




import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


SCREAMING_SNAKE_CASE__	:				Optional[Any]  =       logging.get_logger(__name__)  # pylint: disable=invalid-name




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     torch.FloatTensor
    lowerCamelCase_				:     Optional[torch.FloatTensor]     					=				None




def 					A				(				_SCREAMING_SNAKE_CASE			,_SCREAMING_SNAKE_CASE=0.999			,_SCREAMING_SNAKE_CASE="cosine"			,)     ->			Any:
       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       :				List[str]      			= []
       for i in range(_SCREAMING_SNAKE_CASE   ):
              lowerCamelCase       :				List[Any]      			= i / num_diffusion_timesteps
              lowerCamelCase       :				List[Any]      			= (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE   ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE   )			,_SCREAMING_SNAKE_CASE   )   )
       return torch.tensor(_SCREAMING_SNAKE_CASE			,dtype=torch.floataa   )




class 		UpperCamelCase__     (lowerCAmelCase__   ,    lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     Dict     					=				[e.name for e in KarrasDiffusionSchedulers]
    lowerCamelCase_				:     List[Any]     					=				1







    @register_to_config
    def __init__(   self					,  UpperCamelCase__ = 1000					,  UpperCamelCase__ = 0.0001					,  UpperCamelCase__ = 0.02					,  UpperCamelCase__ = "linear"					,  UpperCamelCase__ = None					,  UpperCamelCase__ = "epsilon"					,  UpperCamelCase__ = "linspace"					,  UpperCamelCase__ = 0					,  )					->				List[Any]:
           if trained_betas is not None:
                  lowerCamelCase       :				Any      			= torch.tensor(UpperCamelCase__					,  dtype=torch.floataa					)
           elif beta_schedule == "linear":
                  lowerCamelCase       :				Any      			= torch.linspace(UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  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					,  UpperCamelCase__					,  dtype=torch.floataa					) ** 2
                  )
           elif beta_schedule == "squaredcos_cap_v2":
                  # Glide cosine schedule
                  lowerCamelCase       :				Any      			= betas_for_alpha_bar(UpperCamelCase__					)
           else:
                  raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''					)

           lowerCamelCase       :				List[str]      			= 1.0 - self.betas
           lowerCamelCase       :				Optional[int]      			= torch.cumprod(self.alphas					,  dim=0					)

           lowerCamelCase       :				List[str]      			= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           lowerCamelCase       :				Optional[int]      			= np.concatenate([sigmas[::-1], [0.0]]					).astype(np.floataa					)
           lowerCamelCase       :				Tuple      			= torch.from_numpy(UpperCamelCase__					)

           # setable values
           lowerCamelCase       :				Optional[int]      			= None
           lowerCamelCase       :				Tuple      			= np.linspace(0					,  num_train_timesteps - 1					,  UpperCamelCase__					,  dtype=UpperCamelCase__					)[::-1].copy()
           lowerCamelCase       :				List[Any]      			= torch.from_numpy(UpperCamelCase__					)
           lowerCamelCase       :				Optional[int]      			= False







    @property
    def    _lowercase					(   self					)					->				int:
           # 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    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					)					->				torch.FloatTensor:
           if isinstance(UpperCamelCase__					,  torch.Tensor					):
                  lowerCamelCase       :				Optional[Any]      			= timestep.to(self.timesteps.device					)
           lowerCamelCase       :				List[Any]      			= (self.timesteps == timestep).nonzero().item()
           lowerCamelCase       :				List[Any]      			= self.sigmas[step_index]
           lowerCamelCase       :				int      			= sample / ((sigma**2 + 1) ** 0.5)
           lowerCamelCase       :				Tuple      			= True
           return sample







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__ = None					)					->				Optional[Any]:
           lowerCamelCase       :				int      			= num_inference_steps

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

           lowerCamelCase       :				str      			= np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
           lowerCamelCase       :				Optional[int]      			= np.interp(UpperCamelCase__					,  np.arange(0					,  len(UpperCamelCase__					)					)					,  UpperCamelCase__					)
           lowerCamelCase       :				str      			= np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
           lowerCamelCase       :				str      			= torch.from_numpy(UpperCamelCase__					).to(device=UpperCamelCase__					)
           if str(UpperCamelCase__					).startswith("mps"					):
                  # mps does not support float64
                  lowerCamelCase       :				Any      			= torch.from_numpy(UpperCamelCase__					).to(UpperCamelCase__					,  dtype=torch.floataa					)
           else:
                  lowerCamelCase       :				int      			= torch.from_numpy(UpperCamelCase__					).to(device=UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__ = None					,  UpperCamelCase__ = True					,  )					->				Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

