





"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							42
					UpperCamelCase      =							None







def 					A__				(			UpperCamelCase					,  UpperCamelCase=0.9_99					,  UpperCamelCase="cosine"					,  ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(UpperCamelCase       ):
																					return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2       ) ** 2

							elif alpha_transform_type == "exp":

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

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

							A		=  []
							for i in range(UpperCamelCase       ):
														A		=  i / num_diffusion_timesteps
														A		=  (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 					_UpperCAmelCase   (	lowercase_     ,							lowercase_ ):
					UpperCamelCase      =							1


					@register_to_config
					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:int = 10_00     ,       __UpperCamelCase			:float = 0.0_001     ,       __UpperCamelCase			:float = 0.02     ,       __UpperCamelCase			:str = "linear"     ,       __UpperCamelCase			:Optional[Union[np.ndarray, List[float]]] = None     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:bool = True     ,       __UpperCamelCase			:int = 0     ,       __UpperCamelCase			:str = "epsilon"     ,       __UpperCamelCase			:float = 1.0     ,       **__UpperCamelCase			:List[Any]     ,       ):
												if kwargs.get("set_alpha_to_one"     ,       __UpperCamelCase		) is not None:
																			A		=  (
																			    "The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."
																			)
																			deprecate("set_alpha_to_one"     ,       "1.0.0"     ,       __UpperCamelCase     ,       standard_warn=__UpperCamelCase		)
																			A		=  kwargs["set_alpha_to_one"]
												if trained_betas is not None:
																			A		=  torch.tensor(__UpperCamelCase     ,       dtype=torch.floataa		)
												elif beta_schedule == "linear":
																			A		=  torch.linspace(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       dtype=torch.floataa		)
												elif beta_schedule == "scaled_linear":
																			# this schedule is very specific to the latent diffusion model.
																			A		=  (
																			    torch.linspace(beta_start**0.5     ,       beta_end**0.5     ,       __UpperCamelCase     ,       dtype=torch.floataa		) ** 2
																			)
												elif beta_schedule == "squaredcos_cap_v2":
																			# Glide cosine schedule
																			A		=  betas_for_alpha_bar(__UpperCamelCase		)
												else:
																			raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"		)

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

												# At every step in inverted ddim, we are looking into the next alphas_cumprod
												# For the final step, there is no next alphas_cumprod, and the index is out of bounds
												# `set_alpha_to_zero` decides whether we set this parameter simply to zero
												# in this case, self.step() just output the predicted noise
												# or whether we use the final alpha of the "non-previous" one.
												A		=  torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

												# standard deviation of the initial noise distribution
												A		=  1.0

												# setable values
												A		=  None
												A		=  torch.from_numpy(np.arange(0     ,       __UpperCamelCase		).copy().astype(np.intaa		)		)


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:Optional[int] = None		):
												return sample


					def 	lowerCamelCase       (					self			:List[str]     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:Union[str, torch.device] = None		):

												if num_inference_steps > self.config.num_train_timesteps:
																			raise ValueError(
																			    f"`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:"
																			    f" {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle"
																			    f" maximal {self.config.num_train_timesteps} timesteps."		)

												A		=  num_inference_steps
												A		=  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
												A		=  (np.arange(0     ,       __UpperCamelCase		) * step_ratio).round().copy().astype(np.intaa		)
												A		=  torch.from_numpy(__UpperCamelCase		).to(__UpperCamelCase		)
												self.timesteps += self.config.steps_offset


					def 	lowerCamelCase       (					self			:Union[str, Any]     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:torch.FloatTensor     ,       __UpperCamelCase			:float = 0.0     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[torch.FloatTensor] = None     ,       __UpperCamelCase			:bool = True     ,       ):
												# 1. get previous step value (=t+1)
												A		=  timestep + self.config.num_train_timesteps // self.num_inference_steps

												# 2. compute alphas, betas
												# change original implementation to exactly match noise levels for analogous forward process
												A		=  self.alphas_cumprod[timestep]
												A		=  (
												    self.alphas_cumprod[prev_timestep]
												    if prev_timestep < self.config.num_train_timesteps
												    else self.final_alpha_cumprod
												)

												A		=  1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																			A		=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																			A		=  model_output
												elif self.config.prediction_type == "sample":
																			A		=  model_output
																			A		=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																			A		=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																			A		=  (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																			raise ValueError(
																			    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"
																			    " `v_prediction`"		)

												# 4. Clip or threshold "predicted x_0"
												if self.config.clip_sample:
																			A		=  pred_original_sample.clamp(
																			    -self.config.clip_sample_range     ,       self.config.clip_sample_range		)

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												A		=  (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												A		=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																			return (prev_sample, pred_original_sample)
												return DDIMSchedulerOutput(prev_sample=__UpperCamelCase     ,       pred_original_sample=__UpperCamelCase		)


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









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case     :		Union[str, Any]       						= {
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case     :		int       						= [
		    'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'EncodecModel',
		    'EncodecPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_encodec import (
		    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    EncodecConfig,
		)
		from .feature_extraction_encodec import EncodecFeatureExtractor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_encodec import (
				    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EncodecModel,
				    EncodecPreTrainedModel,
				)

else:
		import sys

		_snake_case     :		str       						= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


