

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     snake_case_			(				_a							):


							"""simple docstring"""





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

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




							def        A__  ( self					,						_A							):

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

												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					,						_A					,						_A = 4.0					,						_A = None					,						_A = 5_0					,						_A = "pil"					,						_A = True					,						):
												__lowerCAmelCase					=							len(_A							)
												__lowerCAmelCase					=							self.transformer.config.sample_size
												__lowerCAmelCase					=							self.transformer.config.in_channels

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

												__lowerCAmelCase					=							torch.tensor(_A					,						device=self.device							).reshape(-1							)
												__lowerCAmelCase					=							torch.tensor([1_0_0_0] * batch_size					,						device=self.device							)
												__lowerCAmelCase					=							torch.cat([class_labels, class_null]					,						0							) if guidance_scale > 1 else class_labels

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

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

																	__lowerCAmelCase					=							t
																	if not torch.is_tensor(_A							):
																						# 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+)
																						__lowerCAmelCase					=							latent_model_input.device.type == 'mps'
																						if isinstance(_A					,						_A							):
																											__lowerCAmelCase					=							torch.floataa if is_mps else torch.floataa
																						else:
																											__lowerCAmelCase					=							torch.intaa if is_mps else torch.intaa
																						__lowerCAmelCase					=							torch.tensor([timesteps]					,						dtype=_A					,						device=latent_model_input.device							)
																	elif len(timesteps.shape							) == 0:
																						__lowerCAmelCase					=							timesteps[None].to(latent_model_input.device							)
																	# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																	__lowerCAmelCase					=							timesteps.expand(latent_model_input.shape[0]							)
																	# predict noise model_output
																	__lowerCAmelCase					=							self.transformer(
																	    _A					,						timestep=_A					,						class_labels=_A							).sample

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

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

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

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

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

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

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

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

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

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

												if not return_dict:
																	return (samples,)

												return ImagePipelineOutput(images=_A							)









def        __lowercase    (  UpperCAmelCase__ = 10		,					UpperCAmelCase__ = 1_000		,					UpperCAmelCase__ = True      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					    and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), "Invalid type of value(s) specified to function!"

					if min_val > max_val:
										raise ValueError('Invalid value for min_val or max_val (min_value < max_value)'      )
					return min_val if option else max_val


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					return int((number_a + number_a) / 2      )


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					assert (
					    isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      ) and isinstance(UpperCAmelCase__		,					UpperCAmelCase__      )
					), 'argument values must be type of "int"'

					if lower > higher:
										raise ValueError('argument value for lower and higher must be(lower > higher)'      )

					if not lower < to_guess < higher:
										raise ValueError(
										    'guess value must be within the range of lower and higher value'      )

					def answer(UpperCAmelCase__      ) -> str:
										if number > to_guess:
															return "high"
										elif number < to_guess:
															return "low"
										else:
															return "same"

					print('started...'      )

					__lowerCAmelCase					=							lower
					__lowerCAmelCase					=							higher

					__lowerCAmelCase					=							[]

					while True:
										__lowerCAmelCase					=							get_avg(UpperCAmelCase__		,					UpperCAmelCase__      )
										last_numbers.append(UpperCAmelCase__      )

										if answer(UpperCAmelCase__      ) == "low":
															__lowerCAmelCase					=							number
										elif answer(UpperCAmelCase__      ) == "high":
															__lowerCAmelCase					=							number
										else:
															break

					print(F"""guess the number : {last_numbers[-1]}"""      )
					print(F"""details : {last_numbers!s}"""      )


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							int(input('Enter lower value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter high value : '      ).strip()      )
					__lowerCAmelCase					=							int(input('Enter value to guess : '      ).strip()      )
					guess_the_number(UpperCAmelCase__		,					UpperCAmelCase__		,					UpperCAmelCase__      )


if __name__ == "__main__":
		main()






