


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     lowercase	(    a     , a=0.999     , a="cosine"     , ):



		'''simple docstring'''




		if alpha_transform_type == "cosine":

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

		elif alpha_transform_type == "exp":

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

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

		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		for i in range(a ):
				SCREAMING_SNAKE_CASE_				:str				     =						i / num_diffusion_timesteps
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a ) / alpha_bar_fn(a )     , a ) )
		return torch.tensor(a     , dtype=torch.floataa )



class 						_UpperCAmelCase      (     lowercase		,		lowercase							):
		lowerCamelCase_	:			Optional[Any]       			=  [e.name for e in KarrasDiffusionSchedulers]
		lowerCamelCase_	:			int       			=  2






		@register_to_config
		def __init__(						self				:     Optional[int]   ,  UpperCAmelCase				:     int = 10_00   ,  UpperCAmelCase				:     float = 0.00085   ,  UpperCAmelCase				:     float = 0.012   ,  UpperCAmelCase				:     str = "linear"   ,  UpperCAmelCase				:     Optional[Union[np.ndarray, List[float]]] = None   ,  UpperCAmelCase				:     str = "epsilon"   ,  UpperCAmelCase				:     str = "linspace"   ,  UpperCAmelCase				:     int = 0   ,  ):
				if trained_betas is not None:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						torch.tensor(UpperCAmelCase   ,  dtype=torch.floataa)
				elif beta_schedule == "linear":
						SCREAMING_SNAKE_CASE_				:Dict				     =						torch.linspace(UpperCAmelCase   ,  UpperCAmelCase   ,  UpperCAmelCase   ,  dtype=torch.floataa)
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						SCREAMING_SNAKE_CASE_				:str				     =						(
						    torch.linspace(beta_start**0.5   ,  beta_end**0.5   ,  UpperCAmelCase   ,  dtype=torch.floataa) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						SCREAMING_SNAKE_CASE_				:Any				     =						betas_for_alpha_bar(UpperCAmelCase)
				else:
						raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}")

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						1.0 - self.betas
				SCREAMING_SNAKE_CASE_				:Tuple				     =						torch.cumprod(self.alphas   ,  dim=0)

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






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     Dict   ,  UpperCAmelCase				:     Any=None):
				if schedule_timesteps is None:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.timesteps

				SCREAMING_SNAKE_CASE_				:Dict				     =						(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:
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						1 if len(UpperCAmelCase) > 1 else 0
				else:
						SCREAMING_SNAKE_CASE_				:str				     =						timestep.cpu().item() if torch.is_tensor(UpperCAmelCase) else timestep
						SCREAMING_SNAKE_CASE_				:int				     =						self._index_counter[timestep_int]

				return indices[pos].item()






		@property
		def _snake_case		(						self				:     Dict):
				# 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 _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     Union[float, torch.FloatTensor]   ,  ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.index_for_timestep(UpperCAmelCase)

				if self.state_in_first_order:
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.sigmas[step_index]
				else:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.sigmas_interpol[step_index]

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						sample / ((sigma**2 + 1) ** 0.5)
				return sample






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     int   ,  UpperCAmelCase				:     Union[str, torch.device] = None   ,  UpperCAmelCase				:     Optional[int] = None   ,  ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						num_inference_steps

				SCREAMING_SNAKE_CASE_				:Dict				     =						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":
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						np.linspace(0   ,  num_train_timesteps - 1   ,  UpperCAmelCase   ,  dtype=UpperCAmelCase)[::-1].copy()
				elif self.config.timestep_spacing == "leading":
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(np.arange(0   ,  UpperCAmelCase) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase)
						timesteps += self.config.steps_offset
				elif self.config.timestep_spacing == "trailing":
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						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
						SCREAMING_SNAKE_CASE_				:str				     =						(np.arange(UpperCAmelCase   ,  0   ,  -step_ratio)).round().copy().astype(UpperCAmelCase)
						timesteps -= 1
				else:
						raise ValueError(
						    F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'.")

				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						torch.from_numpy(np.log(UpperCAmelCase)).to(UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.interp(UpperCAmelCase   ,  np.arange(0   ,  len(UpperCAmelCase))   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						np.concatenate([sigmas, [0.0]]).astype(np.floataa)
				SCREAMING_SNAKE_CASE_				:int				     =						torch.from_numpy(UpperCAmelCase).to(device=UpperCAmelCase)

				# interpolate sigmas
				SCREAMING_SNAKE_CASE_				:Any				     =						sigmas.log().lerp(sigmas.roll(1).log()   ,  0.5).exp()

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

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

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

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

				SCREAMING_SNAKE_CASE_				:List[str]				     =						None

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






		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     Optional[int]):
				# get log sigma
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						sigma.log()

				# get distribution
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						log_sigma - self.log_sigmas[:, None]

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

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.log_sigmas[low_idx]
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.log_sigmas[high_idx]

				# interpolate sigmas
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(low - log_sigma) / (low - high)
				SCREAMING_SNAKE_CASE_				:Dict				     =						w.clamp(0   ,  1)

