





'''simple docstring'''

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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=0.9_9_9							,	__UpperCAmelCase="cosine"							,	)						->					Tuple:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase      ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * 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}"""      )

   __SCREAMING_SNAKE_CASE      							=       []
   for i in range(__UpperCAmelCase      ):
      __SCREAMING_SNAKE_CASE      							=       i / num_diffusion_timesteps
      __SCREAMING_SNAKE_CASE      							=       (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 __a (   _snake_case,   _snake_case			):

   '''simple docstring'''





   __UpperCamelCase   : int	      =	[e.name for e in KarrasDiffusionSchedulers]
   __UpperCamelCase   : str	      =	2





   @register_to_config
   def __init__(					self				:			int				,lowerCamelCase				:			int = 1000				,lowerCamelCase				:			float = 0.00_085				,lowerCamelCase				:			float = 0.012				,lowerCamelCase				:			str = "linear"				,lowerCamelCase				:			Optional[Union[np.ndarray, List[float]]] = None				,lowerCamelCase				:			str = "epsilon"				,lowerCamelCase				:			Optional[bool] = False				,lowerCamelCase				:			Optional[bool] = False				,lowerCamelCase				:			float = 1.0				,lowerCamelCase				:			str = "linspace"				,lowerCamelCase				:			int = 0				,):

      '''simple docstring'''

      if trained_betas is not None:
         __SCREAMING_SNAKE_CASE      							=       torch.tensor(lowerCamelCase				,dtype=torch.floataa    )
      elif beta_schedule == "linear":
         __SCREAMING_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.
         __SCREAMING_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
         __SCREAMING_SNAKE_CASE      							=       betas_for_alpha_bar(lowerCamelCase				,alpha_transform_type="""cosine"""    )
      elif beta_schedule == "exp":
         __SCREAMING_SNAKE_CASE      							=       betas_for_alpha_bar(lowerCamelCase				,alpha_transform_type="""exp"""    )
      else:
         raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""    )

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

      #  set all values
      self.set_timesteps(lowerCamelCase				,lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       use_karras_sigmas





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			int				,lowerCamelCase				:			Dict=None    ):

      '''simple docstring'''

      if schedule_timesteps is None:
         __SCREAMING_SNAKE_CASE      							=       self.timesteps

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

      return indices[pos].item()





   @property
   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

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

      return (self.sigmas.max() ** 2 + 1) ** 0.5





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			Union[float, torch.FloatTensor]				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.index_for_timestep(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.sigmas[step_index]
      __SCREAMING_SNAKE_CASE      							=       sample / ((sigma**2 + 1) ** 0.5)
      return sample





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       num_inference_steps

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

      __SCREAMING_SNAKE_CASE      							=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
      __SCREAMING_SNAKE_CASE      							=       np.log(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       np.interp(lowerCamelCase				,np.arange(0				,len(lowerCamelCase    )    )				,lowerCamelCase    )

      if self.config.use_karras_sigmas:
         __SCREAMING_SNAKE_CASE      							=       self._convert_to_karras(in_sigmas=lowerCamelCase				,num_inference_steps=self.num_inference_steps    )
         __SCREAMING_SNAKE_CASE      							=       np.array([self._sigma_to_t(lowerCamelCase				,lowerCamelCase    ) for sigma in sigmas]    )

      __SCREAMING_SNAKE_CASE      							=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       torch.from_numpy(lowerCamelCase    ).to(device=lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2    ), sigmas[-1:]]    )

      __SCREAMING_SNAKE_CASE      							=       torch.from_numpy(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2    )]    )

      if str(lowerCamelCase    ).startswith("""mps"""    ):
         # mps does not support float64
         __SCREAMING_SNAKE_CASE      							=       timesteps.to(lowerCamelCase				,dtype=torch.floataa    )
      else:
         __SCREAMING_SNAKE_CASE      							=       timesteps.to(device=lowerCamelCase    )

      # empty dt and derivative
      __SCREAMING_SNAKE_CASE      							=       None
      __SCREAMING_SNAKE_CASE      							=       None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      __SCREAMING_SNAKE_CASE      							=       defaultdict(lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			List[str]				,lowerCamelCase				:			Optional[Any]				,lowerCamelCase				:			Dict    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       np.log(lowerCamelCase    )

      # get distribution
      __SCREAMING_SNAKE_CASE      							=       log_sigma - log_sigmas[:, np.newaxis]

      # get sigmas range
      __SCREAMING_SNAKE_CASE      							=       np.cumsum((dists >= 0)				,axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
      __SCREAMING_SNAKE_CASE      							=       low_idx + 1

      __SCREAMING_SNAKE_CASE      							=       log_sigmas[low_idx]
      __SCREAMING_SNAKE_CASE      							=       log_sigmas[high_idx]

