




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


_lowerCAmelCase  :				Union[str, Any]				=				logging.get_logger(__name__)  # pylint: disable=invalid-name
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class      _UpperCamelCase (			__A						):
      UpperCAmelCase_            =						42
      UpperCAmelCase_            =						None



def   lowerCAmelCase						(						_lowerCAmelCase							:				str  ,							_lowerCAmelCase							:				List[str]=0.999  ,							_lowerCAmelCase							:				Union[str, Any]="cosine"  ,							):




 """simple docstring"""







 if alpha_transform_type == "cosine":

  def alpha_bar_fn(_lowerCAmelCase							:				List[str]		):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2

 elif alpha_transform_type == "exp":

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

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

 UpperCAmelCase__								=  []
 for i in range(_a		):
  UpperCAmelCase__								=  i / num_diffusion_timesteps
  UpperCAmelCase__								=  (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 (			__A		,    __A						):
      UpperCAmelCase_            =						[e.name for e in KarrasDiffusionSchedulers]
      UpperCAmelCase_            =						1
      @register_to_config
      def __init__(     self				:int	, lowerCamelCase				:int = 1000	, lowerCamelCase				:float = 0.00_01	, lowerCamelCase				:float = 0.02	, lowerCamelCase				:str = "linear"	, lowerCamelCase				:Optional[Union[np.ndarray, List[float]]] = None	, lowerCamelCase				:str = "epsilon"	, lowerCamelCase				:str = "linspace"	, lowerCamelCase				:int = 0	, )       ->     Optional[Any]:

       if trained_betas is not None:
        UpperCAmelCase__								=  torch.tensor(lowerCamelCase	, dtype=torch.floataa )
       elif beta_schedule == "linear":
        UpperCAmelCase__								=  torch.linspace(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, dtype=torch.floataa )
       elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        UpperCAmelCase__								=  (
            torch.linspace(beta_start**0.5	, beta_end**0.5	, lowerCamelCase	, dtype=torch.floataa ) ** 2
        )
       elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        UpperCAmelCase__								=  betas_for_alpha_bar(lowerCamelCase )
       else:
        raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )

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

       UpperCAmelCase__								=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
       UpperCAmelCase__								=  np.concatenate([sigmas[::-1], [0.0]] ).astype(np.floataa )
       UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase )

       # setable values
       UpperCAmelCase__								=  None
       UpperCAmelCase__								=  np.linspace(0	, num_train_timesteps - 1	, lowerCamelCase	, dtype=lowerCamelCase )[::-1].copy()
       UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase )
       UpperCAmelCase__								=  False
      @property
      def 		UpperCAmelCase_  (     self				:Optional[Any] )       ->     int:

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

       return (self.sigmas.max() ** 2 + 1) ** 0.5
      def 		UpperCAmelCase_  (     self				:Dict	, lowerCamelCase				:torch.FloatTensor	, lowerCamelCase				:Union[float, torch.FloatTensor] )       ->     torch.FloatTensor:

       if isinstance(lowerCamelCase	, torch.Tensor ):
        UpperCAmelCase__								=  timestep.to(self.timesteps.device )
       UpperCAmelCase__								=  (self.timesteps == timestep).nonzero().item()
       UpperCAmelCase__								=  self.sigmas[step_index]
       UpperCAmelCase__								=  sample / ((sigma**2 + 1) ** 0.5)
       UpperCAmelCase__								=  True
       return sample
      def 		UpperCAmelCase_  (     self				:int	, lowerCamelCase				:int	, lowerCamelCase				:Union[str, torch.device] = None )       ->     int:
       UpperCAmelCase__								=  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":
        UpperCAmelCase__								=  np.linspace(0	, self.config.num_train_timesteps - 1	, lowerCamelCase	, dtype=lowerCamelCase )[
            ::-1
        ].copy()
       elif self.config.timestep_spacing == "leading":
        UpperCAmelCase__								=  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
        UpperCAmelCase__								=  (np.arange(0	, lowerCamelCase ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase )
        timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
        UpperCAmelCase__								=  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
        UpperCAmelCase__								=  (np.arange(self.config.num_train_timesteps	, 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\'.''' )

