


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 ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def 	_lowerCamelCase  (		A_  :	Dict							,  A_  :	Any=0.999							,  A_  :	Tuple="cosine"							,  )			->			int:

   '''simple docstring'''






   if alpha_transform_type == "cosine":

      def alpha_bar_fn(A_  :	Union[str, Any]      ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(A_  :	Optional[int]      ):
         return math.exp(t * -12.0      )

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

   UpperCamelCase__ :			Union[str, Any] 						=[]
   for i in range(lowerCamelCase__      ):
      UpperCamelCase__ :			int 						=i / num_diffusion_timesteps
      UpperCamelCase__ :			Tuple 						=(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 							lowercase__(						snake_case__			,							snake_case__			):






      '''simple docstring'''







      snake_case__       =					[e.name for e in KarrasDiffusionSchedulers]
      snake_case__       =					2




      @register_to_config
      def __init__(		self   ,			__SCREAMING_SNAKE_CASE = 10_00   ,			__SCREAMING_SNAKE_CASE = 0.0_00_85   ,			__SCREAMING_SNAKE_CASE = 0.0_12   ,			__SCREAMING_SNAKE_CASE = "linear"   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = "epsilon"   ,			__SCREAMING_SNAKE_CASE = "linspace"   ,			__SCREAMING_SNAKE_CASE = 0   ,			)					->						List[Any]:







         """simple docstring"""







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

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

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE=None)					->						Any:







         """simple docstring"""







         if schedule_timesteps is None:
            UpperCamelCase__ :			Dict 						=self.timesteps

         UpperCamelCase__ :			List[str] 						=(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:
            UpperCamelCase__ :			Any 						=1 if len(_snake_case) > 1 else 0
         else:
            UpperCamelCase__ :			Tuple 						=timestep.cpu().item() if torch.is_tensor(_snake_case) else timestep
            UpperCamelCase__ :			Optional[Any] 						=self._index_counter[timestep_int]

         return indices[pos].item()




      @property
      def    UpperCAmelCase							(		self)					->						Dict:







         """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   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			)					->						torch.FloatTensor:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.index_for_timestep(_snake_case)

         if self.state_in_first_order:
            UpperCamelCase__ :			Any 						=self.sigmas[step_index]
         else:
            UpperCamelCase__ :			Tuple 						=self.sigmas_interpol[step_index - 1]

         UpperCamelCase__ :			List[str] 						=sample / ((sigma**2 + 1) ** 0.5)
         return sample




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = None   ,			)					->						int:







         """simple docstring"""







         UpperCamelCase__ :			Optional[int] 						=num_inference_steps

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

         UpperCamelCase__ :			List[Any] 						=np.interp(_snake_case   ,			np.arange(0   ,			len(_snake_case))   ,			_snake_case)
         UpperCamelCase__ :			Optional[Any] 						=np.concatenate([sigmas, [0.0]]).astype(np.floataa)
         UpperCamelCase__ :			Union[str, Any] 						=torch.from_numpy(_snake_case).to(device=_snake_case)

         # compute up and down sigmas
         UpperCamelCase__ :			List[str] 						=sigmas.roll(-1)
         UpperCamelCase__ :			int 						=0.0
         UpperCamelCase__ :			str 						=(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
         UpperCamelCase__ :			str 						=(sigmas_next**2 - sigmas_up**2) ** 0.5
         UpperCamelCase__ :			Any 						=0.0

         # compute interpolated sigmas
         UpperCamelCase__ :			Any 						=sigmas.log().lerp(sigmas_down.log()   ,			0.5).exp()
         UpperCamelCase__ :			Union[str, Any] 						=0.0

         # set sigmas
         UpperCamelCase__ :			Any 						=torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
         UpperCamelCase__ :			int 						=torch.cat(
             [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
         UpperCamelCase__ :			str 						=torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
         UpperCamelCase__ :			Tuple 						=torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

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

         UpperCamelCase__ :			Union[str, Any] 						=self.sigma_to_t(_snake_case).to(_snake_case   ,			dtype=timesteps.dtype)
         UpperCamelCase__ :			str 						=torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,			dim=-1).flatten()

         UpperCamelCase__ :			Optional[int] 						=torch.cat([timesteps[:1], interleaved_timesteps])

         UpperCamelCase__ :			str 						=None

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




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE)					->						Dict:







         """simple docstring"""







         UpperCamelCase__ :			List[str] 						=sigma.log()

         # get distribution
         UpperCamelCase__ :			Dict 						=log_sigma - self.log_sigmas[:, None]

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

         UpperCamelCase__ :			Dict 						=self.log_sigmas[low_idx]
         UpperCamelCase__ :			Any 						=self.log_sigmas[high_idx]

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

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




      @property
      def    UpperCAmelCase							(		self)					->						str:







         """simple docstring"""







         return self.sample is None




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE = None   ,			__SCREAMING_SNAKE_CASE = True   ,			)					->						Union[SchedulerOutput, Tuple]:







         """simple docstring"""







         UpperCamelCase__ :			Union[str, Any] 						=self.index_for_timestep(_snake_case)

