
'''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 		A_							(						_lowerCAmelCase :     int					,						_lowerCAmelCase :     Any=0.9_9_9					,						_lowerCAmelCase :     int="cosine"					,						):



      """simple docstring"""




      if alpha_transform_type == "cosine":

            def alpha_bar_fn(_lowerCAmelCase :     List[Any]				):
                  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(_lowerCAmelCase :     Optional[int]				):
                  return math.exp(t * -1_2.0				)

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

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



class 			UpperCAmelCase__    (							A    ,						A			):
    lowerCAmelCase_					     =		[e.name for e in KarrasDiffusionSchedulers]
    lowerCAmelCase_					     =		2



    @register_to_config
    def __init__(						self   :							List[Any],__A   :							int = 1_0_0_0,__A   :							float = 0.00085,__A   :							float = 0.012,__A   :							str = "linear",__A   :							Optional[Union[np.ndarray, List[float]]] = None,__A   :							str = "epsilon",__A   :							str = "linspace",__A   :							int = 0,):
          if trained_betas is not None:
                _lowerCamelCase					: List[str]		    = torch.tensor(__A,dtype=torch.floataa  )
          elif beta_schedule == "linear":
                _lowerCamelCase					: Optional[Any]		    = torch.linspace(__A,__A,__A,dtype=torch.floataa  )
          elif beta_schedule == "scaled_linear":
                # this schedule is very specific to the latent diffusion model.
                _lowerCamelCase					: List[Any]		    = (
                    torch.linspace(beta_start**0.5,beta_end**0.5,__A,dtype=torch.floataa  ) ** 2
                )
          elif beta_schedule == "squaredcos_cap_v2":
                # Glide cosine schedule
                _lowerCamelCase					: Union[str, Any]		    = betas_for_alpha_bar(__A  )
          else:
                raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )

          _lowerCamelCase					: Dict		    = 1.0 - self.betas
          _lowerCamelCase					: List[Any]		    = torch.cumprod(self.alphas,dim=0  )

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



    def 	lowerCamelCase_	(						self   :							Optional[Any],__A   :							Union[str, Any],__A   :							List[str]=None  ):
          if schedule_timesteps is None:
                _lowerCamelCase					: Tuple		    = self.timesteps

          _lowerCamelCase					: Optional[Any]		    = (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:
                _lowerCamelCase					: Optional[int]		    = 1 if len(__A  ) > 1 else 0
          else:
                _lowerCamelCase					: Optional[Any]		    = timestep.cpu().item() if torch.is_tensor(__A  ) else timestep
                _lowerCamelCase					: List[Any]		    = self._index_counter[timestep_int]

          return indices[pos].item()



    @property
    def 	lowerCamelCase_	(						self   :							Tuple  ):
          # 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 	lowerCamelCase_	(						self   :							Dict,__A   :							torch.FloatTensor,__A   :							Union[float, torch.FloatTensor],):
          _lowerCamelCase					: Tuple		    = self.index_for_timestep(__A  )

          if self.state_in_first_order:
                _lowerCamelCase					: str		    = self.sigmas[step_index]
          else:
                _lowerCamelCase					: Dict		    = self.sigmas_interpol[step_index]

          _lowerCamelCase					: Tuple		    = sample / ((sigma**2 + 1) ** 0.5)
          return sample



    def 	lowerCamelCase_	(						self   :							List[str],__A   :							int,__A   :							Union[str, torch.device] = None,__A   :							Optional[int] = None,):
          _lowerCamelCase					: int		    = num_inference_steps

          _lowerCamelCase					: Optional[int]		    = 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":
                _lowerCamelCase					: Optional[Any]		    = np.linspace(0,num_train_timesteps - 1,__A,dtype=__A  )[::-1].copy()
          elif self.config.timestep_spacing == "leading":
                _lowerCamelCase					: Optional[int]		    = 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					: Tuple		    = (np.arange(0,__A  ) * step_ratio).round()[::-1].copy().astype(__A  )
                timesteps += self.config.steps_offset
          elif self.config.timestep_spacing == "trailing":
                _lowerCamelCase					: 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
                _lowerCamelCase					: Union[str, Any]		    = (np.arange(__A,0,-step_ratio  )).round().copy().astype(__A  )
                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					: int		    = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
          _lowerCamelCase					: Dict		    = torch.from_numpy(np.log(__A  )  ).to(__A  )

          _lowerCamelCase					: Union[str, Any]		    = np.interp(__A,np.arange(0,len(__A  )  ),__A  )
          _lowerCamelCase					: Dict		    = np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
          _lowerCamelCase					: Dict		    = torch.from_numpy(__A  ).to(device=__A  )

          # interpolate sigmas
          _lowerCamelCase					: List[str]		    = sigmas.log().lerp(sigmas.roll(1  ).log(),0.5  ).exp()

