 0, 1]		)
      a		:str											= torch.from_numpy(UpperCAmelCase_		).unsqueeze(0		)
      return image







class 	_snake_case						(   _snake_case							):




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						):
       super().__init__()

       self.register_modules(
           unet=_lowerCamelCase      ,						scheduler=_lowerCamelCase      ,						movq=_lowerCamelCase      ,						)
       a		:str											= 2 ** (len(self.movq.config.block_out_channels					) - 1)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					):
       # get the original timestep using init_timestep
       a		:Optional[int]											= min(int(num_inference_steps * strength					)      ,						_lowerCamelCase					)

       a		:Optional[Any]											= max(num_inference_steps - init_timestep      ,						0					)
       a		:Tuple											= self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase=None					):
       if not isinstance(_lowerCamelCase      ,						(torch.Tensor, PIL.Image.Image, list)					):
             raise ValueError(
                 F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowerCamelCase					)}'''					)

       a		:Union[str, Any]											= image.to(device=_lowerCamelCase      ,						dtype=_lowerCamelCase					)

       a		:List[str]											= batch_size * num_images_per_prompt

       if image.shape[1] == 4:
             a		:Optional[Any]											= image

       else:
             if isinstance(_lowerCamelCase      ,						_lowerCamelCase					) and len(_lowerCamelCase					) != batch_size:
                   raise ValueError(
                       F'''You have passed a list of generators of length {len(_lowerCamelCase					)}, but requested an effective batch'''
                       F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''					)

             elif isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
                   a		:List[str]											= [
                       self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(_lowerCamelCase					)
                   ]
                   a		:Dict											= torch.cat(_lowerCamelCase      ,						dim=0					)
             else:
                   a		:List[str]											= self.movq.encode(_lowerCamelCase					).latent_dist.sample(_lowerCamelCase					)

             a		:Optional[int]											= self.movq.config.scaling_factor * init_latents

       a		:Union[str, Any]											= torch.cat([init_latents]      ,						dim=0					)

       a		:int											= init_latents.shape
       a		:Union[str, Any]											= randn_tensor(_lowerCamelCase      ,						generator=_lowerCamelCase      ,						device=_lowerCamelCase      ,						dtype=_lowerCamelCase					)

       # get latents
       a		:List[str]											= self.scheduler.add_noise(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)

       a		:List[str]											= init_latents

       return latents




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0					):
       if is_accelerate_available():
             from accelerate import cpu_offload
       else:
             raise ImportError('''Please install accelerate via `pip install accelerate`'''					)

       a		:List[Any]											= torch.device(F'''cuda:{gpu_id}'''					)

       a		:Any											= [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
             if cpu_offloaded_model is not None:
                   cpu_offload(_lowerCamelCase      ,						_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0					):
       if is_accelerate_available() and is_accelerate_version('''>='''      ,						'''0.17.0.dev0'''					):
             from accelerate import cpu_offload_with_hook
       else:
             raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''					)

       a		:Any											= torch.device(F'''cuda:{gpu_id}'''					)

       if self.device.type != "cpu":
             self.to('''cpu'''      ,						silence_dtype_warnings=_lowerCamelCase					)
             torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       a		:Any											= None
       for cpu_offloaded_model in [self.unet, self.movq]:
             a		:Optional[int]											= cpu_offload_with_hook(_lowerCamelCase      ,						_lowerCamelCase      ,						prev_module_hook=_lowerCamelCase					)

       # We'll offload the last model manually.
       a		:Optional[int]											= hook




 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def  SCREAMING_SNAKE_CASE__       (	self					):
       if not hasattr(self.unet      ,						'''_hf_hook'''					):
             return self.device
       for module in self.unet.modules():
             if (
                 hasattr(_lowerCamelCase      ,						'''_hf_hook'''					)
                 and hasattr(module._hf_hook      ,						'''execution_device'''					)
                 and module._hf_hook.execution_device is not None
             ):
                   return torch.device(module._hf_hook.execution_device					)
       return self.device




 @torch.no_grad()
 @replace_example_docstring(_lowerCamelCase					)
 def __call__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 512      ,						_lowerCamelCase = 512      ,						_lowerCamelCase = 100      ,						_lowerCamelCase = 4.0      ,						_lowerCamelCase = 0.3      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = None      ,						_lowerCamelCase = "pil"      ,						_lowerCamelCase = True      ,						):
       a		:Union[str, Any]											= self._execution_device

       a		:Optional[Any]											= guidance_scale > 1.0

       if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
             a		:int											= torch.cat(_lowerCamelCase      ,						dim=0					)
       if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
             a		:str											= torch.cat(_lowerCamelCase      ,						dim=0					)
       if isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
             a		:Tuple											= torch.cat(_lowerCamelCase      ,						dim=0					)

       a		:Tuple											= image_embeds.shape[0]

