
'''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__   (  __UpperCAmelCase     ):






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


											"""simple docstring"""





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

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):
																	lowercase_	:     Optional[int]													=       list(__SCREAMING_SNAKE_CASE		)

											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				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = 4.0				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = 50				,							__SCREAMING_SNAKE_CASE = "pil"				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       len(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       self.transformer.config.sample_size
											lowercase_	:     Tuple													=       self.transformer.config.in_channels

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

											lowercase_	:     Union[str, Any]													=       torch.tensor(__SCREAMING_SNAKE_CASE				,							device=self.device		).reshape(-1		)
											lowercase_	:     Any													=       torch.tensor([10_00] * batch_size				,							device=self.device		)
											lowercase_	:     List[str]													=       torch.cat([class_labels, class_null]				,							0		) if guidance_scale > 1 else class_labels

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

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

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

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

																							lowercase_	:     Tuple													=       uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																							lowercase_	:     Union[str, Any]													=       torch.cat([half_eps, half_eps]				,							dim=0		)

																							lowercase_	:     Optional[Any]													=       torch.cat([eps, rest]				,							dim=1		)

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

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

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

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

											lowercase_	:     Optional[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
											lowercase_	:     str													=       samples.cpu().permute(0				,							2				,							3				,							1		).float().numpy()

											if output_type == "pil":
																	lowercase_	:     Any													=       self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (samples,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)










import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