           if (
               isinstance(UpperCamelCase__					,  UpperCamelCase__					)
               or isinstance(UpperCamelCase__					,  torch.IntTensor					)
               or isinstance(UpperCamelCase__					,  torch.LongTensor					)
           ):
                  raise ValueError(
                      (
                          "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                          " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
                          " one of the `scheduler.timesteps` as a timestep."
                      )					,  )

           if not self.is_scale_input_called:
                  logger.warning(
                      "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
                      "See `StableDiffusionPipeline` for a usage example."					)

           if isinstance(UpperCamelCase__					,  torch.Tensor					):
                  lowerCamelCase       :				str      			= timestep.to(self.timesteps.device					)

           lowerCamelCase       :				Optional[int]      			= (self.timesteps == timestep).nonzero().item()
           lowerCamelCase       :				int      			= self.sigmas[step_index]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                  lowerCamelCase       :				Any      			= sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                  # * c_out + input * c_skip
                  lowerCamelCase       :				List[Any]      			= model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**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`'''					)

           lowerCamelCase       :				int      			= self.sigmas[step_index]
           lowerCamelCase       :				Union[str, Any]      			= self.sigmas[step_index + 1]
           lowerCamelCase       :				List[Any]      			= (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
           lowerCamelCase       :				str      			= (sigma_to**2 - sigma_up**2) ** 0.5

           # 2. Convert to an ODE derivative
           lowerCamelCase       :				Any      			= (sample - pred_original_sample) / sigma

           lowerCamelCase       :				int      			= sigma_down - sigma

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

           lowerCamelCase       :				int      			= model_output.device
           lowerCamelCase       :				Tuple      			= randn_tensor(model_output.shape					,  dtype=model_output.dtype					,  device=UpperCamelCase__					,  generator=UpperCamelCase__					)

           lowerCamelCase       :				Any      			= prev_sample + noise * sigma_up

           if not return_dict:
                  return (prev_sample,)

           return EulerAncestralDiscreteSchedulerOutput(
               prev_sample=UpperCamelCase__					,  pred_original_sample=UpperCamelCase__					)







    def    _lowercase					(   self					,  UpperCamelCase__					,  UpperCamelCase__					,  UpperCamelCase__					,  )					->				torch.FloatTensor:
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           lowerCamelCase       :				Dict      			= 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
                  lowerCamelCase       :				int      			= self.timesteps.to(original_samples.device					,  dtype=torch.floataa					)
                  lowerCamelCase       :				List[Any]      			= timesteps.to(original_samples.device					,  dtype=torch.floataa					)
           else:
                  lowerCamelCase       :				int      			= self.timesteps.to(original_samples.device					)
                  lowerCamelCase       :				List[Any]      			= timesteps.to(original_samples.device					)

           lowerCamelCase       :				str      			= [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

           lowerCamelCase       :				Optional[Any]      			= original_samples + noise * sigma
           return noisy_samples







    def __len__(   self					)					->				str:
           return self.config.num_train_timesteps












import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


SCREAMING_SNAKE_CASE__	:				int  =       logging.get_logger(__name__)




class 		UpperCamelCase__     (lowerCAmelCase__					):

    '''simple docstring'''



    lowerCamelCase_				:     str     					=				"""speech-encoder-decoder"""
    lowerCamelCase_				:     List[str]     					=				True







    def __init__(   self					,  **UpperCamelCase__					)					->				Dict:
           super().__init__(**UpperCamelCase__					)
           if "encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError(
                      F'''A configuraton of type {self.model_type} cannot be instantiated because not both `encoder` and'''
                      F''' `decoder` sub-configurations are passed, but only {kwargs}'''					)

           lowerCamelCase       :				Dict      			= kwargs.pop("encoder"					)
           lowerCamelCase       :				Tuple      			= encoder_config.pop("model_type"					)
           lowerCamelCase       :				int      			= kwargs.pop("decoder"					)
           lowerCamelCase       :				List[Any]      			= decoder_config.pop("model_type"					)

           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				str      			= AutoConfig.for_model(UpperCamelCase__					,  **UpperCamelCase__					)
           lowerCamelCase       :				Tuple      			= True







    @classmethod
    def    _lowercase					(   cls					,  UpperCamelCase__					,  UpperCamelCase__					,  **UpperCamelCase__					)					->				PretrainedConfig:
           logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"					)
           lowerCamelCase       :				Dict      			= True
           lowerCamelCase       :				Union[str, Any]      			= True

           return cls(encoder=encoder_config.to_dict()					,  decoder=decoder_config.to_dict()					,  **UpperCamelCase__					)







    def    _lowercase					(   self					)					->				Tuple:
           lowerCamelCase       :				Tuple      			= copy.deepcopy(self.__dict__					)
           lowerCamelCase       :				Optional[int]      			= self.encoder.to_dict()
           lowerCamelCase       :				List[str]      			= self.decoder.to_dict()
           lowerCamelCase       :				Dict      			= self.__class__.model_type
           return output