       if do_classifier_free_guidance:
             a		:int											= image_embeds.repeat_interleave(_lowerCamelCase      ,						dim=0					)
             a		:List[str]											= negative_image_embeds.repeat_interleave(_lowerCamelCase      ,						dim=0					)
             a		:Optional[Any]											= hint.repeat_interleave(_lowerCamelCase      ,						dim=0					)

       a		:Optional[int]											= torch.cat([negative_image_embeds, image_embeds]      ,						dim=0					).to(dtype=self.unet.dtype      ,						device=_lowerCamelCase					)
       a		:List[Any]											= torch.cat([hint, hint]      ,						dim=0					).to(dtype=self.unet.dtype      ,						device=_lowerCamelCase					)

       if not isinstance(_lowerCamelCase      ,						_lowerCamelCase					):
             a		:List[str]											= [image]
       if not all(isinstance(_lowerCamelCase      ,						(PIL.Image.Image, torch.Tensor)					) for i in image					):
             raise ValueError(
                 F'''Input is in incorrect format: {[type(_lowerCamelCase					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''					)

       a		:List[Any]											= torch.cat([prepare_image(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					) for i in image]      ,						dim=0					)
       a		:Optional[Any]											= image.to(dtype=image_embeds.dtype      ,						device=_lowerCamelCase					)

       a		:Any											= self.movq.encode(_lowerCamelCase					)['''latents''']
       a		:Any											= latents.repeat_interleave(_lowerCamelCase      ,						dim=0					)
       self.scheduler.set_timesteps(_lowerCamelCase      ,						device=_lowerCamelCase					)
       a		:Optional[int]											= self.get_timesteps(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= timesteps[:1].repeat(batch_size * num_images_per_prompt					)
       a		:List[str]											= downscale_height_and_width(_lowerCamelCase      ,						_lowerCamelCase      ,						self.movq_scale_factor					)
       a		:Optional[Any]											= self.prepare_latents(
           _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						image_embeds.dtype      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       for i, t in enumerate(self.progress_bar(_lowerCamelCase					)					):
             # expand the latents if we are doing classifier free guidance
             a		:Dict											= torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

             a		:str											= {'''image_embeds''': image_embeds, '''hint''': hint}
             a		:str											= self.unet(
                 sample=_lowerCamelCase      ,						timestep=_lowerCamelCase      ,						encoder_hidden_states=_lowerCamelCase      ,						added_cond_kwargs=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						)[0]

             if do_classifier_free_guidance:
                   a		:Union[str, Any]											= noise_pred.split(latents.shape[1]      ,						dim=1					)
                   a		:List[str]											= noise_pred.chunk(2					)
                   a		:Union[str, Any]											= variance_pred.chunk(2					)
                   a		:str											= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                   a		:Optional[Any]											= torch.cat([noise_pred, variance_pred_text]      ,						dim=1					)

             if not (
                 hasattr(self.scheduler.config      ,						'''variance_type'''					)
                 and self.scheduler.config.variance_type in ["learned", "learned_range"]
             ):
                   a		:List[str]											= noise_pred.split(latents.shape[1]      ,						dim=1					)

             # compute the previous noisy sample x_t -> x_t-1

             a		:Tuple											= self.scheduler.step(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						)[0]

       # post-processing
       a		:str											= self.movq.decode(_lowerCamelCase      ,						force_not_quantize=_lowerCamelCase					)['''sample''']

       if output_type not in ["pt", "np", "pil"]:
             raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''					)

       if output_type in ["np", "pil"]:
             a		:Dict											= image * 0.5 + 0.5
             a		:List[str]											= image.clamp(0      ,						1					)
             a		:Tuple											= image.cpu().permute(0      ,						2      ,						3      ,						1					).float().numpy()

       if output_type == "pil":
             a		:Optional[Any]											= self.numpy_to_pil(_lowerCamelCase					)

       if not return_dict:
             return (image,)

       return ImagePipelineOutput(images=_lowerCamelCase					)









import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 	_snake_case						(   unittest.TestCase							):
 # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
 SCREAMING_SNAKE_CASE__   = (ScoreSdeVeScheduler,)
 SCREAMING_SNAKE_CASE__   = ()




