

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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=0.999		,				SCREAMING_SNAKE_CASE="cosine"		,				)							->   Optional[Any]:

     """simple docstring"""







     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

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

     else:
          raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''					)

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





class A__     (			lowercase__	,		lowercase__			):



 """simple docstring"""







 _lowercase  :       List[str]  			=    [e.name for e in KarrasDiffusionSchedulers]
 _lowercase  :       Any  			=    2

 @register_to_config
 def __init__(		self			:						List[str]  ,    A_			:						int = 1_0_0_0  ,    A_			:						float = 0.00085  ,    A_			:						float = 0.012  ,    A_			:						str = "linear"  ,    A_			:						Optional[Union[np.ndarray, List[float]]] = None  ,    A_			:						str = "epsilon"  ,    A_			:						str = "linspace"  ,    A_			:						int = 0  ,    ):






      '''simple docstring'''




      if trained_betas is not None:
           _lowerCAmelCase :       Optional[int]	   =     torch.tensor(A_  ,    dtype=torch.floataa							)
      elif beta_schedule == "linear":
           _lowerCAmelCase :       Optional[Any]	   =     torch.linspace(A_  ,    A_  ,    A_  ,    dtype=torch.floataa							)
      elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowerCAmelCase :       Dict	   =     (
               torch.linspace(beta_start**0.5  ,    beta_end**0.5  ,    A_  ,    dtype=torch.floataa							) ** 2
           )
      elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowerCAmelCase :       List[str]	   =     betas_for_alpha_bar(A_							)
      else:
           raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''							)

      _lowerCAmelCase :       Tuple	   =     1.0 - self.betas
      _lowerCAmelCase :       Tuple	   =     torch.cumprod(self.alphas  ,    dim=0							)

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

 def  __magic_name__							(		self			:						str  ,    A_			:						Optional[Any]  ,    A_			:						Optional[int]=None							):






      '''simple docstring'''




      if schedule_timesteps is None:
           _lowerCAmelCase :       List[Any]	   =     self.timesteps

      _lowerCAmelCase :       str	   =     (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:
           _lowerCAmelCase :       Any	   =     1 if len(A_							) > 1 else 0
      else:
           _lowerCAmelCase :       str	   =     timestep.cpu().item() if torch.is_tensor(A_							) else timestep
           _lowerCAmelCase :       Union[str, Any]	   =     self._index_counter[timestep_int]

      return indices[pos].item()

 @property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




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

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

 def  __magic_name__							(		self			:						Any  ,    A_			:						torch.FloatTensor  ,    A_			:						Union[float, torch.FloatTensor]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     self.index_for_timestep(A_							)

      if self.state_in_first_order:
           _lowerCAmelCase :       int	   =     self.sigmas[step_index]
      else:
           _lowerCAmelCase :       Optional[Any]	   =     self.sigmas_interpol[step_index - 1]

      _lowerCAmelCase :       Any	   =     sample / ((sigma**2 + 1) ** 0.5)
      return sample

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						int  ,    A_			:						Union[str, torch.device] = None  ,    A_			:						Optional[int] = None  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     num_inference_steps

      _lowerCAmelCase :       Any	   =     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":
           _lowerCAmelCase :       Dict	   =     np.linspace(0  ,    num_train_timesteps - 1  ,    A_  ,    dtype=A_							)[::-1].copy()
      elif self.config.timestep_spacing == "leading":
           _lowerCAmelCase :       List[str]	   =     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
           _lowerCAmelCase :       List[Any]	   =     (np.arange(0  ,    A_							) * step_ratio).round()[::-1].copy().astype(A_							)
           timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
           _lowerCAmelCase :       Tuple	   =     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
           _lowerCAmelCase :       Optional[int]	   =     (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\'.'''							)

      _lowerCAmelCase :       Dict	   =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
      _lowerCAmelCase :       Dict	   =     torch.from_numpy(np.log(A_							)							).to(A_							)

      _lowerCAmelCase :       Union[str, Any]	   =     np.interp(A_  ,    np.arange(0  ,    len(A_							)							)  ,    A_							)
      _lowerCAmelCase :       Dict	   =     np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
      _lowerCAmelCase :       Union[str, Any]	   =     torch.from_numpy(A_							).to(device=A_							)

      # compute up and down sigmas
      _lowerCAmelCase :       Tuple	   =     sigmas.roll(-1							)
      _lowerCAmelCase :       Optional[Any]	   =     0.0
      _lowerCAmelCase :       Optional[int]	   =     (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
      _lowerCAmelCase :       Dict	   =     (sigmas_next**2 - sigmas_up**2) ** 0.5
      _lowerCAmelCase :       List[str]	   =     0.0

      # compute interpolated sigmas
      _lowerCAmelCase :       Optional[Any]	   =     sigmas.log().lerp(sigmas_down.log()  ,    0.5							).exp()
      _lowerCAmelCase :       Optional[int]	   =     0.0

