



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 						UpperCAmelCase_					( a):







							def __init__(     self,    __a,    __a,    __a,    __a = None,    ):

														'''simple docstring'''

														super().__init__()
														self.register_modules(transformer=__a,    vae=__a,    scheduler=__a)

														# create a imagenet -> id dictionary for easier use
														_lowerCAmelCase  :							List[str]				     =					{}
														if idalabel is not None:
																					for key, value in idalabel.items():
																												for label in value.split(","):
																																			_lowerCAmelCase  :							Optional[int]				     =					int(__a)
																					_lowerCAmelCase  :							Union[str, Any]				     =					dict(sorted(self.labels.items()))







							def      snake_case__   (     self,    __a):

														'''simple docstring'''

														if not isinstance(__a,    __a):
																					_lowerCAmelCase  :							int				     =					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 = 50,    __a = "pil",    __a = True,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					len(__a)
														_lowerCAmelCase  :							Optional[int]				     =					self.transformer.config.sample_size
														_lowerCAmelCase  :							int				     =					self.transformer.config.in_channels

														_lowerCAmelCase  :							List[str]				     =					randn_tensor(
														    shape=(batch_size, latent_channels, latent_size, latent_size),    generator=__a,    device=self.device,    dtype=self.transformer.dtype,    )
														_lowerCAmelCase  :							Union[str, Any]				     =					torch.cat([latents] * 2) if guidance_scale > 1 else latents

														_lowerCAmelCase  :							List[str]				     =					torch.tensor(__a,    device=self.device).reshape(-1)
														_lowerCAmelCase  :							Optional[int]				     =					torch.tensor([1000] * batch_size,    device=self.device)
														_lowerCAmelCase  :							List[str]				     =					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  :							Any				     =					latent_model_input[: len(__a) // 2]
																												_lowerCAmelCase  :							Optional[Any]				     =					torch.cat([half, half],    dim=0)
																					_lowerCAmelCase  :							Tuple				     =					self.scheduler.scale_model_input(__a,    __a)

																					_lowerCAmelCase  :							str				     =					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  :							List[Any]				     =					latent_model_input.device.type == "mps"
																												if isinstance(__a,    __a):
																																			_lowerCAmelCase  :							str				     =					torch.floataa if is_mps else torch.floataa
																												else:
																																			_lowerCAmelCase  :							List[Any]				     =					torch.intaa if is_mps else torch.intaa
																												_lowerCAmelCase  :							Tuple				     =					torch.tensor([timesteps],    dtype=__a,    device=latent_model_input.device)
																					elif len(timesteps.shape) == 0:
																												_lowerCAmelCase  :							Dict				     =					timesteps[None].to(latent_model_input.device)
																					# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																					_lowerCAmelCase  :							Tuple				     =					timesteps.expand(latent_model_input.shape[0])
																					# predict noise model_output
																					_lowerCAmelCase  :							Union[str, Any]				     =					self.transformer(
																					    __a,    timestep=__a,    class_labels=__a).sample

																					# perform guidance
																					if guidance_scale > 1:
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							Any				     =					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					torch.split(__a,    len(__a) // 2,    dim=0)

																												_lowerCAmelCase  :							Any				     =					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																												_lowerCAmelCase  :							Optional[Any]				     =					torch.cat([half_eps, half_eps],    dim=0)

																												_lowerCAmelCase  :							Optional[int]				     =					torch.cat([eps, rest],    dim=1)

																					# learned sigma
																					if self.transformer.config.out_channels // 2 == latent_channels:
																												_lowerCAmelCase     ,					_lowerCAmelCase  :							Union[str, Any]				     =					torch.split(__a,    __a,    dim=1)
																					else:
																												_lowerCAmelCase  :							Optional[int]				     =					noise_pred

																					# compute previous image: x_t -> x_t-1
																					_lowerCAmelCase  :							Dict				     =					self.scheduler.step(__a,    __a,    __a).prev_sample

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

														_lowerCAmelCase  :							Optional[int]				     =					1 / self.vae.config.scaling_factor * latents
														_lowerCAmelCase  :							Any				     =					self.vae.decode(__a).sample

														_lowerCAmelCase  :							List[Any]				     =					(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  :							Optional[Any]				     =					samples.cpu().permute(0,    2,    3,    1).float().numpy()

														if output_type == "pil":
																					_lowerCAmelCase  :							Optional[int]				     =					self.numpy_to_pil(__a)

														if not return_dict:
																					return (samples,)

														return ImagePipelineOutput(images=__a)





def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations(_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														return sum(count_of_possible_combinations(target - item						) for item in array						)

							return count_of_possible_combinations(_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							def count_of_possible_combinations_with_dp_array(
							    _lowerCamelCase       ,				_lowerCamelCase						) -> int:
														if target < 0:
																					return 0
														if target == 0:
																					return 1
														if dp_array[target] != -1:
																					return dp_array[target]
														_lowerCAmelCase  :							Optional[int]				     =					sum(
														    count_of_possible_combinations_with_dp_array(target - item       ,				_lowerCamelCase						)
														    for item in array						)
														_lowerCAmelCase  :							Any				     =					answer
														return answer

							_lowerCAmelCase  :							List[Any]				     =					[-1] * (target + 1)
							return count_of_possible_combinations_with_dp_array(_lowerCamelCase       ,				_lowerCamelCase						)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[0] * (target + 1)
							_lowerCAmelCase  :							List[str]				     =					1

							for i in range(1       ,				target + 1						):
														for j in range(_lowerCamelCase						):
																					if i - array[j] >= 0:
																												dp_array[i] += dp_array[i - array[j]]

							return dp_array[target]


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						_snake_case	=						3
						_snake_case	=						5
						_snake_case	=						[1, 2, 5]
						print(combination_sum_iv(n, array, target))
