


'''simple docstring'''







def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     List[Any]				)	->		Dict:
						UpperCAmelCase_     : str         =					word.upper()
						UpperCAmelCase_     : Union[str, Any]         =					0
						UpperCAmelCase_     : Dict         =					{}
						UpperCAmelCase_     : List[Any]         =					[]

						for letter in word:
												if letter not in letter_nums:
																		UpperCAmelCase_     : int         =					str(snake_case__				)
																		next_num += 1
												word_pattern.append(letter_nums[letter]				)
						return ".".join(snake_case__				)


if __name__ == "__main__":
					import pprint
					import time

					snake_case_       :    Tuple          =					time.time()
					with open("dictionary.txt") as in_file:
										snake_case_       :    int          =					in_file.read().splitlines()

					snake_case_       :    int          =					{}
					for word in word_list:
										snake_case_       :    int          =					get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															snake_case_       :    Optional[Any]          =					[word]

					with open("word_patterns.txt", "w") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					snake_case_       :    Any          =					round(time.time() - start_time, 2)
					print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
					# Done!  9,581 word patterns found in 0.58 seconds.












'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __a (lowerCamelCase       ):
							__a   :				UNetaDModel
							__a   :				PNDMScheduler

							def __init__(							self							:       Optional[Any]    ,       __magic_name__							:       UNetaDModel    ,       __magic_name__							:       PNDMScheduler				)   ->    Tuple:







													"""simple docstring"""
													super().__init__()

													UpperCAmelCase_     : Optional[int]         =					PNDMScheduler.from_config(scheduler.config				)

													self.register_modules(unet=__magic_name__    ,       scheduler=__magic_name__				)



							@torch.no_grad()
							def __call__(							self							:       Union[str, Any]    ,       __magic_name__							:       int = 1    ,       __magic_name__							:       int = 50    ,       __magic_name__							:       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,       __magic_name__							:       Optional[str] = "pil"    ,       __magic_name__							:       bool = True    ,       **__magic_name__							:       Dict    ,       )   ->    Union[ImagePipelineOutput, Tuple]:







													"""simple docstring"""
													UpperCAmelCase_     : Dict         =					randn_tensor(
													    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    ,       generator=__magic_name__    ,       device=self.device    ,       )

													self.scheduler.set_timesteps(__magic_name__				)
													for t in self.progress_bar(self.scheduler.timesteps				):
																			UpperCAmelCase_     : Optional[int]         =					self.unet(__magic_name__    ,       __magic_name__				).sample

																			UpperCAmelCase_     : str         =					self.scheduler.step(__magic_name__    ,       __magic_name__    ,       __magic_name__				).prev_sample

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

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__magic_name__				)