      # set sigmas
      _lowerCAmelCase :       str	   =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
      _lowerCAmelCase :       Tuple	   =     torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)
      _lowerCAmelCase :       str	   =     torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2							), sigmas_up[-1:]]							)
      _lowerCAmelCase :       Optional[int]	   =     torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2							), sigmas_down[-1:]]							)

      if str(A_							).startswith("mps"							):
           # mps does not support float64
           _lowerCAmelCase :       Dict	   =     torch.from_numpy(A_							).to(A_  ,    dtype=torch.floataa							)
      else:
           _lowerCAmelCase :       Tuple	   =     torch.from_numpy(A_							).to(A_							)

      _lowerCAmelCase :       Optional[int]	   =     self.sigma_to_t(A_							).to(A_  ,    dtype=timesteps.dtype							)
      _lowerCAmelCase :       Dict	   =     torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])  ,    dim=-1							).flatten()

      _lowerCAmelCase :       Optional[int]	   =     torch.cat([timesteps[:1], interleaved_timesteps]							)

      _lowerCAmelCase :       Optional[int]	   =     None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      _lowerCAmelCase :       int	   =     defaultdict(A_							)

 def  __magic_name__							(		self			:						Optional[int]  ,    A_			:						Union[str, Any]							):






      '''simple docstring'''




      _lowerCAmelCase :       List[Any]	   =     sigma.log()

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

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

      _lowerCAmelCase :       Dict	   =     self.log_sigmas[low_idx]
      _lowerCAmelCase :       List[Any]	   =     self.log_sigmas[high_idx]

      # interpolate sigmas
      _lowerCAmelCase :       int	   =     (low - log_sigma) / (low - high)
      _lowerCAmelCase :       List[Any]	   =     w.clamp(0  ,    1							)

      # transform interpolation to time range
      _lowerCAmelCase :       List[Any]	   =     (1 - w) * low_idx + w * high_idx
      _lowerCAmelCase :       Tuple	   =     t.view(sigma.shape							)
      return t

 @property
 def  __magic_name__							(		self			:						Union[str, Any]							):






      '''simple docstring'''




      return self.sample is None

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						Union[torch.FloatTensor, np.ndarray]  ,    A_			:						Union[float, torch.FloatTensor]  ,    A_			:						Union[torch.FloatTensor, np.ndarray]  ,    A_			:						Optional[torch.Generator] = None  ,    A_			:						bool = True  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       str	   =     self.index_for_timestep(A_							)

      # advance index counter by 1
      _lowerCAmelCase :       Optional[Any]	   =     timestep.cpu().item() if torch.is_tensor(A_							) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
           _lowerCAmelCase :       Optional[int]	   =     self.sigmas[step_index]
           _lowerCAmelCase :       Optional[int]	   =     self.sigmas_interpol[step_index]
           _lowerCAmelCase :       Any	   =     self.sigmas_up[step_index]
           _lowerCAmelCase :       Union[str, Any]	   =     self.sigmas_down[step_index - 1]
      else:
           # 2nd order / KPDM2's method
           _lowerCAmelCase :       Any	   =     self.sigmas[step_index - 1]
           _lowerCAmelCase :       str	   =     self.sigmas_interpol[step_index - 1]
           _lowerCAmelCase :       int	   =     self.sigmas_up[step_index - 1]
           _lowerCAmelCase :       Optional[Any]	   =     self.sigmas_down[step_index - 1]

      # 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
      _lowerCAmelCase :       Dict	   =     0
      _lowerCAmelCase :       str	   =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      _lowerCAmelCase :       Tuple	   =     model_output.device
      _lowerCAmelCase :       int	   =     randn_tensor(model_output.shape  ,    dtype=model_output.dtype  ,    device=A_  ,    generator=A_							)

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
           _lowerCAmelCase :       Tuple	   =     sigma_hat if self.state_in_first_order else sigma_interpol
           _lowerCAmelCase :       List[Any]	   =     sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
           _lowerCAmelCase :       Any	   =     sigma_hat if self.state_in_first_order else sigma_interpol
           _lowerCAmelCase :       List[str]	   =     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
           _lowerCAmelCase :       Union[str, Any]	   =     (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           _lowerCAmelCase :       Any	   =     sigma_interpol - sigma_hat

           # store for 2nd order step
           _lowerCAmelCase :       str	   =     sample
           _lowerCAmelCase :       List[str]	   =     dt
           _lowerCAmelCase :       Tuple	   =     sample + derivative * dt
      else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           _lowerCAmelCase :       List[Any]	   =     (sample - pred_original_sample) / sigma_interpol
           # 3. delta timestep
           _lowerCAmelCase :       List[str]	   =     sigma_down - sigma_hat

           _lowerCAmelCase :       str	   =     self.sample
           _lowerCAmelCase :       Tuple	   =     None

           _lowerCAmelCase :       Any	   =     sample + derivative * dt
           _lowerCAmelCase :       int	   =     prev_sample + noise * sigma_up

