



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							A	(     _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   List[Any]=0.999							,   _UpperCAmelCase				:   Tuple="cosine"							,   )				-> Optional[Any]:







			'''simple docstring'''
			if alpha_transform_type == "cosine":

						def alpha_bar_fn(_UpperCAmelCase				:   List[Any]    ):
									return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

			elif alpha_transform_type == "exp":

						def alpha_bar_fn(_UpperCAmelCase				:   List[Any]    ):
									return math.exp(t * -12.0    )

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

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







class 			__lowerCAmelCase    (			__lowerCamelCase				,						__lowerCamelCase						):
			UpperCamelCase   					=     [e.name for e in KarrasDiffusionSchedulers]
			UpperCamelCase   					=     2



			@register_to_config
			def __init__(						self   :  Any ,		A   :  Optional[Any] = 10_00 ,		A   :  Union[str, Any] = 0.0_0_0_8_5 ,		A   :  Union[str, Any] = 0.0_1_2 ,		A   :  Optional[int] = "linear" ,		A   :  Dict = None ,		A   :  Tuple = "epsilon" ,		A   :  List[Any] = "linspace" ,		A   :  str = 0 ,		)   ->    Tuple:



						"""simple docstring"""






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

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

						#  set all values
						self.set_timesteps(A ,		A ,		A)



			def     _lowerCamelCase       (						self   :  str ,		A   :  int ,		A   :  str=None)   ->    str:



						"""simple docstring"""






						if schedule_timesteps is None:
									_UpperCAmelCase		       =   self.timesteps

						_UpperCAmelCase		       =   (schedule_timesteps == timestep).nonzero()

						# The sigma index that is taken for the **very** first `step`
						# is always the second index (or the last index if there is only 1)
						# This way we can ensure we don't accidentally skip a sigma in
						# case we start in the middle of the denoising schedule (e.g. for image-to-image)
						if len(self._index_counter) == 0:
									_UpperCAmelCase		       =   1 if len(A) > 1 else 0
						else:
									_UpperCAmelCase		       =   timestep.cpu().item() if torch.is_tensor(A) else timestep
									_UpperCAmelCase		       =   self._index_counter[timestep_int]

						return indices[pos].item()



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



						"""simple docstring"""






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

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



			def     _lowerCamelCase       (						self   :  Any ,		A   :  List[str] ,		A   :  List[Any] ,		)   ->    torch.FloatTensor:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.index_for_timestep(A)

						if self.state_in_first_order:
									_UpperCAmelCase		       =   self.sigmas[step_index]
						else:
									_UpperCAmelCase		       =   self.sigmas_interpol[step_index]

						_UpperCAmelCase		       =   sample / ((sigma**2 + 1) ** 0.5)
						return sample



			def     _lowerCamelCase       (						self   :  Dict ,		A   :  str ,		A   :  Union[str, Any] = None ,		A   :  Tuple = None ,		)   ->    str:



						"""simple docstring"""






						_UpperCAmelCase		       =   num_inference_steps

						_UpperCAmelCase		       =   num_train_timesteps or self.config.num_train_timesteps

						# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
						if self.config.timestep_spacing == "linspace":
									_UpperCAmelCase		       =   np.linspace(0 ,		num_train_timesteps - 1 ,		A ,		dtype=A)[::-1].copy()
						elif self.config.timestep_spacing == "leading":
									_UpperCAmelCase		       =   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
									_UpperCAmelCase		       =   (np.arange(0 ,		A) * step_ratio).round()[::-1].copy().astype(A)
									timesteps += self.config.steps_offset
						elif self.config.timestep_spacing == "trailing":
									_UpperCAmelCase		       =   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
									_UpperCAmelCase		       =   (np.arange(A ,		0 ,		-step_ratio)).round().copy().astype(A)
									timesteps -= 1
						else:
									raise ValueError(
									    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")

						_UpperCAmelCase		       =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
						_UpperCAmelCase		       =   torch.from_numpy(np.log(A)).to(A)

						_UpperCAmelCase		       =   np.interp(A ,		np.arange(0 ,		len(A)) ,		A)
						_UpperCAmelCase		       =   np.concatenate([sigmas, [0.0]]).astype(np.floataa)
						_UpperCAmelCase		       =   torch.from_numpy(A).to(device=A)

						# interpolate sigmas
						_UpperCAmelCase		       =   sigmas.log().lerp(sigmas.roll(1).log() ,		0.5).exp()

						_UpperCAmelCase		       =   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
						_UpperCAmelCase		       =   torch.cat(
						    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])

						if str(A).startswith('mps'):
									# mps does not support float64
									_UpperCAmelCase		       =   torch.from_numpy(A).to(A ,		dtype=torch.floataa)
						else:
									_UpperCAmelCase		       =   torch.from_numpy(A).to(A)

						# interpolate timesteps
						_UpperCAmelCase		       =   self.sigma_to_t(A).to(A ,		dtype=timesteps.dtype)
						_UpperCAmelCase		       =   torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]) ,		dim=-1).flatten()

						_UpperCAmelCase		       =   torch.cat([timesteps[:1], interleaved_timesteps])