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Tuple											= 4
       a		:List[str]											= 3
       a		:Any											= 8
       a		:Any											= 8

       a		:List[str]											= torch.rand((batch_size, num_channels, height, width)					)

       return sample




 @property
 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:int											= 4
       a		:Union[str, Any]											= 3
       a		:Optional[Any]											= 8
       a		:Tuple											= 8

       a		:Dict											= batch_size * num_channels * height * width
       a		:Optional[int]											= torch.arange(_lowerCamelCase					)
       a		:List[Any]											= sample.reshape(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase					)
       a		:Union[str, Any]											= sample / num_elems
       a		:Dict											= sample.permute(3      ,						0      ,						1      ,						2					)

       return sample




 def  SCREAMING_SNAKE_CASE__       (	self					):
       def model(_lowerCamelCase      ,						_lowerCamelCase      ,						*_lowerCamelCase					):
             return sample * t / (t + 1)

       return model




 def  SCREAMING_SNAKE_CASE__       (	self      ,						**_lowerCamelCase					):
       a		:str											= {
           '''num_train_timesteps''': 2000,
           '''snr''': 0.15,
           '''sigma_min''': 0.01,
           '''sigma_max''': 1348,
           '''sampling_eps''': 1e-5,
       }

       config.update(**_lowerCamelCase					)
       return config




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Optional[int]											= dict(self.forward_default_kwargs					)

       for scheduler_class in self.scheduler_classes:
             a		:Union[str, Any]											= self.dummy_sample
             a		:Tuple											= 0.1 * sample

             a		:Union[str, Any]											= self.get_scheduler_config(**_lowerCamelCase					)
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Optional[int]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:List[str]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Dict											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[Any]											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase=0      ,						**_lowerCamelCase					):
       a		:Dict											= dict(self.forward_default_kwargs					)
       kwargs.update(_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:int											= self.dummy_sample
             a		:str											= 0.1 * sample

             a		:Optional[Any]											= self.get_scheduler_config()
             a		:Union[str, Any]											= scheduler_class(**_lowerCamelCase					)

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_lowerCamelCase					)
                   a		:Union[str, Any]											= scheduler_class.from_pretrained(_lowerCamelCase					)

             a		:Union[str, Any]											= scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Optional[int]											= new_scheduler.step_pred(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

             a		:List[str]											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= new_scheduler.step_correct(
                 _lowerCamelCase      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler correction are not identical"




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for timesteps in [10, 100, 1000]:
             self.check_over_configs(num_train_timesteps=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]      ,						[1, 100, 1000]					):
             self.check_over_configs(sigma_min=_lowerCamelCase      ,						sigma_max=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       for t in [0.1, 0.5, 0.75]:
             self.check_over_forward(time_step=_lowerCamelCase					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:Dict											= dict(self.forward_default_kwargs					)

       a		:Optional[Any]											= self.scheduler_classes[0]
       a		:Union[str, Any]											= self.get_scheduler_config()
       a		:List[Any]											= scheduler_class(**_lowerCamelCase					)

       a		:Union[str, Any]											= 3

       a		:List[Any]											= self.dummy_model()
       a		:Dict											= self.dummy_sample_deter

       scheduler.set_sigmas(_lowerCamelCase					)
       scheduler.set_timesteps(_lowerCamelCase					)
       a		:str											= torch.manual_seed(0					)

       for i, t in enumerate(scheduler.timesteps					):
             a		:Optional[int]											= scheduler.sigmas[i]

             for _ in range(scheduler.config.correct_steps					):
                   with torch.no_grad():
                         a		:Dict											= model(_lowerCamelCase      ,						_lowerCamelCase					)
                   a		:int											= scheduler.step_correct(_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					).prev_sample

             with torch.no_grad():
                   a		:Any											= model(_lowerCamelCase      ,						_lowerCamelCase					)

             a		:Optional[Any]											= scheduler.step_pred(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						generator=_lowerCamelCase      ,						**_lowerCamelCase					)
             a							,       a		:Any											= output.prev_sample, output.prev_sample_mean

       a		:Dict											= torch.sum(torch.abs(_lowerCamelCase					)					)
       a		:int											= torch.mean(torch.abs(_lowerCamelCase					)					)

       assert np.isclose(result_sum.item()      ,						143_7275_8528.0					)
       assert np.isclose(result_mean.item()      ,						1871_4530.0					)




 def  SCREAMING_SNAKE_CASE__       (	self					):
       a		:str											= dict(self.forward_default_kwargs					)

       a		:Union[str, Any]											= kwargs.pop('''num_inference_steps'''      ,						_lowerCamelCase					)

       for scheduler_class in self.scheduler_classes:
             a		:Optional[int]											= self.get_scheduler_config()
             a		:str											= scheduler_class(**_lowerCamelCase					)

             a		:List[str]											= self.dummy_sample
             a		:List[Any]											= 0.1 * sample

             if num_inference_steps is not None and hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   scheduler.set_timesteps(_lowerCamelCase					)
             elif num_inference_steps is not None and not hasattr(_lowerCamelCase      ,						'''set_timesteps'''					):
                   a		:Dict											= num_inference_steps

             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						0      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample
             a		:Tuple											= scheduler.step_pred(_lowerCamelCase      ,						1      ,						_lowerCamelCase      ,						generator=torch.manual_seed(0					)      ,						**_lowerCamelCase					).prev_sample

             self.assertEqual(output_a.shape      ,						sample.shape					)
             self.assertEqual(output_a.shape      ,						output_a.shape					)