      # interpolate sigmas
      __SCREAMING_SNAKE_CASE      							=       (low - log_sigma) / (low - high)
      __SCREAMING_SNAKE_CASE      							=       np.clip(lowerCamelCase				,0				,1    )

      # transform interpolation to time range
      __SCREAMING_SNAKE_CASE      							=       (1 - w) * low_idx + w * high_idx
      __SCREAMING_SNAKE_CASE      							=       t.reshape(sigma.shape    )
      return t





   def      UpperCAmelCase__     (					self				:			Optional[int]				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       in_sigmas[-1].item()
      __SCREAMING_SNAKE_CASE      							=       in_sigmas[0].item()

      __SCREAMING_SNAKE_CASE      							=       7.0  # 7.0 is the value used in the paper
      __SCREAMING_SNAKE_CASE      							=       np.linspace(0				,1				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       sigma_min ** (1 / rho)
      __SCREAMING_SNAKE_CASE      							=       sigma_max ** (1 / rho)
      __SCREAMING_SNAKE_CASE      							=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
      return sigmas





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return self.dt is None





   def      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase				:			Union[float, torch.FloatTensor]				,lowerCamelCase				:			Union[torch.FloatTensor, np.ndarray]				,lowerCamelCase				:			bool = True				,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.index_for_timestep(lowerCamelCase    )

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

      if self.state_in_first_order:
         __SCREAMING_SNAKE_CASE      							=       self.sigmas[step_index]
         __SCREAMING_SNAKE_CASE      							=       self.sigmas[step_index + 1]
      else:
         # 2nd order / Heun's method
         __SCREAMING_SNAKE_CASE      							=       self.sigmas[step_index - 1]
         __SCREAMING_SNAKE_CASE      							=       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      							=       0
      __SCREAMING_SNAKE_CASE      							=       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      							=       sigma_hat if self.state_in_first_order else sigma_next
         __SCREAMING_SNAKE_CASE      							=       sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         __SCREAMING_SNAKE_CASE      							=       sigma_hat if self.state_in_first_order else sigma_next
         __SCREAMING_SNAKE_CASE      							=       model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
      elif self.config.prediction_type == "sample":
         __SCREAMING_SNAKE_CASE      							=       model_output
      else:
         raise ValueError(
             f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

      if self.config.clip_sample:
         __SCREAMING_SNAKE_CASE      							=       pred_original_sample.clamp(
             -self.config.clip_sample_range				,self.config.clip_sample_range    )

      if self.state_in_first_order:
         # 2. Convert to an ODE derivative for 1st order
         __SCREAMING_SNAKE_CASE      							=       (sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __SCREAMING_SNAKE_CASE      							=       sigma_next - sigma_hat

         # store for 2nd order step
         __SCREAMING_SNAKE_CASE      							=       derivative
         __SCREAMING_SNAKE_CASE      							=       dt
         __SCREAMING_SNAKE_CASE      							=       sample
      else:
         # 2. 2nd order / Heun's method
         __SCREAMING_SNAKE_CASE      							=       (sample - pred_original_sample) / sigma_next
         __SCREAMING_SNAKE_CASE      							=       (self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         __SCREAMING_SNAKE_CASE      							=       self.dt
         __SCREAMING_SNAKE_CASE      							=       self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         __SCREAMING_SNAKE_CASE      							=       None
         __SCREAMING_SNAKE_CASE      							=       None
         __SCREAMING_SNAKE_CASE      							=       None

      __SCREAMING_SNAKE_CASE      							=       sample + derivative * dt

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=lowerCamelCase    )





   def      UpperCAmelCase__     (					self				:			Dict				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			torch.FloatTensor				,lowerCamelCase				:			torch.FloatTensor				,):

      '''simple docstring'''

      __SCREAMING_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
         __SCREAMING_SNAKE_CASE      							=       self.timesteps.to(original_samples.device				,dtype=torch.floataa    )
         __SCREAMING_SNAKE_CASE      							=       timesteps.to(original_samples.device				,dtype=torch.floataa    )
      else:
         __SCREAMING_SNAKE_CASE      							=       self.timesteps.to(original_samples.device    )
         __SCREAMING_SNAKE_CASE      							=       timesteps.to(original_samples.device    )

      __SCREAMING_SNAKE_CASE      							=       [self.index_for_timestep(lowerCamelCase				,lowerCamelCase    ) for t in timesteps]

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

      __SCREAMING_SNAKE_CASE      							=       original_samples + noise * sigma
      return noisy_samples





   def __len__(					self				:			Optional[int]    ):

      '''simple docstring'''

      return self.config.num_train_timesteps












'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a        =						{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    a        =						[
        "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SEWForCTC",
        "SEWForSequenceClassification",
        "SEWModel",
        "SEWPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_sew import (
            SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
            SEWForCTC,
            SEWForSequenceClassification,
            SEWModel,
            SEWPreTrainedModel,
        )


else:
    import sys

    a        =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





