


import argparse
import datetime


def        _UpperCamelCase							(      lowercase__      ):
						__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		{
						    '''0''': '''Sunday''',
						    '''1''': '''Monday''',
						    '''2''': '''Tuesday''',
						    '''3''': '''Wednesday''',
						    '''4''': '''Thursday''',
						    '''5''': '''Friday''',
						    '''6''': '''Saturday''',
						}

						__SCREAMING_SNAKE_CASE						:     Dict					=		{0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}

						# Validate
						if not 0 < len(lowercase__      ) < 11:
												raise ValueError('''Must be 10 characters long'''      )

						# Get month
						__SCREAMING_SNAKE_CASE						:     int					=		int(date_input[0] + date_input[1]      )
						# Validate
						if not 0 < m < 13:
												raise ValueError('''Month must be between 1 - 12'''      )

						__SCREAMING_SNAKE_CASE						:     str					=		date_input[2]
						# Validate
						if sep_a not in ["-", "/"]:
												raise ValueError('''Date separator must be \'-\' or \'/\''''      )

						# Get day
						__SCREAMING_SNAKE_CASE						:     int					=		int(date_input[3] + date_input[4]      )
						# Validate
						if not 0 < d < 32:
												raise ValueError('''Date must be between 1 - 31'''      )

						# Get second separator
						__SCREAMING_SNAKE_CASE						:     str					=		date_input[5]
						# Validate
						if sep_a not in ["-", "/"]:
												raise ValueError('''Date separator must be \'-\' or \'/\''''      )

						# Get year
						__SCREAMING_SNAKE_CASE						:     int					=		int(date_input[6] + date_input[7] + date_input[8] + date_input[9]      )
						# Arbitrary year range
						if not 45 < y < 8500:
												raise ValueError(
												    '''Year out of range. There has to be some sort of limit...right?'''      )

						# Get datetime obj for validation
						__SCREAMING_SNAKE_CASE						:     List[str]					=		datetime.date(int(lowercase__      )    ,       int(lowercase__      )    ,       int(lowercase__      )      )

						# Start math
						if m <= 2:
												__SCREAMING_SNAKE_CASE						:     Dict					=		y - 1
												__SCREAMING_SNAKE_CASE						:     List[str]					=		m + 12
						# maths var
						__SCREAMING_SNAKE_CASE						:     int					=		int(str(lowercase__      )[:2]      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(str(lowercase__      )[2:]      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(2.6 * m - 5.39      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(c / 4      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(k / 4      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(d + k      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(t + u + v + x      )
						__SCREAMING_SNAKE_CASE						:     int					=		int(z - (2 * c)      )
						__SCREAMING_SNAKE_CASE						:     int					=		round(w % 7      )
						# End math

						# Validate math
						if f != convert_datetime_days[dt_ck.weekday()]:
												raise AssertionError('''The date was evaluated incorrectly. Contact developer.'''      )

						# Response
						__SCREAMING_SNAKE_CASE						:     str					=		F'''Your date {date_input}, is a {days[str(lowercase__      )]}!'''
						return response


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase	: int  				=argparse.ArgumentParser(
						    description=(
						        'Find out what day of the week nearly any date is or was. Enter '
						        'date as a string in the mm-dd-yyyy or mm/dd/yyyy format'
						    )
						)
						parser.add_argument(
						    'date_input', type=str, help='Date as a string (mm-dd-yyyy or mm/dd/yyyy)'
						)
						__lowerCAmelCase	: int  				=parser.parse_args()
						zeller(args.date_input)




'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

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





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




