
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 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase=0.999			,						__lowerCAmelCase="cosine"			,						):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

    snake_case__  = []
    for i in range(__lowerCAmelCase							):
        snake_case__  = i / num_diffusion_timesteps
        snake_case__  = (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     _SCREAMING_SNAKE_CASE     (					a__				,    a__       ):
      _A  :						List[Any]					=   [e.name for e in KarrasDiffusionSchedulers]
      _A  :						Optional[Any]					=   2
      @register_to_config
      def __init__(	self						,		lowerCamelCase = 10_00						,		lowerCamelCase = 0.0_0_0_8_5						,		lowerCamelCase = 0.0_1_2						,		lowerCamelCase = "linear"						,		lowerCamelCase = None						,		lowerCamelCase = "epsilon"						,		lowerCamelCase = "linspace"						,		lowerCamelCase = 0						,		):
          if trained_betas is not None:
              snake_case__  = torch.tensor(lowerCamelCase_						,		dtype=torch.floataa       )
          elif beta_schedule == "linear":
              snake_case__  = torch.linspace(lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_						,		dtype=torch.floataa       )
          elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              snake_case__  = (
                  torch.linspace(beta_start**0.5						,		beta_end**0.5						,		lowerCamelCase_						,		dtype=torch.floataa       ) ** 2
              )
          elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              snake_case__  = betas_for_alpha_bar(lowerCamelCase_       )
          else:
              raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""       )

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

          #  set all values
          self.set_timesteps(lowerCamelCase_						,		lowerCamelCase_						,		lowerCamelCase_       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase=None       ):
          if schedule_timesteps is None:
              snake_case__  = self.timesteps

          snake_case__  = (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:
              snake_case__  = 1 if len(lowerCamelCase_       ) > 1 else 0
          else:
              snake_case__  = timestep.cpu().item() if torch.is_tensor(lowerCamelCase_       ) else timestep
              snake_case__  = self._index_counter[timestep_int]

          return indices[pos].item()
      @property
      def A_	(	self       ):
          # standard deviation of the initial noise distribution
          if self.config.timestep_spacing in ["linspace", "trailing"]:
              return self.sigmas.max()

          return (self.sigmas.max() ** 2 + 1) ** 0.5
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		):
          snake_case__  = self.index_for_timestep(lowerCamelCase_       )

          if self.state_in_first_order:
              snake_case__  = self.sigmas[step_index]
          else:
              snake_case__  = self.sigmas_interpol[step_index - 1]

          snake_case__  = sample / ((sigma**2 + 1) ** 0.5)
          return sample
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = None						,		):
          snake_case__  = num_inference_steps

          snake_case__  = 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":
              snake_case__  = np.linspace(0						,		num_train_timesteps - 1						,		lowerCamelCase_						,		dtype=lowerCamelCase_       )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
              snake_case__  = 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
              snake_case__  = (np.arange(0						,		lowerCamelCase_       ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_       )
              timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
              snake_case__  = 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
              snake_case__  = (np.arange(lowerCamelCase_						,		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'."""       )

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

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

          # compute up and down sigmas
          snake_case__  = sigmas.roll(-1       )
          snake_case__  = 0.0
          snake_case__  = (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
          snake_case__  = (sigmas_next**2 - sigmas_up**2) ** 0.5
          snake_case__  = 0.0

          # compute interpolated sigmas
          snake_case__  = sigmas.log().lerp(sigmas_down.log()						,		0.5       ).exp()
          snake_case__  = 0.0

          # set sigmas
          snake_case__  = torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2       ), sigmas[-1:]]       )
          snake_case__  = torch.cat(
              [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2       ), sigmas_interpol[-1:]]       )
          snake_case__  = torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2       ), sigmas_up[-1:]]       )
          snake_case__  = torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2       ), sigmas_down[-1:]]       )

          if str(lowerCamelCase_       ).startswith("mps"       ):
              # mps does not support float64
              snake_case__  = torch.from_numpy(lowerCamelCase_       ).to(lowerCamelCase_						,		dtype=torch.floataa       )
          else:
              snake_case__  = torch.from_numpy(lowerCamelCase_       ).to(lowerCamelCase_       )

          snake_case__  = self.sigma_to_t(lowerCamelCase_       ).to(lowerCamelCase_						,		dtype=timesteps.dtype       )
          snake_case__  = torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,		dim=-1       ).flatten()