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=A_							)

 def  __magic_name__							(		self			:						Tuple  ,    A_			:						torch.FloatTensor  ,    A_			:						torch.FloatTensor  ,    A_			:						torch.FloatTensor  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Optional[Any]	   =     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
           _lowerCAmelCase :       Dict	   =     self.timesteps.to(original_samples.device  ,    dtype=torch.floataa							)
           _lowerCAmelCase :       Dict	   =     timesteps.to(original_samples.device  ,    dtype=torch.floataa							)
      else:
           _lowerCAmelCase :       Tuple	   =     self.timesteps.to(original_samples.device							)
           _lowerCAmelCase :       Dict	   =     timesteps.to(original_samples.device							)

      _lowerCAmelCase :       Tuple	   =     [self.index_for_timestep(A_  ,    A_							) for t in timesteps]

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

      _lowerCAmelCase :       int	   =     original_samples + noise * sigma
      return noisy_samples


 def __len__(		self			:						List[Any]							):






      '''simple docstring'''




      return self.config.num_train_timesteps













from __future__ import annotations

from collections.abc import Iterable
from dataclasses import dataclass
from typing import Any



class  _a  :






							"""simple docstring"""

							snake_case_							=    ("_front", "_back", "_len")



							@dataclass
							class  _a  :






														"""simple docstring"""

														snake_case_							=    None
														snake_case_							=    None
														snake_case_							=    None







							class  _a  :






														"""simple docstring"""

														snake_case_							=    ("_cur",)

														def __init__(   self					:  Tuple				,						a					:  Deque._Node | None     )						->None:
																				SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       cur

														def __iter__(   self					:  Dict     )						->Deque._Iterator:
																				return self






														def   A_	(   self					:  int     )						->Any:
																				if self._cur is None:
																										# finished iterating
																										raise StopIteration
																				SCREAMING_SNAKE_CASE__  :					Tuple =       self._cur.val
																				SCREAMING_SNAKE_CASE__  :					int =       self._cur.next_node

																				return val







							def __init__(   self					:  str				,						a					:  Iterable[Any] | None = None     )						->None:
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					Any =       None
													SCREAMING_SNAKE_CASE__  :					int =       0

													if iterable is not None:
																			# append every value to the deque
																			for val in iterable:
																									self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					int =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       node
																			SCREAMING_SNAKE_CASE__  :					Any =       self._back
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node  # assign new back to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  List[str]				,						a					:  Any     )						->None:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._Node(a				,						a				,						a     )
													if self.is_empty():
																			# front = back
																			SCREAMING_SNAKE_CASE__  :					str =       node
																			SCREAMING_SNAKE_CASE__  :					Dict =       1
													else:
																			# connect nodes
																			SCREAMING_SNAKE_CASE__  :					Any =       self._front
																			SCREAMING_SNAKE_CASE__  :					Tuple =       node
																			SCREAMING_SNAKE_CASE__  :					int =       node  # assign new front to the new node

																			self._len += 1

																			# make sure there were no errors
																			assert not self.is_empty(), "Error on appending value."

							def   A_	(   self					:  Dict				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.append(a     )

							def   A_	(   self					:  Tuple				,						a					:  Iterable[Any]     )						->None:
													for val in iterable:
																			self.appendleft(a     )

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._back
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       self._back.prev_node  # set new back
													# drop the last node - python will deallocate memory automatically
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  Any     )						->Any:
													assert not self.is_empty(), "Deque is empty."

													SCREAMING_SNAKE_CASE__  :					Any =       self._front
													SCREAMING_SNAKE_CASE__  :					Dict =       self._front.next_node  # set new front and drop the first node
													SCREAMING_SNAKE_CASE__  :					Dict =       None

													self._len -= 1

													return topop.val

							def   A_	(   self					:  str     )						->bool:
													return self._front is None

							def __len__(   self					:  List[str]     )						->int:
													return self._len

							def __eq__(   self					:  List[Any]				,						a					:  object     )						->bool:

													if not isinstance(a				,						a     ):
																			return NotImplemented

													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._front
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       other._front

													# if the length of the dequeues are not the same, they are not equal
													if len(self     ) != len(a     ):
																			return False

													while me is not None and oth is not None:
																			# compare every value
																			if me.val != oth.val:
																									return False
																			SCREAMING_SNAKE_CASE__  :					Any =       me.next_node
																			SCREAMING_SNAKE_CASE__  :					Any =       oth.next_node

													return True

							def __iter__(   self					:  Any     )						->Deque._Iterator:
													return Deque._Iterator(self._front     )






							def __repr__(   self					:  Any     )						->str:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
													SCREAMING_SNAKE_CASE__  :					int =       self._front
													while aux is not None:
																			# append the values in a list to display
																			values_list.append(aux.val     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aux.next_node

													return f"""[{', '.join(repr(a     ) for val in values_list     )}]"""


if __name__ == "__main__":
				import doctest

				doctest.testmod()