





import os

from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home


lowerCAmelCase							:       Dict					 =	HUGGINGFACE_HUB_CACHE


lowerCAmelCase							:       Optional[Any]					 =	"config.json"
lowerCAmelCase							:       str					 =	"diffusion_pytorch_model.bin"
lowerCAmelCase							:       Union[str, Any]					 =	"diffusion_flax_model.msgpack"
lowerCAmelCase							:       str					 =	"model.onnx"
lowerCAmelCase							:       str					 =	"diffusion_pytorch_model.safetensors"
lowerCAmelCase							:       Optional[int]					 =	"weights.pb"
lowerCAmelCase							:       Tuple					 =	"https://huggingface.co"
lowerCAmelCase							:       Optional[Any]					 =	default_cache_path
lowerCAmelCase							:       List[Any]					 =	"diffusers_modules"
lowerCAmelCase							:       Dict					 =	os.getenv("""HF_MODULES_CACHE""", os.path.join(hf_cache_home, """modules"""))
lowerCAmelCase							:       int					 =	["fp16", "non-ema"]
lowerCAmelCase							:       Union[str, Any]					 =	".self_attn"







# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __init__( self		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







					super().__init__()
					self.register_modules(unet=A		,      scheduler=A	)







		@torch.no_grad()
		def __call__( self		,      A = 1		,      A = None		,      A = 50		,      A = "pil"		,      A = True		,      **A		,      )  -> Union[ImagePipelineOutput, Tuple]:







					'''simple docstring'''







					lowerCamelCase    		=  torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,      generator=A		,      )
					lowerCamelCase    		=  image.to(self.device	)

					# set step values
					self.scheduler.set_timesteps(A	)

					for t in self.progress_bar(self.scheduler.timesteps	):
								# 1. predict noise model_output
								lowerCamelCase    		=  self.unet(A		,      A	).sample

								# 2. predict previous mean of image x_t-1 and add variance depending on eta
								# eta corresponds to η in paper and should be between [0, 1]
								# do x_t -> x_t-1
								lowerCamelCase    		=  self.scheduler.step(A		,      A		,      A	).prev_sample

					lowerCamelCase    		=  (image / 2 + 0.5).clamp(0		,      1	)
					lowerCamelCase    		=  image.cpu().permute(0		,      2		,      3		,      1	).numpy()
					if output_type == "pil":
								lowerCamelCase    		=  self.numpy_to_pil(A	)

					if not return_dict:
								return (image,), "This is a local test"

					return ImagePipelineOutput(images=A	), "This is a local test"

