





import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE_      :			List[str]		  =      logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		42
	__UpperCamelCase        =		None





def    SCREAMING_SNAKE_CASE							(			snake_case	,      snake_case=0.999	,      snake_case="cosine"	,      )		->						List[str]:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(snake_case       ):
						return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(snake_case       ):
						return math.exp(t * -12.0       )

		else:
				raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"       )

		__lowercase					=    []
		for i in range(snake_case       ):
				__lowercase					=    i / num_diffusion_timesteps
				__lowercase					=    (i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(snake_case       ) / alpha_bar_fn(snake_case       )	,      snake_case       )       )
		return torch.tensor(snake_case	,      dtype=torch.floataa       )


class 		snake_case_      (       UpperCAmelCase_    ,    UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		[e.name for e in KarrasDiffusionSchedulers]
	__UpperCamelCase        =		1




	@register_to_config
	def __init__(	self       :    Optional[Any]				,			__lowerCamelCase       :    int = 1_000				,			__lowerCamelCase       :    float = 0.0001				,			__lowerCamelCase       :    float = 0.02				,			__lowerCamelCase       :    str = "linear"				,			__lowerCamelCase       :    Optional[Union[np.ndarray, List[float]]] = None				,			__lowerCamelCase       :    str = "epsilon"				,			__lowerCamelCase       :    str = "linspace"				,			__lowerCamelCase       :    int = 0				,			)					->   Union[str, Any]:

			'''simple docstring'''




			if trained_betas is not None:
					__lowercase					=    torch.tensor(__lowerCamelCase				,			dtype=torch.floataa      )
			elif beta_schedule == "linear":
					__lowercase					=    torch.linspace(__lowerCamelCase				,			__lowerCamelCase				,			__lowerCamelCase				,			dtype=torch.floataa      )
			elif beta_schedule == "scaled_linear":
					# this schedule is very specific to the latent diffusion model.
					__lowercase					=    (
					    torch.linspace(beta_start**0.5				,			beta_end**0.5				,			__lowerCamelCase				,			dtype=torch.floataa      ) ** 2
					)
			elif beta_schedule == "squaredcos_cap_v2":
					# Glide cosine schedule
					__lowercase					=    betas_for_alpha_bar(__lowerCamelCase      )
			else:
					raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"      )

			__lowercase					=    1.0 - self.betas
			__lowercase					=    torch.cumprod(self.alphas				,			dim=0      )

			__lowercase					=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
			__lowercase					=    np.concatenate([sigmas[::-1], [0.0]]      ).astype(np.floataa      )
			__lowercase					=    torch.from_numpy(__lowerCamelCase      )

			# setable values
			__lowercase					=    None
			__lowercase					=    np.linspace(0				,			num_train_timesteps - 1				,			__lowerCamelCase				,			dtype=__lowerCamelCase      )[::-1].copy()
			__lowercase					=    torch.from_numpy(__lowerCamelCase      )
			__lowercase					=    False




	@property
	def       UpperCAmelCase (	self       :    Optional[int]      )					->   Any:

			'''simple docstring'''




			if self.config.timestep_spacing in ["linspace", "trailing"]:
					return self.sigmas.max()

			return (self.sigmas.max() ** 2 + 1) ** 0.5




	def       UpperCAmelCase (	self       :    Tuple				,			__lowerCamelCase       :    torch.FloatTensor				,			__lowerCamelCase       :    Union[float, torch.FloatTensor]      )					->   torch.FloatTensor:

			'''simple docstring'''




			if isinstance(__lowerCamelCase				,			torch.Tensor      ):
					__lowercase					=    timestep.to(self.timesteps.device      )
			__lowercase					=    (self.timesteps == timestep).nonzero().item()
			__lowercase					=    self.sigmas[step_index]
			__lowercase					=    sample / ((sigma**2 + 1) ** 0.5)
			__lowercase					=    True
			return sample




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    int				,			__lowerCamelCase       :    Union[str, torch.device] = None      )					->   str:

			'''simple docstring'''




			__lowercase					=    num_inference_steps

			# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
			if self.config.timestep_spacing == "linspace":
					__lowercase					=    np.linspace(0				,			self.config.num_train_timesteps - 1				,			__lowerCamelCase				,			dtype=__lowerCamelCase      )[
					    ::-1
					].copy()
			elif self.config.timestep_spacing == "leading":
					__lowercase					=    self.config.num_train_timesteps // self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					__lowercase					=    (np.arange(0				,			__lowerCamelCase      ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase      )
					timesteps += self.config.steps_offset
			elif self.config.timestep_spacing == "trailing":
					__lowercase					=    self.config.num_train_timesteps / self.num_inference_steps
					# creates integer timesteps by multiplying by ratio
					# casting to int to avoid issues when num_inference_step is power of 3
					__lowercase					=    (np.arange(self.config.num_train_timesteps				,			0				,			-step_ratio      )).round().copy().astype(__lowerCamelCase      )
					timesteps -= 1
			else:
					raise ValueError(
					    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."      )

			__lowercase					=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
			__lowercase					=    np.interp(__lowerCamelCase				,			np.arange(0				,			len(__lowerCamelCase      )      )				,			__lowerCamelCase      )
			__lowercase					=    np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
			__lowercase					=    torch.from_numpy(__lowerCamelCase      ).to(device=__lowerCamelCase      )
			if str(__lowerCamelCase      ).startswith('mps'      ):
					# mps does not support float64
					__lowercase					=    torch.from_numpy(__lowerCamelCase      ).to(__lowerCamelCase				,			dtype=torch.floataa      )
			else:
					__lowercase					=    torch.from_numpy(__lowerCamelCase      ).to(device=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    str				,			__lowerCamelCase       :    torch.FloatTensor				,			__lowerCamelCase       :    Union[float, torch.FloatTensor]				,			__lowerCamelCase       :    torch.FloatTensor				,			__lowerCamelCase       :    Optional[torch.Generator] = None				,			__lowerCamelCase       :    bool = True				,			)					->   Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