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

         if self.state_in_first_order:
            UpperCamelCase__ :			Dict 						=self.sigmas[step_index]
            UpperCamelCase__ :			str 						=self.sigmas_interpol[step_index]
            UpperCamelCase__ :			Optional[int] 						=self.sigmas_up[step_index]
            UpperCamelCase__ :			Union[str, Any] 						=self.sigmas_down[step_index - 1]
         else:
            # 2nd order / KPDM2's method
            UpperCamelCase__ :			Tuple 						=self.sigmas[step_index - 1]
            UpperCamelCase__ :			List[str] 						=self.sigmas_interpol[step_index - 1]
            UpperCamelCase__ :			int 						=self.sigmas_up[step_index - 1]
            UpperCamelCase__ :			List[Any] 						=self.sigmas_down[step_index - 1]

         # 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
         UpperCamelCase__ :			Any 						=0
         UpperCamelCase__ :			List[Any] 						=sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

         UpperCamelCase__ :			List[Any] 						=model_output.device
         UpperCamelCase__ :			Optional[int] 						=randn_tensor(model_output.shape   ,			dtype=model_output.dtype   ,			device=_snake_case   ,			generator=_snake_case)

         # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
         if self.config.prediction_type == "epsilon":
            UpperCamelCase__ :			str 						=sigma_hat if self.state_in_first_order else sigma_interpol
            UpperCamelCase__ :			str 						=sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
            UpperCamelCase__ :			Dict 						=sigma_hat if self.state_in_first_order else sigma_interpol
            UpperCamelCase__ :			Tuple 						=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
            UpperCamelCase__ :			List[Any] 						=(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            UpperCamelCase__ :			Optional[int] 						=sigma_interpol - sigma_hat

            # store for 2nd order step
            UpperCamelCase__ :			Optional[int] 						=sample
            UpperCamelCase__ :			Dict 						=dt
            UpperCamelCase__ :			Any 						=sample + derivative * dt
         else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            UpperCamelCase__ :			Any 						=(sample - pred_original_sample) / sigma_interpol
            # 3. delta timestep
            UpperCamelCase__ :			Dict 						=sigma_down - sigma_hat

            UpperCamelCase__ :			Dict 						=self.sample
            UpperCamelCase__ :			Dict 						=None

            UpperCamelCase__ :			Tuple 						=sample + derivative * dt
            UpperCamelCase__ :			int 						=prev_sample + noise * sigma_up

         if not return_dict:
            return (prev_sample,)

         return SchedulerOutput(prev_sample=_snake_case)




      def    UpperCAmelCase							(		self   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE   ,			)					->						torch.FloatTensor:







         """simple docstring"""







         UpperCamelCase__ :			int 						=self.sigmas.to(device=original_samples.device   ,			dtype=original_samples.dtype)
         if original_samples.device.type == "mps" and torch.is_floating_point(_snake_case):
            # mps does not support float64
            UpperCamelCase__ :			Optional[Any] 						=self.timesteps.to(original_samples.device   ,			dtype=torch.floataa)
            UpperCamelCase__ :			List[str] 						=timesteps.to(original_samples.device   ,			dtype=torch.floataa)
         else:
            UpperCamelCase__ :			Any 						=self.timesteps.to(original_samples.device)
            UpperCamelCase__ :			Optional[Any] 						=timesteps.to(original_samples.device)

         UpperCamelCase__ :			Union[str, Any] 						=[self.index_for_timestep(_snake_case   ,			_snake_case) for t in timesteps]

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

         UpperCamelCase__ :			str 						=original_samples + noise * sigma
         return noisy_samples




      def __len__(		self)					->						int:







         """simple docstring"""







         return self.config.num_train_timesteps









from collections import namedtuple

UpperCamelCase											=      namedtuple('''from_to''', '''from_ to''')

UpperCamelCase											=      {
    '''millimeter''': '''mm''',
    '''centimeter''': '''cm''',
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''inch''': '''in''',
    '''inche''': '''in''',  # Trailing 's' has been stripped off
    '''feet''': '''ft''',
    '''foot''': '''ft''',
    '''yard''': '''yd''',
    '''mile''': '''mi''',
}

UpperCamelCase											=      {
    '''mm''': from_to(0.001, 1_0_0_0),
    '''cm''': from_to(0.01, 1_0_0),
    '''m''': from_to(1, 1),
    '''km''': from_to(1_0_0_0, 0.001),
    '''in''': from_to(0.0254, 39.3701),
    '''ft''': from_to(0.3048, 3.2_8084),
    '''yd''': from_to(0.9144, 1.0_9361),
    '''mi''': from_to(1609.34, 0.0_0062_1371),
}




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      float:
 lowerCamelCase_							:    List[Any]							=     from_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[Any]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 lowerCamelCase_							:    List[Any]							=     to_type.lower().rstrip('s'				)
 lowerCamelCase_							:    Optional[int]							=     TYPE_CONVERSION.get(lowerCamelCase__ ,			lowerCamelCase__				)
 if new_from not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[Any]							=     (
      F'Invalid \'from_type\' value: {from_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 if new_to not in METRIC_CONVERSION:
  lowerCamelCase_							:    List[str]							=     (
      F'Invalid \'to_type\' value: {to_type!r}.\n'
      F'Conversion abbreviations are: {", ".join(lowerCamelCase__				)}'
  )
  raise ValueError(lowerCamelCase__				)
 return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
       import doctest

       doctest.testmod()
