



'''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    _SCREAMING_SNAKE_CASE  (       UpperCamelCase							,    UpperCamelCase=0.999							,    UpperCamelCase="cosine"							,    ):



    """simple docstring"""





    if alpha_transform_type == "cosine":

        def alpha_bar_fn(UpperCamelCase			):
            return math.cos((t + 0.008) / 1.008 * 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}"""			)

    lowerCAmelCase__ :  Optional[Any]							      =					[]
    for i in range(UpperCamelCase			):
        lowerCAmelCase__ :  Union[str, Any]							      =					i / num_diffusion_timesteps
        lowerCAmelCase__ :  Tuple							      =					(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        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_						, SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




  __lowercase							:      List[Any]				=      [e.name for e in KarrasDiffusionSchedulers]
  __lowercase							:      str				=      2




  @register_to_config
  def __init__(    self  ,__UpperCAmelCase = 1000  ,__UpperCAmelCase = 0.0_0_0_8_5  ,__UpperCAmelCase = 0.0_1_2  ,__UpperCAmelCase = "linear"  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "epsilon"  ,__UpperCAmelCase = "linspace"  ,__UpperCAmelCase = 0  ,)    ->		List[Any]:
      if trained_betas is not None:
          lowerCAmelCase__ :  Union[str, Any]							      =					torch.tensor(__UpperCAmelCase  ,dtype=torch.floataa      )
      elif beta_schedule == "linear":
          lowerCAmelCase__ :  List[str]							      =					torch.linspace(__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,dtype=torch.floataa      )
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          lowerCAmelCase__ :  Union[str, Any]							      =					(
              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[Any]							      =					1.0 - self.betas
      lowerCAmelCase__ :  Optional[Any]							      =					torch.cumprod(self.alphas  ,dim=0      )

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase=None      )    ->		Tuple:
      if schedule_timesteps is None:
          lowerCAmelCase__ :  int							      =					self.timesteps

      lowerCAmelCase__ :  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:
          lowerCAmelCase__ :  Dict							      =					1 if len(__UpperCAmelCase      ) > 1 else 0
      else:
          lowerCAmelCase__ :  Tuple							      =					timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase      ) else timestep
          lowerCAmelCase__ :  Optional[Any]							      =					self._index_counter[timestep_int]

      return indices[pos].item()




  @property
  def 							UpperCAmelCase_	(    self      )    ->		str:
      # 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 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,)    ->		torch.FloatTensor:
      lowerCAmelCase__ :  int							      =					self.index_for_timestep(__UpperCAmelCase      )

      if self.state_in_first_order:
          lowerCAmelCase__ :  Tuple							      =					self.sigmas[step_index]
      else:
          lowerCAmelCase__ :  Any							      =					self.sigmas_interpol[step_index]

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




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,)    ->		int:
      lowerCAmelCase__ :  Tuple							      =					num_inference_steps

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

      lowerCAmelCase__ :  Optional[int]							      =					np.interp(__UpperCAmelCase  ,np.arange(0  ,len(__UpperCAmelCase      )      )  ,__UpperCAmelCase      )
      lowerCAmelCase__ :  Optional[Any]							      =					np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
      lowerCAmelCase__ :  Optional[Any]							      =					torch.from_numpy(__UpperCAmelCase      ).to(device=__UpperCAmelCase      )

      # interpolate sigmas
      lowerCAmelCase__ :  Dict							      =					sigmas.log().lerp(sigmas.roll(1      ).log()  ,0.5      ).exp()

      lowerCAmelCase__ :  Dict							      =					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
      lowerCAmelCase__ :  List[Any]							      =					torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )

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

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

      lowerCAmelCase__ :  Tuple							      =					torch.cat([timesteps[:1], interleaved_timesteps]      )

      lowerCAmelCase__ :  List[str]							      =					None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      lowerCAmelCase__ :  Any							      =					defaultdict(__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase      )    ->		int:
      # get log sigma
      lowerCAmelCase__ :  str							      =					sigma.log()

      # get distribution
      lowerCAmelCase__ :  Union[str, Any]							      =					log_sigma - self.log_sigmas[:, None]

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

      lowerCAmelCase__ :  Any							      =					self.log_sigmas[low_idx]
      lowerCAmelCase__ :  Optional[Any]							      =					self.log_sigmas[high_idx]

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

      # transform interpolation to time range
      lowerCAmelCase__ :  Tuple							      =					(1 - w) * low_idx + w * high_idx
      lowerCAmelCase__ :  Union[str, Any]							      =					t.view(sigma.shape      )
      return t




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Tuple:
      return self.sample is None




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = True  ,)    ->		Union[SchedulerOutput, Tuple]:
      lowerCAmelCase__ :  Optional[Any]							      =					self.index_for_timestep(__UpperCAmelCase      )

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

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

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

          # 3. delta timestep
          lowerCAmelCase__ :  Optional[Any]							      =					sigma_next - sigma_hat

          lowerCAmelCase__ :  Tuple							      =					self.sample
          lowerCAmelCase__ :  int							      =					None

      lowerCAmelCase__ :  List[Any]							      =					sample + derivative * dt

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=__UpperCAmelCase      )




  def 							UpperCAmelCase_	(    self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,)    ->		torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      lowerCAmelCase__ :  List[Any]							      =					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__ :  Union[str, Any]							      =					self.timesteps.to(original_samples.device  ,dtype=torch.floataa      )
          lowerCAmelCase__ :  Optional[Any]							      =					timesteps.to(original_samples.device  ,dtype=torch.floataa      )
      else:
          lowerCAmelCase__ :  List[str]							      =					self.timesteps.to(original_samples.device      )
          lowerCAmelCase__ :  Union[str, Any]							      =					timesteps.to(original_samples.device      )

      lowerCAmelCase__ :  List[Any]							      =					[self.index_for_timestep(__UpperCAmelCase  ,__UpperCAmelCase      ) for t in timesteps]

      lowerCAmelCase__ :  List[str]							      =					sigmas[step_indices].flatten()
      while len(sigma.shape      ) < len(original_samples.shape      ):
          lowerCAmelCase__ :  Dict							      =					sigma.unsqueeze(-1      )

      lowerCAmelCase__ :  str							      =					original_samples + noise * sigma
      return noisy_samples






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







'''simple docstring'''





def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  str							      =					set()

    # Replace all the whitespace in our sentence
    lowerCAmelCase__ :  Tuple							      =					input_str.replace(""" """							,    """"""			)
    for alpha in input_str:
        if "a" <= alpha.lower() <= "z":
            frequency.add(alpha.lower()			)
    return len(UpperCamelCase			) == 26
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    lowerCAmelCase__ :  Any							      =					[False] * 26
    for char in input_str:
        if char.islower():
            lowerCAmelCase__ :  Optional[Any]							      =					True
        elif char.isupper():
            lowerCAmelCase__ :  Any							      =					True
    return all(UpperCamelCase			)
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = "The quick brown fox jumps over the lazy dog"							,    ):



    """simple docstring"""





    return len({char for char in input_str.lower() if char.isalpha()}			) == 26
def    _SCREAMING_SNAKE_CASE  (       ):



    """simple docstring"""





    from timeit import timeit

    lowerCAmelCase__ :  Union[str, Any]							      =					"""from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"""
    print(timeit("""is_pangram()"""							,    setup=UpperCamelCase			)			)
    print(timeit("""is_pangram_faster()"""							,    setup=UpperCamelCase			)			)
    print(timeit("""is_pangram_fastest()"""							,    setup=UpperCamelCase			)			)
    # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
    # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()