			'''simple docstring'''




			if (
			    isinstance(__lowerCamelCase				,			__lowerCamelCase      )
			    or isinstance(__lowerCamelCase				,			torch.IntTensor      )
			    or isinstance(__lowerCamelCase				,			torch.LongTensor      )
			):
					raise ValueError(
					    (
					        'Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'
					        ' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'
					        ' one of the `scheduler.timesteps` as a timestep.'
					    )				,			)

			if not self.is_scale_input_called:
					logger.warning(
					    'The `scale_model_input` function should be called before `step` to ensure correct denoising. '
					    'See `StableDiffusionPipeline` for a usage example.'      )

			if isinstance(__lowerCamelCase				,			torch.Tensor      ):
					__lowercase					=    timestep.to(self.timesteps.device      )

			__lowercase					=    (self.timesteps == timestep).nonzero().item()
			__lowercase					=    self.sigmas[step_index]

			# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
			if self.config.prediction_type == "epsilon":
					__lowercase					=    sample - sigma * model_output
			elif self.config.prediction_type == "v_prediction":
					# * c_out + input * c_skip
					__lowercase					=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
			elif self.config.prediction_type == "sample":
					raise NotImplementedError('prediction_type not implemented yet: sample'      )
			else:
					raise ValueError(
					    F"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"      )

			__lowercase					=    self.sigmas[step_index]
			__lowercase					=    self.sigmas[step_index + 1]
			__lowercase					=    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
			__lowercase					=    (sigma_to**2 - sigma_up**2) ** 0.5

			# 2. Convert to an ODE derivative
			__lowercase					=    (sample - pred_original_sample) / sigma

			__lowercase					=    sigma_down - sigma

			__lowercase					=    sample + derivative * dt

			__lowercase					=    model_output.device
			__lowercase					=    randn_tensor(model_output.shape				,			dtype=model_output.dtype				,			device=__lowerCamelCase				,			generator=__lowerCamelCase      )

			__lowercase					=    prev_sample + noise * sigma_up

			if not return_dict:
					return (prev_sample,)

			return EulerAncestralDiscreteSchedulerOutput(
			    prev_sample=__lowerCamelCase				,			pred_original_sample=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    List[str]				,			__lowerCamelCase       :    torch.FloatTensor				,			__lowerCamelCase       :    torch.FloatTensor				,			__lowerCamelCase       :    torch.FloatTensor				,			)					->   torch.FloatTensor:

			'''simple docstring'''




			__lowercase					=    self.sigmas.to(device=original_samples.device				,			dtype=original_samples.dtype      )
			if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase      ):
					# mps does not support float64
					__lowercase					=    self.timesteps.to(original_samples.device				,			dtype=torch.floataa      )
					__lowercase					=    timesteps.to(original_samples.device				,			dtype=torch.floataa      )
			else:
					__lowercase					=    self.timesteps.to(original_samples.device      )
					__lowercase					=    timesteps.to(original_samples.device      )

			__lowercase					=    [(schedule_timesteps == t).nonzero().item() for t in timesteps]

			__lowercase					=    sigmas[step_indices].flatten()
			while len(sigma.shape      ) < len(original_samples.shape      ):
					__lowercase					=    sigma.unsqueeze(-1      )

			__lowercase					=    original_samples + noise * sigma
			return noisy_samples






	def __len__(	self       :    Optional[Any]      )					->   Any:

			'''simple docstring'''




			return self.config.num_train_timesteps











import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		snake_case_      (       UpperCAmelCase_	):







	'''simple docstring'''
	__UpperCamelCase        =		(VQDiffusionScheduler,)




	def       UpperCAmelCase (	self       :    int				,			**__lowerCamelCase       :    List[Any]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    {
			    'num_vec_classes': 4_097,
			    'num_train_timesteps': 100,
			}

			config.update(**__lowerCamelCase      )
			return config




	def       UpperCAmelCase (	self       :    List[Any]				,			__lowerCamelCase       :    List[str]      )					->   Tuple:

			'''simple docstring'''




			__lowercase					=    4
			__lowercase					=    8
			__lowercase					=    8

			__lowercase					=    torch.randint(0				,			__lowerCamelCase				,			(batch_size, height * width)      )

			return sample




	@property
	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			assert False




	def       UpperCAmelCase (	self       :    Union[str, Any]				,			__lowerCamelCase       :    str      )					->   Dict:

			'''simple docstring'''




			def model(__lowerCamelCase       :    Dict				,			__lowerCamelCase       :    Optional[Any]				,			*__lowerCamelCase       :    List[Any]      ):
					__lowercase	,			__lowercase					=    sample.shape
					__lowercase					=    torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)      )
					__lowercase					=    F.log_softmax(logits.double()				,			dim=1      ).float()
					return return_value

			return model




	def       UpperCAmelCase (	self       :    Dict      )					->   List[Any]:

			'''simple docstring'''




			for timesteps in [2, 5, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Optional[Any]      )					->   int:

			'''simple docstring'''




			for num_vec_classes in [5, 100, 1_000, 4_000]:
					self.check_over_configs(num_vec_classes=__lowerCamelCase      )




	def       UpperCAmelCase (	self       :    Any      )					->   List[Any]:

			'''simple docstring'''




			for t in [0, 50, 99]:
					self.check_over_forward(time_step=__lowerCamelCase      )






	def       UpperCAmelCase (	self       :    str      )					->   Optional[int]:

			'''simple docstring'''




			pass




