






"""simple docstring"""

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 		__lowerCamelCase				( a__			):





		'''simple docstring'''






		def __init__(   self							:	Optional[Any]						,			a_							:	str						,			a_							:	Tuple						,			a_							:	Dict						,			a_							:	Optional[Any] = None						,			):
				super().__init__()
				self.register_modules(transformer=lowerCAmelCase__						,			vae=lowerCAmelCase__						,			scheduler=lowerCAmelCase__	)

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





		def    lowerCamelCase       (   self							:	Tuple						,			a_							:	List[str]	):

				if not isinstance(lowerCAmelCase__						,			lowerCAmelCase__	):
						lowerCAmelCase_   :							Optional[Any]									=			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							:	List[str]						,			a_							:	Union[str, Any]						,			a_							:	Optional[int] = 4.0						,			a_							:	List[Any] = None						,			a_							:	str = 50						,			a_							:	Any = "pil"						,			a_							:	Any = True						,			):
				lowerCAmelCase_   :							Dict									=			len(lowerCAmelCase__	)
				lowerCAmelCase_   :							Tuple									=			self.transformer.config.sample_size
				lowerCAmelCase_   :							Tuple									=			self.transformer.config.in_channels

				lowerCAmelCase_   :							Optional[int]									=			randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)						,			generator=lowerCAmelCase__						,			device=self.device						,			dtype=self.transformer.dtype						,			)
				lowerCAmelCase_   :							Tuple									=			torch.cat([latents] * 2	) if guidance_scale > 1 else latents

				lowerCAmelCase_   :							List[str]									=			torch.tensor(lowerCAmelCase__						,			device=self.device	).reshape(-1	)
				lowerCAmelCase_   :							str									=			torch.tensor([10_00] * batch_size						,			device=self.device	)
				lowerCAmelCase_   :							Dict									=			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:
								lowerCAmelCase_   :							Any									=			latent_model_input[: len(lowerCAmelCase__	) // 2]
								lowerCAmelCase_   :							Tuple									=			torch.cat([half, half]						,			dim=0	)
						lowerCAmelCase_   :							List[str]									=			self.scheduler.scale_model_input(lowerCAmelCase__						,			lowerCAmelCase__	)

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

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

								lowerCAmelCase_   :							Dict									=			uncond_eps + guidance_scale * (cond_eps - uncond_eps)
								lowerCAmelCase_   :							Optional[int]									=			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_   :							str									=			torch.split(lowerCAmelCase__						,			lowerCAmelCase__						,			dim=1	)
						else:
								lowerCAmelCase_   :							Any									=			noise_pred

						# compute previous image: x_t -> x_t-1
						lowerCAmelCase_   :							Optional[int]									=			self.scheduler.step(lowerCAmelCase__						,			lowerCAmelCase__						,			lowerCAmelCase__	).prev_sample

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

				lowerCAmelCase_   :							Optional[Any]									=			1 / self.vae.config.scaling_factor * latents
				lowerCAmelCase_   :							Any									=			self.vae.decode(lowerCAmelCase__	).sample

				lowerCAmelCase_   :							Tuple									=			(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_   :							int									=			self.numpy_to_pil(lowerCAmelCase__	)

				if not return_dict:
						return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase__	)







"""simple docstring"""




from __future__ import annotations






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    list[int]    ,       _lowercase						:    int					) ->int:






	'''simple docstring'''






	if len(_lowercase					) < k or k < 0:
		raise ValueError("Invalid Input"					)
	a      :		Optional[Any]	=				sum(array[:k]					)
	for i in range(len(_lowercase					) - k					):
		a      :		Optional[Any]	=				current_sum - array[i] + array[i + k]
		a      :		Union[str, Any]	=				max(_lowercase    ,       _lowercase					)
	return max_sum


if __name__ == "__main__":
		from doctest import testmod
		from random import randint

		testmod()
		a						: Any     	=			[randint(-1000, 1000) for i in range(100)]
		a						: List[str]     	=			randint(0, 110)
		print(F'''The maximum sum of {k} consecutive elements is {max_sum_in_array(array,k)}''')


