






# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE      (      a_       ):
     '''simple docstring'''


     def __init__(					self							:						Tuple       ,			UpperCAmelCase_							:						Optional[Any]       ,			UpperCAmelCase_							:						Dict   ):
       super().__init__()
       self.register_modules(unet=UpperCAmelCase_       ,			scheduler=UpperCAmelCase_   )






     @torch.no_grad()
     def __call__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Tuple = 1       ,			UpperCAmelCase_							:						List[str] = None       ,			UpperCAmelCase_							:						List[str] = 50       ,			UpperCAmelCase_							:						Any = "pil"       ,			UpperCAmelCase_							:						Any = True       ,			**UpperCAmelCase_							:						str       ,			):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     torch.randn(
           (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,			generator=UpperCAmelCase_       ,			)
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     image.to(self.device   )

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

       for t in self.progress_bar(self.scheduler.timesteps   ):
         # 1. predict noise model_output
         SCREAMING_SNAKE_CASE							:   str						    =     self.unet(UpperCAmelCase_       ,			UpperCAmelCase_   ).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
         SCREAMING_SNAKE_CASE							:   int						    =     self.scheduler.step(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   ).prev_sample

       SCREAMING_SNAKE_CASE							:   List[str]						    =     (image / 2 + 0.5).clamp(0       ,			1   )
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     image.cpu().permute(0       ,			2       ,			3       ,			1   ).numpy()
       if output_type == "pil":
         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.numpy_to_pil(UpperCAmelCase_   )

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

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










# Lint as: python3
import itertools
import os
import re


UpperCAmelCase   :    Any     =		re.compile(r"([A-Z]+)([A-Z][a-z])")
UpperCAmelCase   :    Optional[Any]     =		re.compile(r"([a-z\d])([A-Z])")

UpperCAmelCase   :    Optional[Any]     =		re.compile(r"(?<!_)_(?!_)")
UpperCAmelCase   :    Any     =		re.compile(r"(_{2,})")

UpperCAmelCase   :    List[Any]     =		r"^\w+(\.\w+)*$"

UpperCAmelCase   :    Any     =		r"<>:/\|?*"






def 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




			lowerCamelCase    		=  _uppercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			lowerCamelCase    		=  _lowercase_uppercase_re.sub(R"""\1_\2"""  ,							lowerCamelCase__      )
			return name.lower()






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  _single_underscore_re.split(lowerCamelCase__      )
			lowerCamelCase    		=  [_multiple_underscores_re.split(lowerCamelCase__      ) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(lowerCamelCase__      ) if n != """"""      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			return camelcase_to_snakecase(lowerCamelCase__      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]  ,							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			if os.path.basename(lowerCamelCase__      ) != name:
						raise ValueError(f'Should be a dataset name, not a path: {name}'      )
			if not re.match(_split_re  ,							lowerCamelCase__      ):
						raise ValueError(f'Split name should match \'{_split_re}\'\' but got \'{split}\'.'      )
			return f'{filename_prefix_for_name(lowerCamelCase__      )}-{split}'






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			if filetype_suffix:
						prefix += f'.{filetype_suffix}'
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )
			return f'{filepath}*'






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						int=None  ,							lowerCamelCase__		:						str=None      ):





			'''simple docstring'''




			lowerCamelCase    		=  filename_prefix_for_split(lowerCamelCase__  ,							lowerCamelCase__      )
			lowerCamelCase    		=  os.path.join(lowerCamelCase__  ,							lowerCamelCase__      )

			if shard_lengths:
						lowerCamelCase    		=  len(lowerCamelCase__      )
						lowerCamelCase    		=  [f'{prefix}-{shard_id:05d}-of-{num_shards:05d}' for shard_id in range(lowerCamelCase__      )]
						if filetype_suffix:
									lowerCamelCase    		=  [filename + f'.{filetype_suffix}' for filename in filenames]
						return filenames
			else:
						lowerCamelCase    		=  prefix
						if filetype_suffix:
									filename += f'.{filetype_suffix}'
						return [filename]

