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      __lowercase					(    _A    ):

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


									'''simple docstring'''

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

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

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Union[str, List[str]]   )							->		List[int]:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												lowercase							=		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[Any]							,							__lowerCamelCase				:      List[int]							,							__lowerCamelCase				:      float = 4.0							,							__lowerCamelCase				:      Optional[Union[torch.Generator, List[torch.Generator]]] = None							,							__lowerCamelCase				:      int = 50							,							__lowerCamelCase				:      Optional[str] = "pil"							,							__lowerCamelCase				:      bool = True							,							)							->		Union[ImagePipelineOutput, Tuple]:


									'''simple docstring'''

									lowercase							=		len(__lowerCamelCase   )
									lowercase							=		self.transformer.config.sample_size
									lowercase							=		self.transformer.config.in_channels

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

									lowercase							=		torch.tensor(__lowerCamelCase							,							device=self.device   ).reshape(-1   )
									lowercase							=		torch.tensor([10_00] * batch_size							,							device=self.device   )
									lowercase							=		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:
															lowercase							=		latent_model_input[: len(__lowerCamelCase   ) // 2]
															lowercase							=		torch.cat([half, half]							,							dim=0   )
												lowercase							=		self.scheduler.scale_model_input(__lowerCamelCase							,							__lowerCamelCase   )

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

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

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

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

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

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

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

									lowercase							=		1 / self.vae.config.scaling_factor * latents
									lowercase							=		self.vae.decode(__lowerCamelCase   ).sample

									lowercase							=		(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							=		samples.cpu().permute(0							,							2							,							3							,							1   ).float().numpy()

									if output_type == "pil":
												lowercase							=		self.numpy_to_pil(__lowerCamelCase   )

									if not return_dict:
												return (samples,)

									return ImagePipelineOutput(images=__lowerCamelCase   )






from __future__ import annotations

from collections.abc import MutableSequence






class      __lowercase					:

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      MutableSequence[float]   )							->		None:


									'''simple docstring'''

									if len(__lowerCamelCase   ) != degree + 1:
												raise ValueError(
												    '''The number of coefficients should be equal to the degree + 1.'''   )

									lowercase							=		list(__lowerCamelCase   )
									lowercase							=		degree

						def __add__(	self				:      Any							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									if self.degree > polynomial_a.degree:
												lowercase							=		self.coefficients[:]
												for i in range(polynomial_a.degree + 1   ):
															coefficients[i] += polynomial_a.coefficients[i]
												return Polynomial(self.degree							,							__lowerCamelCase   )
									else:
												lowercase							=		polynomial_a.coefficients[:]
												for i in range(self.degree + 1   ):
															coefficients[i] += self.coefficients[i]
												return Polynomial(polynomial_a.degree							,							__lowerCamelCase   )

						def __sub__(	self				:      str							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									return self + polynomial_a * Polynomial(0							,							[-1]   )

						def __neg__(	self				:      str   )							->		Polynomial:


									'''simple docstring'''

									return Polynomial(self.degree							,							[-c for c in self.coefficients]   )

						def __mul__(	self				:      List[str]							,							__lowerCamelCase				:      Polynomial   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + polynomial_a.degree + 1)
									for i in range(self.degree + 1   ):
												for j in range(polynomial_a.degree + 1   ):
															coefficients[i + j] += (
															    self.coefficients[i] * polynomial_a.coefficients[j]
															)

									return Polynomial(self.degree + polynomial_a.degree							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      int | float   )							->		int | float:


									'''simple docstring'''

									lowercase							=		0
									for i in range(self.degree + 1   ):
												result += self.coefficients[i] * (substitution**i)
									return result

						def __str__(	self				:      str   )							->		str:


									'''simple docstring'''

									lowercase							=		''''''
									for i in range(self.degree							,							-1							,							-1   ):
												if self.coefficients[i] == 0:
															continue
												elif self.coefficients[i] > 0:
															if polynomial:
																		polynomial += " + "
												else:
															polynomial += " - "

												if i == 0:
															polynomial += str(abs(self.coefficients[i]   )   )
												elif i == 1:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x"
												else:
															polynomial += str(abs(self.coefficients[i]   )   ) + "x^" + str(__lowerCamelCase   )

									return polynomial

						def __repr__(	self				:      Tuple   )							->		str:


									'''simple docstring'''

									return self.__str__()

						def        __a		(	self				:      Union[str, Any]   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * self.degree
									for i in range(self.degree   ):
												lowercase							=		self.coefficients[i + 1] * (i + 1)
									return Polynomial(self.degree - 1							,							__lowerCamelCase   )

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      int | float = 0   )							->		Polynomial:


									'''simple docstring'''

									lowercase							=		[0] * (self.degree + 2)
									lowercase							=		constant
									for i in range(self.degree + 1   ):
												lowercase							=		self.coefficients[i] / (i + 1)
									return Polynomial(self.degree + 1							,							__lowerCamelCase   )

						def __eq__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									if not isinstance(__lowerCamelCase							,							__lowerCamelCase   ):
												return False

									if self.degree != polynomial_a.degree:
												return False

									for i in range(self.degree + 1   ):
												if self.coefficients[i] != polynomial_a.coefficients[i]:
															return False

									return True



						def __ne__(	self				:      Tuple							,							__lowerCamelCase				:      object   )							->		bool:


									'''simple docstring'''

									return not self.__eq__(__lowerCamelCase   )





