






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			(		lowercase_      ):



				"""simple docstring"""



				def __init__(      self    :    List[Any]			,		UpperCamelCase__    :    TransformeraDModel			,		UpperCamelCase__    :    AutoencoderKL			,		UpperCamelCase__    :    KarrasDiffusionSchedulers			,		UpperCamelCase__    :    Optional[Dict[int, str]] = None			,		):






											'''simple docstring'''







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

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



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    Union[str, List[str]]):






											'''simple docstring'''







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

											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[Any]			,		UpperCamelCase__    :    List[int]			,		UpperCamelCase__    :    float = 4.0			,		UpperCamelCase__    :    Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		UpperCamelCase__    :    int = 5_0			,		UpperCamelCase__    :    Optional[str] = "pil"			,		UpperCamelCase__    :    bool = True			,		):






											'''simple docstring'''







											snake_case__	  =       len(UpperCamelCase__)
											snake_case__	  =       self.transformer.config.sample_size
											snake_case__	  =       self.transformer.config.in_channels

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

											if not return_dict:
																		return (samples,)

											return ImagePipelineOutput(images=UpperCamelCase__)








from collections.abc import Callable

def _UpperCAmelCase   (	a						:	Callable[[float], float]  ,					a						:	float  ,					a						:	float ):
							snake_case__	  =       a
							snake_case__	  =       b
							if function(a ) == 0:  # one of the a or b is a root for the function
														return a
							elif function(a ) == 0:
														return b
							elif (
							    function(a ) * function(a ) > 0
							):  # if none of these are root and they are both positive or negative,
														# then this algorithm can't find the root
														raise ValueError("""could not find root in given interval.""" )
							else:
														snake_case__	  =       start + (end - start) / 2.0
														while abs(start - mid ) > 10**-7:  # until precisely equals to 10^-7
																					if function(a ) == 0:
																												return mid
																					elif function(a ) * function(a ) < 0:
																												snake_case__	  =       mid
																					else:
																												snake_case__	  =       mid
																					snake_case__	  =       start + (end - start) / 2.0
														return mid

def _UpperCAmelCase   (	a						:	float ):
							return x**3 - 2 * x - 5


if __name__ == "__main__":
	print(bisection(f, 1, 1_0_0_0))

	import doctest

	doctest.testmod()