       UpperCAmelCase__								=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
       UpperCAmelCase__								=  np.interp(lowerCamelCase	, np.arange(0	, len(lowerCamelCase ) )	, lowerCamelCase )
       UpperCAmelCase__								=  np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
       UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase )
       if str(lowerCamelCase ).startswith("mps" ):
        # mps does not support float64
        UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase ).to(lowerCamelCase	, dtype=torch.floataa )
       else:
        UpperCAmelCase__								=  torch.from_numpy(lowerCamelCase ).to(device=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:torch.FloatTensor	, lowerCamelCase				:Union[float, torch.FloatTensor]	, lowerCamelCase				:torch.FloatTensor	, lowerCamelCase				:Optional[torch.Generator] = None	, lowerCamelCase				:bool = True	, )       ->     Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

       if (
           isinstance(lowerCamelCase	, lowerCamelCase )
           or isinstance(lowerCamelCase	, torch.IntTensor )
           or isinstance(lowerCamelCase	, 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(lowerCamelCase	, torch.Tensor ):
        UpperCAmelCase__								=  timestep.to(self.timesteps.device )

       UpperCAmelCase__								=  (self.timesteps == timestep).nonzero().item()
       UpperCAmelCase__								=  self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
        UpperCAmelCase__								=  sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        UpperCAmelCase__								=  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`''' )

       UpperCAmelCase__								=  self.sigmas[step_index]
       UpperCAmelCase__								=  self.sigmas[step_index + 1]
       UpperCAmelCase__								=  (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
       UpperCAmelCase__								=  (sigma_to**2 - sigma_up**2) ** 0.5

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

       UpperCAmelCase__								=  sigma_down - sigma

       UpperCAmelCase__								=  sample + derivative * dt

       UpperCAmelCase__								=  model_output.device
       UpperCAmelCase__								=  randn_tensor(model_output.shape	, dtype=model_output.dtype	, device=lowerCamelCase	, generator=lowerCamelCase )

       UpperCAmelCase__								=  prev_sample + noise * sigma_up

       if not return_dict:
        return (prev_sample,)

       return EulerAncestralDiscreteSchedulerOutput(
           prev_sample=lowerCamelCase	, pred_original_sample=lowerCamelCase )
      def 		UpperCAmelCase_  (     self				:str	, lowerCamelCase				:torch.FloatTensor	, lowerCamelCase				:torch.FloatTensor	, lowerCamelCase				:torch.FloatTensor	, )       ->     torch.FloatTensor:
       UpperCAmelCase__								=  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
        UpperCAmelCase__								=  self.timesteps.to(original_samples.device	, dtype=torch.floataa )
        UpperCAmelCase__								=  timesteps.to(original_samples.device	, dtype=torch.floataa )
       else:
        UpperCAmelCase__								=  self.timesteps.to(original_samples.device )
        UpperCAmelCase__								=  timesteps.to(original_samples.device )

       UpperCAmelCase__								=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       UpperCAmelCase__								=  original_samples + noise * sigma
       return noisy_samples







      def __len__(     self				:List[Any] )       ->     Dict:
       return self.config.num_train_timesteps











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowerCAmelCase  :				str						=				{
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Union[str, Any]						=				[
      "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EsmForMaskedLM",
      "EsmForSequenceClassification",
      "EsmForTokenClassification",
      "EsmModel",
      "EsmPreTrainedModel",
  ]
  _lowerCAmelCase  :				Tuple						=				["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				str						=				[
      "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEsmForMaskedLM",
      "TFEsmForSequenceClassification",
      "TFEsmForTokenClassification",
      "TFEsmModel",
      "TFEsmPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  from .tokenization_esm import EsmTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmForMaskedLM,
        EsmForSequenceClassification,
        EsmForTokenClassification,
        EsmModel,
        EsmPreTrainedModel,
    )
    from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_esm import (
        TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEsmForMaskedLM,
        TFEsmForSequenceClassification,
        TFEsmForTokenClassification,
        TFEsmModel,
        TFEsmPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase  :				List[Any]						=				_LazyModule(__name__, globals()["__file__"], _import_structure)