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






		@property
		def _snake_case		(						self				:     Union[str, Any]):
				return self.sample is None






		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     Union[torch.FloatTensor, np.ndarray]   ,  UpperCAmelCase				:     Union[float, torch.FloatTensor]   ,  UpperCAmelCase				:     Union[torch.FloatTensor, np.ndarray]   ,  UpperCAmelCase				:     bool = True   ,  ):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						self.index_for_timestep(UpperCAmelCase)

				# advance index counter by 1
				SCREAMING_SNAKE_CASE_				:Dict				     =						timestep.cpu().item() if torch.is_tensor(UpperCAmelCase) else timestep
				self._index_counter[timestep_int] += 1

				if self.state_in_first_order:
						SCREAMING_SNAKE_CASE_				:Tuple				     =						self.sigmas[step_index]
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.sigmas_interpol[step_index + 1]
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.sigmas[step_index + 1]
				else:
						# 2nd order / KDPM2's method
						SCREAMING_SNAKE_CASE_				:str				     =						self.sigmas[step_index - 1]
						SCREAMING_SNAKE_CASE_				:Tuple				     =						self.sigmas_interpol[step_index]
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						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
				SCREAMING_SNAKE_CASE_				:int				     =						0
				SCREAMING_SNAKE_CASE_				:Any				     =						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":
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						sigma_hat if self.state_in_first_order else sigma_interpol
						SCREAMING_SNAKE_CASE_				:List[str]				     =						sample - sigma_input * model_output
				elif self.config.prediction_type == "v_prediction":
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						sigma_hat if self.state_in_first_order else sigma_interpol
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						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
						SCREAMING_SNAKE_CASE_				:Tuple				     =						(sample - pred_original_sample) / sigma_hat
						# 3. delta timestep
						SCREAMING_SNAKE_CASE_				:str				     =						sigma_interpol - sigma_hat

						# store for 2nd order step
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						sample
				else:
						# DPM-Solver-2
						# 2. Convert to an ODE derivative for 2nd order
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(sample - pred_original_sample) / sigma_interpol

						# 3. delta timestep
						SCREAMING_SNAKE_CASE_				:List[str]				     =						sigma_next - sigma_hat

						SCREAMING_SNAKE_CASE_				:List[str]				     =						self.sample
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						None

				SCREAMING_SNAKE_CASE_				:List[str]				     =						sample + derivative * dt

				if not return_dict:
						return (prev_sample,)

				return SchedulerOutput(prev_sample=UpperCAmelCase)






		def _snake_case		(						self				:     Optional[Any]   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     torch.FloatTensor   ,  UpperCAmelCase				:     torch.FloatTensor   ,  ):
				# Make sure sigmas and timesteps have the same device and dtype as original_samples
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.sigmas.to(device=original_samples.device   ,  dtype=original_samples.dtype)
				if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase):
						# mps does not support float64
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self.timesteps.to(original_samples.device   ,  dtype=torch.floataa)
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						timesteps.to(original_samples.device   ,  dtype=torch.floataa)
				else:
						SCREAMING_SNAKE_CASE_				:int				     =						self.timesteps.to(original_samples.device)
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						timesteps.to(original_samples.device)

				SCREAMING_SNAKE_CASE_				:Dict				     =						[self.index_for_timestep(UpperCAmelCase   ,  UpperCAmelCase) for t in timesteps]

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

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						original_samples + noise * sigma
				return noisy_samples







		def __len__(						self				:     List[str]):
				return self.config.num_train_timesteps










from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__     					=			Lock()


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''simple docstring'''




		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0     , 10 ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(a )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:str				     =						rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						SCREAMING_SNAKE_CASE_				:int				     =						min(a     , a )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(a )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:int				     =						lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						SCREAMING_SNAKE_CASE_				:Dict				     =						max(a     , a )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(a )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe() )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		SCREAMING_SNAKE_CASE_				:str				     =						Pipe()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Pipe()
		process_array_.append(
		    Process(
		        target=a     , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     , ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						temp_rs
		SCREAMING_SNAKE_CASE_				:Any				     =						temp_rr

		for i in range(1     , len(a ) - 1 ):
				SCREAMING_SNAKE_CASE_				:int				     =						Pipe()
				SCREAMING_SNAKE_CASE_				:Dict				     =						Pipe()
				process_array_.append(
				    Process(
				        target=a     , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     , ) )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp_rs
				SCREAMING_SNAKE_CASE_				:int				     =						temp_rr

		process_array_.append(
		    Process(
		        target=a     , args=(
		            len(a ) - 1,
		            arr[len(a ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(a ) - 1],
		        )     , ) )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0     , len(a ) ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						result_pipe[p][0].recv()
				process_array_[p].join()
		return arr


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						list(range(10     , 0     , -1 ) )
		print("Initial List" )
		print(*a )
		SCREAMING_SNAKE_CASE_				:int				     =						odd_even_transposition(a )
		print("Sorted List\n" )
		print(*a )


if __name__ == "__main__":
		main()