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

          snake_case__  = None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          snake_case__  = defaultdict(lowerCamelCase_       )
      def A_	(	self						,		lowerCamelCase       ):
          # get log sigma
          snake_case__  = sigma.log()

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

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

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

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

          # transform interpolation to time range
          snake_case__  = (1 - w) * low_idx + w * high_idx
          snake_case__  = t.view(sigma.shape       )
          return t
      @property
      def A_	(	self       ):
          return self.sample is None
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase = None						,		lowerCamelCase = True						,		):
          snake_case__  = self.index_for_timestep(lowerCamelCase_       )

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

          if self.state_in_first_order:
              snake_case__  = self.sigmas[step_index]
              snake_case__  = self.sigmas_interpol[step_index]
              snake_case__  = self.sigmas_up[step_index]
              snake_case__  = self.sigmas_down[step_index - 1]
          else:
              # 2nd order / KPDM2's method
              snake_case__  = self.sigmas[step_index - 1]
              snake_case__  = self.sigmas_interpol[step_index - 1]
              snake_case__  = self.sigmas_up[step_index - 1]
              snake_case__  = 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
          snake_case__  = 0
          snake_case__  = sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

          snake_case__  = model_output.device
          snake_case__  = randn_tensor(model_output.shape						,		dtype=model_output.dtype						,		device=lowerCamelCase_						,		generator=lowerCamelCase_       )

          # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
          if self.config.prediction_type == "epsilon":
              snake_case__  = sigma_hat if self.state_in_first_order else sigma_interpol
              snake_case__  = sample - sigma_input * model_output
          elif self.config.prediction_type == "v_prediction":
              snake_case__  = sigma_hat if self.state_in_first_order else sigma_interpol
              snake_case__  = 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
              snake_case__  = (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              snake_case__  = sigma_interpol - sigma_hat

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

              snake_case__  = self.sample
              snake_case__  = None

              snake_case__  = sample + derivative * dt
              snake_case__  = prev_sample + noise * sigma_up

          if not return_dict:
              return (prev_sample,)

          return SchedulerOutput(prev_sample=lowerCamelCase_       )
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase						,		):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          snake_case__  = 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
              snake_case__  = self.timesteps.to(original_samples.device						,		dtype=torch.floataa       )
              snake_case__  = timesteps.to(original_samples.device						,		dtype=torch.floataa       )
          else:
              snake_case__  = self.timesteps.to(original_samples.device       )
              snake_case__  = timesteps.to(original_samples.device       )

          snake_case__  = [self.index_for_timestep(lowerCamelCase_						,		lowerCamelCase_       ) for t in timesteps]

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

          snake_case__  = original_samples + noise * sigma
          return noisy_samples



      def __len__(	self       ):
          return self.config.num_train_timesteps







'''simple docstring'''






from __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

__UpperCAmelCase    				=							TypeVar('''T''')
__UpperCAmelCase    				=							TypeVar('''U''')




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							Union[str, Any]:
														lowerCAmelCase__          =	key
														lowerCAmelCase__          =	val
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None







							def __repr__(		self   )       ->							str:
														return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
														    self.key					,    self.val					,    self.freq					,    self.next is not None					,    self.prev is not None   )




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							def __init__(		self   )       ->							None:
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
														lowerCAmelCase__   ,					lowerCAmelCase__          =	self.rear, self.head







							def __repr__(		self   )       ->							str:
														lowerCAmelCase__          =	['''DoubleLinkedList''']
														lowerCAmelCase__          =	self.head
														while node.next is not None:
																					rep.append(str(lowerCamelCase_   )   )
																					lowerCAmelCase__          =	node.next
														rep.append(str(self.rear   )   )
														return ",\n    ".join(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:
														lowerCAmelCase__          =	self.rear.prev