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

          if str(__A  ).startswith("mps"  ):
                # mps does not support float64
                _lowerCamelCase					: List[Any]		    = torch.from_numpy(__A  ).to(__A,dtype=torch.floataa  )
          else:
                _lowerCamelCase					: Tuple		    = torch.from_numpy(__A  ).to(__A  )

          # interpolate timesteps
          _lowerCamelCase					: Optional[Any]		    = self.sigma_to_t(__A  ).to(__A,dtype=timesteps.dtype  )
          _lowerCamelCase					: List[Any]		    = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),dim=-1  ).flatten()

          _lowerCamelCase					: Any		    = torch.cat([timesteps[:1], interleaved_timesteps]  )

          _lowerCamelCase					: Dict		    = None

          # for exp beta schedules, such as the one for `pipeline_shap_e.py`
          # we need an index counter
          _lowerCamelCase					: List[str]		    = defaultdict(__A  )



    def 	lowerCamelCase_	(						self   :							List[Any],__A   :							Dict  ):
          # get log sigma
          _lowerCamelCase					: Any		    = sigma.log()

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

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

          _lowerCamelCase					: Dict		    = self.log_sigmas[low_idx]
          _lowerCamelCase					: Dict		    = self.log_sigmas[high_idx]

          # interpolate sigmas
          _lowerCamelCase					: List[str]		    = (low - log_sigma) / (low - high)
          _lowerCamelCase					: Dict		    = w.clamp(0,1  )

          # transform interpolation to time range
          _lowerCamelCase					: Dict		    = (1 - w) * low_idx + w * high_idx
          _lowerCamelCase					: List[str]		    = t.view(sigma.shape  )
          return t



    @property
    def 	lowerCamelCase_	(						self   :							Any  ):
          return self.sample is None



    def 	lowerCamelCase_	(						self   :							Optional[int],__A   :							Union[torch.FloatTensor, np.ndarray],__A   :							Union[float, torch.FloatTensor],__A   :							Union[torch.FloatTensor, np.ndarray],__A   :							bool = True,):
          _lowerCamelCase					: List[str]		    = self.index_for_timestep(__A  )

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

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

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

                # 3. delta timestep
                _lowerCamelCase					: str		    = sigma_next - sigma_hat

                _lowerCamelCase					: List[Any]		    = self.sample
                _lowerCamelCase					: Optional[int]		    = None

          _lowerCamelCase					: Optional[Any]		    = sample + derivative * dt

          if not return_dict:
                return (prev_sample,)

          return SchedulerOutput(prev_sample=__A  )



    def 	lowerCamelCase_	(						self   :							Any,__A   :							torch.FloatTensor,__A   :							torch.FloatTensor,__A   :							torch.FloatTensor,):
          # Make sure sigmas and timesteps have the same device and dtype as original_samples
          _lowerCamelCase					: 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(__A  ):
                # mps does not support float64
                _lowerCamelCase					: List[Any]		    = self.timesteps.to(original_samples.device,dtype=torch.floataa  )
                _lowerCamelCase					: str		    = timesteps.to(original_samples.device,dtype=torch.floataa  )
          else:
                _lowerCamelCase					: Tuple		    = self.timesteps.to(original_samples.device  )
                _lowerCamelCase					: Any		    = timesteps.to(original_samples.device  )

          _lowerCamelCase					: Optional[int]		    = [self.index_for_timestep(__A,__A  ) for t in timesteps]

          _lowerCamelCase					: Union[str, Any]		    = sigmas[step_indices].flatten()
          while len(sigma.shape  ) < len(original_samples.shape  ):
                _lowerCamelCase					: Dict		    = sigma.unsqueeze(-1  )

          _lowerCamelCase					: Tuple		    = original_samples + noise * sigma
          return noisy_samples



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

"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig''']}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    ['''BeitFeatureExtractor''']
   SCREAMING_SNAKE_CASE_            =    ['''BeitImageProcessor''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''BEIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''BeitForImageClassification''',
       '''BeitForMaskedImageModeling''',
       '''BeitForSemanticSegmentation''',
       '''BeitModel''',
       '''BeitPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''FlaxBeitForImageClassification''',
       '''FlaxBeitForMaskedImageModeling''',
       '''FlaxBeitModel''',
       '''FlaxBeitPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_beit import BeitFeatureExtractor
      from .image_processing_beit import BeitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_beit import (
          BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BeitForImageClassification,
          BeitForMaskedImageModeling,
          BeitForSemanticSegmentation,
          BeitModel,
          BeitPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_beit import (
          FlaxBeitForImageClassification,
          FlaxBeitForMaskedImageModeling,
          FlaxBeitModel,
          FlaxBeitPreTrainedModel,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