						_UpperCAmelCase		       =   None

						# for exp beta schedules, such as the one for `pipeline_shap_e.py`
						# we need an index counter
						_UpperCAmelCase		       =   defaultdict(A)



			def     _lowerCamelCase       (						self   :  Optional[int] ,		A   :  List[str])   ->    Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   sigma.log()

						# get distribution
						_UpperCAmelCase		       =   log_sigma - self.log_sigmas[:, None]

						# get sigmas range
						_UpperCAmelCase		       =   dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
						_UpperCAmelCase		       =   low_idx + 1

						_UpperCAmelCase		       =   self.log_sigmas[low_idx]
						_UpperCAmelCase		       =   self.log_sigmas[high_idx]

						# interpolate sigmas
						_UpperCAmelCase		       =   (low - log_sigma) / (low - high)
						_UpperCAmelCase		       =   w.clamp(0 ,		1)

						# transform interpolation to time range
						_UpperCAmelCase		       =   (1 - w) * low_idx + w * high_idx
						_UpperCAmelCase		       =   t.view(sigma.shape)
						return t



			@property
			def     _lowerCamelCase       (						self   :  List[str])   ->    Union[str, Any]:



						"""simple docstring"""






						return self.sample is None



			def     _lowerCamelCase       (						self   :  Any ,		A   :  str ,		A   :  Union[str, Any] ,		A   :  Optional[Any] ,		A   :  List[Any] = True ,		)   ->    Union[SchedulerOutput, Tuple]:



						"""simple docstring"""






						_UpperCAmelCase		       =   self.index_for_timestep(A)

						# advance index counter by 1
						_UpperCAmelCase		       =   timestep.cpu().item() if torch.is_tensor(A) else timestep
						self._index_counter[timestep_int] += 1

						if self.state_in_first_order:
									_UpperCAmelCase		       =   self.sigmas[step_index]
									_UpperCAmelCase		       =   self.sigmas_interpol[step_index + 1]
									_UpperCAmelCase		       =   self.sigmas[step_index + 1]
						else:
									# 2nd order / KDPM2's method
									_UpperCAmelCase		       =   self.sigmas[step_index - 1]
									_UpperCAmelCase		       =   self.sigmas_interpol[step_index]
									_UpperCAmelCase		       =   self.sigmas[step_index]

						# currently only gamma=0 is supported. This usually works best anyways.
						# We can support gamma in the future but then need to scale the timestep before
						# passing it to the model which requires a change in API
						_UpperCAmelCase		       =   0
						_UpperCAmelCase		       =   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
									_UpperCAmelCase		       =   sigma_hat if self.state_in_first_order else sigma_interpol
									_UpperCAmelCase		       =   sample - sigma_input * model_output
						elif self.config.prediction_type == "v_prediction":
									_UpperCAmelCase		       =   sigma_hat if self.state_in_first_order else sigma_interpol
									_UpperCAmelCase		       =   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
									    sample / (sigma_input**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`")

						if self.state_in_first_order:
									# 2. Convert to an ODE derivative for 1st order
									_UpperCAmelCase		       =   (sample - pred_original_sample) / sigma_hat
									# 3. delta timestep
									_UpperCAmelCase		       =   sigma_interpol - sigma_hat

									# store for 2nd order step
									_UpperCAmelCase		       =   sample
						else:
									# DPM-Solver-2
									# 2. Convert to an ODE derivative for 2nd order
									_UpperCAmelCase		       =   (sample - pred_original_sample) / sigma_interpol

									# 3. delta timestep
									_UpperCAmelCase		       =   sigma_next - sigma_hat

									_UpperCAmelCase		       =   self.sample
									_UpperCAmelCase		       =   None

						_UpperCAmelCase		       =   sample + derivative * dt

						if not return_dict:
									return (prev_sample,)

						return SchedulerOutput(prev_sample=A)



			def     _lowerCamelCase       (						self   :  Union[str, Any] ,		A   :  int ,		A   :  Any ,		A   :  str ,		)   ->    torch.FloatTensor:



						"""simple docstring"""






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

						_UpperCAmelCase		       =   [self.index_for_timestep(A ,		A) for t in timesteps]

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

						_UpperCAmelCase		       =   original_samples + noise * sigma
						return noisy_samples



			def __len__(						self   :  Tuple)   ->    Any:



						"""simple docstring"""






						return self.config.num_train_timesteps







UpperCAmelCase_     							= 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_     							= 'TRWAGMYFPDXBNJZSQVHLCKE'




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	bool:

    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				Any          =      f"Expected string as input, found {type(__UpperCAmelCase      ).__name__}"
        raise TypeError(__UpperCAmelCase      )

    UpperCamelCase__							:				int          =      spanish_id.replace('''-'''		,   ''''''      ).upper()
    if len(__UpperCAmelCase      ) != 9:
        raise ValueError(__UpperCAmelCase      )

    try:
        UpperCamelCase__							:				List[str]          =      int(spanish_id_clean[0:8]      )
        UpperCamelCase__							:				Optional[int]          =      spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(__UpperCAmelCase      ) from ex

    if letter.isdigit():
        raise ValueError(__UpperCAmelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