														# All nodes other than self.head are guaranteed to have non-None previous
														assert previous is not None

														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	previous
														lowerCAmelCase__          =	node
														lowerCAmelCase__          =	self.rear
														node.freq += 1
														self._position_node(lowerCamelCase_   )







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							None:

														while node.prev is not None and node.prev.freq > node.freq:
																					# swap node with previous node
																					lowerCAmelCase__          =	node.prev

																					lowerCAmelCase__          =	previous_node.prev
																					lowerCAmelCase__          =	node.prev
																					lowerCAmelCase__          =	previous_node
																					lowerCAmelCase__          =	node







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							DoubleLinkedListNode[T, U] | None:

														if node.prev is None or node.next is None:
																					return None

														lowerCAmelCase__          =	node.next
														lowerCAmelCase__          =	node.prev
														lowerCAmelCase__          =	None
														lowerCAmelCase__          =	None
														return node




class 		a__				(							Generic[T, U]  ):





							'''simple docstring'''






							lowercase__			:   dict[Callable[[T], U], LFUCache[T, U]]							=      {}







							def __init__(		self					,    lowerCamelCase_   )       ->							Any:
														lowerCAmelCase__          =	DoubleLinkedList()
														lowerCAmelCase__          =	capacity
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	0
														lowerCAmelCase__          =	{}







							def __repr__(		self   )       ->							str:
														return (
														    F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
														    F"""capacity={self.capacity}, current_size={self.num_keys})"""
														)







							def __contains__(		self					,    lowerCamelCase_   )       ->							bool:
														return key in self.cache







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_   )       ->							U | None:

														if key in self.cache:
																					self.hits += 1
																					lowerCAmelCase__          =	self.cache[key]
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node == value_node

																					# node is guaranteed not None because it is in self.cache
																					assert node is not None
																					self.list.add(lowerCamelCase_   )
																					return node.val
														self.miss += 1
														return None







							def   __SCREAMING_SNAKE_CASE							(		self					,    lowerCamelCase_					,    lowerCamelCase_   )       ->							None:

														if key not in self.cache:
																					if self.num_keys >= self.capacity:
																												# delete first node when over capacity
																												lowerCAmelCase__          =	self.list.head.next

																												# guaranteed to have a non-None first node when num_keys > 0
																												# explain to type checker via assertions
																												assert first_node is not None
																												assert first_node.key is not None
																												assert self.list.remove(lowerCamelCase_   ) is not None
																												# first_node guaranteed to be in list

																												del self.cache[first_node.key]
																												self.num_keys -= 1
																					lowerCAmelCase__          =	DoubleLinkedListNode(lowerCamelCase_					,    lowerCamelCase_   )
																					self.list.add(self.cache[key]   )
																					self.num_keys += 1

														else:
																					lowerCAmelCase__          =	self.list.remove(self.cache[key]   )
																					assert node is not None  # node guaranteed to be in list
																					lowerCAmelCase__          =	value
																					self.list.add(lowerCamelCase_   )







							@classmethod
							def   __SCREAMING_SNAKE_CASE							(		cls					,    lowerCamelCase_ = 1_28   )       ->							Callable[[Callable[[T], U]], Callable[..., U]]:

														def cache_decorator_inner(lowerCamelCase_   ) -> Callable[..., U]:
																					def cache_decorator_wrapper(*lowerCamelCase_   ) -> U:
																												if func not in cls.decorator_function_to_instance_map:
																																			lowerCAmelCase__          =	LFUCache(lowerCamelCase_   )

																												lowerCAmelCase__          =	cls.decorator_function_to_instance_map[func].get(args[0]   )
																												if result is None:
																																			lowerCAmelCase__          =	func(*lowerCamelCase_   )
																																			cls.decorator_function_to_instance_map[func].put(args[0]					,    lowerCamelCase_   )
																												return result

																					def cache_info() -> LFUCache[T, U]:
																												return cls.decorator_function_to_instance_map[func]

																					setattr(lowerCamelCase_					,    '''cache_info'''					,    lowerCamelCase_   )  # noqa: B010

																					return cache_decorator_wrapper

														return cache_decorator_inner


if __name__ == "__main__":
		import doctest

		doctest.testmod()