


'''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       (						lowerCAmelCase__							):



		'''simple docstring'''







		def __init__(					self		:						Any  ,     _A		:						TransformeraDModel  ,     _A		:						AutoencoderKL  ,     _A		:						KarrasDiffusionSchedulers  ,     _A		:						Optional[Dict[int, str]] = None  ,     )  -> Optional[Any]:
			super().__init__()
			self.register_modules(transformer=lowercase_  ,     vae=lowercase_  ,     scheduler=lowercase_			)

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







		def    __lowerCAmelCase		(					self		:						List[Any]  ,     _A		:						Union[str, List[str]]			)  -> Optional[Any]:

			if not isinstance(lowercase_  ,     lowercase_			):
				__magic_name__       :    Tuple     	=     list(lowercase_			)

			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		:						Optional[int]  ,     _A		:						List[int]  ,     _A		:						float = 4.0  ,     _A		:						Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,     _A		:						int = 50  ,     _A		:						Optional[str] = "pil"  ,     _A		:						bool = True  ,     )  -> List[str]:
			__magic_name__       :    Any     	=     len(lowercase_			)
			__magic_name__       :    List[str]     	=     self.transformer.config.sample_size
			__magic_name__       :    Union[str, Any]     	=     self.transformer.config.in_channels

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

			__magic_name__       :    Optional[int]     	=     torch.tensor(lowercase_  ,     device=self.device			).reshape(-1			)
			__magic_name__       :    Dict     	=     torch.tensor([1000] * batch_size  ,     device=self.device			)
			__magic_name__       :    Tuple     	=     torch.cat([class_labels, class_null]  ,     0			) if guidance_scale > 1 else class_labels

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

			for t in self.progress_bar(self.scheduler.timesteps			):
				if guidance_scale > 1:
					__magic_name__       :    List[Any]     	=     latent_model_input[: len(lowercase_			) // 2]
					__magic_name__       :    Union[str, Any]     	=     torch.cat([half, half]  ,     dim=0			)
				__magic_name__       :    Optional[Any]     	=     self.scheduler.scale_model_input(lowercase_  ,     lowercase_			)

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

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

					__magic_name__       :    int     	=     uncond_eps + guidance_scale * (cond_eps - uncond_eps)
					__magic_name__       :    str     	=     torch.cat([half_eps, half_eps]  ,     dim=0			)

					__magic_name__       :    List[Any]     	=     torch.cat([eps, rest]  ,     dim=1			)

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

				# compute previous image: x_t -> x_t-1
				__magic_name__       :    int     	=     self.scheduler.step(lowercase_  ,     lowercase_  ,     lowercase_			).prev_sample

			if guidance_scale > 1:
				__magic_name__       :    Optional[Any]     	=     latent_model_input.chunk(2  ,     dim=0			)
			else:
				__magic_name__       :    Dict     	=     latent_model_input

			__magic_name__       :    Union[str, Any]     	=     1 / self.vae.config.scaling_factor * latents
			__magic_name__       :    Tuple     	=     self.vae.decode(lowercase_			).sample

			__magic_name__       :    str     	=     (samples / 2 + 0.5).clamp(0  ,     1			)

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

			if output_type == "pil":
				__magic_name__       :    Union[str, Any]     	=     self.numpy_to_pil(lowercase_			)

			if not return_dict:
				return (samples,)

			return ImagePipelineOutput(images=lowercase_			)




"""simple docstring"""

from __future__ import annotations




def __lowercase			(							_a		,			_a		,			_a		,			):
    if (stress, tangential_force, area).count(0    ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''    )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''    )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''    )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''    )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
     import doctest

     doctest.testmod()





