

from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class        __SCREAMING_SNAKE_CASE					(     UpperCAmelCase__       ):






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase = None  , ):
														super().__init__()
														self.register_modules(transformer=__lowerCAmelCase  , vae=__lowerCAmelCase  , scheduler=__lowerCAmelCase  )

														# create a imagenet -> id dictionary for easier use
														UpperCamelCase__				     =			{}
														if idalabel is not None:
																					for key, value in idalabel.items():
																												for label in value.split(""","""  ):
																																			UpperCamelCase__				     =			int(__lowerCAmelCase  )
																					UpperCamelCase__				     =			dict(sorted(self.labels.items()  )  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  ):






														if not isinstance(__lowerCAmelCase  , __lowerCAmelCase  ):
																					UpperCamelCase__				     =			list(__lowerCAmelCase  )

														for l in label:
																					if l not in self.labels:
																												raise ValueError(
																												    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""  )

														return [self.labels[l] for l in label]







							@torch.no_grad()
							def __call__(						self  , __lowerCAmelCase  , __lowerCAmelCase = 4.0  , __lowerCAmelCase = None  , __lowerCAmelCase = 50  , __lowerCAmelCase = "pil"  , __lowerCAmelCase = True  , ):
														UpperCamelCase__				     =			len(__lowerCAmelCase  )
														UpperCamelCase__				     =			self.transformer.config.sample_size
														UpperCamelCase__				     =			self.transformer.config.in_channels

														UpperCamelCase__				     =			randn_tensor(
														    shape=(batch_size, latent_channels, latent_size, latent_size)  , generator=__lowerCAmelCase  , device=self.device  , dtype=self.transformer.dtype  , )
														UpperCamelCase__				     =			torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

														UpperCamelCase__				     =			torch.tensor(__lowerCAmelCase  , device=self.device  ).reshape(-1  )
														UpperCamelCase__				     =			torch.tensor([1000] * batch_size  , device=self.device  )
														UpperCamelCase__				     =			torch.cat([class_labels, class_null]  , 0  ) if guidance_scale > 1 else class_labels

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

														for t in self.progress_bar(self.scheduler.timesteps  ):
																					if guidance_scale > 1:
																												UpperCamelCase__				     =			latent_model_input[: len(__lowerCAmelCase  ) // 2]
																												UpperCamelCase__				     =			torch.cat([half, half]  , dim=0  )
																					UpperCamelCase__				     =			self.scheduler.scale_model_input(__lowerCAmelCase  , __lowerCAmelCase  )

																					UpperCamelCase__				     =			t
																					if not torch.is_tensor(__lowerCAmelCase  ):
																												# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																												# This would be a good case for the `match` statement (Python 3.10+)
																												UpperCamelCase__				     =			latent_model_input.device.type == """mps"""
																												if isinstance(__lowerCAmelCase  , __lowerCAmelCase  ):
																																			UpperCamelCase__				     =			torch.floataa if is_mps else torch.floataa
																												else:
																																			UpperCamelCase__				     =			torch.intaa if is_mps else torch.intaa
																												UpperCamelCase__				     =			torch.tensor([timesteps]  , dtype=__lowerCAmelCase  , device=latent_model_input.device  )
																					elif len(timesteps.shape  ) == 0:
																												UpperCamelCase__				     =			timesteps[None].to(latent_model_input.device  )
																					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																					UpperCamelCase__				     =			timesteps.expand(latent_model_input.shape[0]  )
																					# predict noise model_output
																					UpperCamelCase__				     =			self.transformer(
																					    __lowerCAmelCase  , timestep=__lowerCAmelCase  , class_labels=__lowerCAmelCase  ).sample

																					# perform guidance
																					if guidance_scale > 1:
																												UpperCamelCase__     ,     UpperCamelCase__				     =			noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																												UpperCamelCase__     ,     UpperCamelCase__				     =			torch.split(__lowerCAmelCase  , len(__lowerCAmelCase  ) // 2  , dim=0  )

																												UpperCamelCase__				     =			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																												UpperCamelCase__				     =			torch.cat([half_eps, half_eps]  , dim=0  )

																												UpperCamelCase__				     =			torch.cat([eps, rest]  , dim=1  )

																					# learned sigma
																					if self.transformer.config.out_channels // 2 == latent_channels:
																												UpperCamelCase__     ,     UpperCamelCase__				     =			torch.split(__lowerCAmelCase  , __lowerCAmelCase  , dim=1  )
																					else:
																												UpperCamelCase__				     =			noise_pred

																					# compute previous image: x_t -> x_t-1
																					UpperCamelCase__				     =			self.scheduler.step(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ).prev_sample

														if guidance_scale > 1:
																					UpperCamelCase__     ,     UpperCamelCase__				     =			latent_model_input.chunk(2  , dim=0  )
														else:
																					UpperCamelCase__				     =			latent_model_input

														UpperCamelCase__				     =			1 / self.vae.config.scaling_factor * latents
														UpperCamelCase__				     =			self.vae.decode(__lowerCAmelCase  ).sample

														UpperCamelCase__				     =			(samples / 2 + 0.5).clamp(0  , 1  )

														# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
														UpperCamelCase__				     =			samples.cpu().permute(0  , 2  , 3  , 1  ).float().numpy()

														if output_type == "pil":
																					UpperCamelCase__				     =			self.numpy_to_pil(__lowerCAmelCase  )

														if not return_dict:
																					return (samples,)

														return ImagePipelineOutput(images=__lowerCAmelCase  )











import argparse
import datetime





def     _UpperCamelCase     (a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			{
							    """0""": """Sunday""",
							    """1""": """Monday""",
							    """2""": """Tuesday""",
							    """3""": """Wednesday""",
							    """4""": """Thursday""",
							    """5""": """Friday""",
							    """6""": """Saturday""",
							}

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

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

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

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

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

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

							# Get year
							UpperCamelCase__				     =			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
							UpperCamelCase__				     =			datetime.date(int(a__       )   ,			int(a__       )   ,			int(a__       )       )

							# Start math
							if m <= 2:
														UpperCamelCase__				     =			y - 1
														UpperCamelCase__				     =			m + 12
							# maths var
							UpperCamelCase__				     =			int(str(a__       )[:2]       )
							UpperCamelCase__				     =			int(str(a__       )[2:]       )
							UpperCamelCase__				     =			int(2.6 * m - 5.39       )
							UpperCamelCase__				     =			int(c / 4       )
							UpperCamelCase__				     =			int(k / 4       )
							UpperCamelCase__				     =			int(d + k       )
							UpperCamelCase__				     =			int(t + u + v + x       )
							UpperCamelCase__				     =			int(z - (2 * c)       )
							UpperCamelCase__				     =			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
							UpperCamelCase__				     =			f"""Your date {date_input}, is a {days[str(a__       )]}!"""
							return response


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					UpperCamelCase__											= 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)"
					)
					UpperCamelCase__											= parser.parse_args()
					zeller(args.date_input)




