

'''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     (    A__     ,				A__=0.9_9_9     ,				A__="cosine"     ,				):






 """simple docstring"""



 if alpha_transform_type == "cosine":

  def alpha_bar_fn(A__      ):
   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(A__      ):
   return math.exp(t * -1_2.0      )

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

 __lowercase							  = []
 for i in range(lowercase__      ):
  __lowercase							  = i / num_diffusion_timesteps
  __lowercase							  = (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(lowercase__      ) / alpha_bar_fn(lowercase__      )     ,				lowercase__      )      )
 return torch.tensor(lowercase__     ,				dtype=torch.floataa      )






class 			lowercase_	(A_				,       A_      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[Any]							=			[e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE    :  int							=			2






  @register_to_config
  def __init__(      self   :     Optional[int]					,lowercase__   :     Optional[Any] = 1_0_0_0					,lowercase__   :     Any = 0.0_0_0_8_5					,lowercase__   :     str = 0.0_1_2					,lowercase__   :     Tuple = "linear"					,lowercase__   :     Optional[Any] = None					,lowercase__   :     Optional[Any] = "epsilon"					,lowercase__   :     Dict = "linspace"					,lowercase__   :     Dict = 0					,):
   if trained_betas is not None:
    __lowercase							  = torch.tensor(lowercase__					,dtype=torch.floataa			)
   elif beta_schedule == "linear":
    __lowercase							  = torch.linspace(lowercase__					,lowercase__					,lowercase__					,dtype=torch.floataa			)
   elif beta_schedule == "scaled_linear":
    # this schedule is very specific to the latent diffusion model.
    __lowercase							  = (
        torch.linspace(beta_start**0.5					,beta_end**0.5					,lowercase__					,dtype=torch.floataa			) ** 2
    )
   elif beta_schedule == "squaredcos_cap_v2":
    # Glide cosine schedule
    __lowercase							  = betas_for_alpha_bar(lowercase__			)
   else:
    raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"			)

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

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Tuple					,lowercase__   :     Optional[int]=None			):
   if schedule_timesteps is None:
    __lowercase							  = self.timesteps

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

   return indices[pos].item()






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   # 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 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     Any					,lowercase__   :     List[str]					,):
   __lowercase							  = self.index_for_timestep(lowercase__			)

   if self.state_in_first_order:
    __lowercase							  = self.sigmas[step_index]
   else:
    __lowercase							  = self.sigmas_interpol[step_index]

   __lowercase							  = sample / ((sigma**2 + 1) ** 0.5)
   return sample






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,lowercase__   :     str					,lowercase__   :     Union[str, Any] = None					,lowercase__   :     Dict = None					,):
   __lowercase							  = num_inference_steps

   __lowercase							  = 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":
    __lowercase							  = np.linspace(0					,num_train_timesteps - 1					,lowercase__					,dtype=lowercase__			)[::-1].copy()
   elif self.config.timestep_spacing == "leading":
    __lowercase							  = 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
    __lowercase							  = (np.arange(0					,lowercase__			) * step_ratio).round()[::-1].copy().astype(lowercase__			)
    timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
    __lowercase							  = 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
    __lowercase							  = (np.arange(lowercase__					,0					,-step_ratio			)).round().copy().astype(lowercase__			)
    timesteps -= 1
   else:
    raise ValueError(
        F"{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."			)

   __lowercase							  = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5			)
   __lowercase							  = torch.from_numpy(np.log(lowercase__			)			).to(lowercase__			)

   __lowercase							  = np.interp(lowercase__					,np.arange(0					,len(lowercase__			)			)					,lowercase__			)
   __lowercase							  = np.concatenate([sigmas, [0.0]]			).astype(np.floataa			)
   __lowercase							  = torch.from_numpy(lowercase__			).to(device=lowercase__			)

   # interpolate sigmas
   __lowercase							  = sigmas.log().lerp(sigmas.roll(1			).log()					,0.5			).exp()

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

   if str(lowercase__			).startswith('''mps'''			):
    # mps does not support float64
    __lowercase							  = torch.from_numpy(lowercase__			).to(lowercase__					,dtype=torch.floataa			)
   else:
    __lowercase							  = torch.from_numpy(lowercase__			).to(lowercase__			)

   # interpolate timesteps
   __lowercase							  = self.sigma_to_t(lowercase__			).to(lowercase__					,dtype=timesteps.dtype			)
   __lowercase							  = torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					,dim=-1			).flatten()

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

   __lowercase							  = None

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     Union[str, Any]			):
   # get log sigma
   __lowercase							  = sigma.log()

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

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

   __lowercase							  = self.log_sigmas[low_idx]
   __lowercase							  = self.log_sigmas[high_idx]

   # interpolate sigmas
   __lowercase							  = (low - log_sigma) / (low - high)
   __lowercase							  = w.clamp(0					,1			)

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






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   return self.sample is None






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     Union[str, Any]					,lowercase__   :     Tuple					,lowercase__   :     Union[str, Any]					,lowercase__   :     Tuple = True					,):
   __lowercase							  = self.index_for_timestep(lowercase__			)

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

   if self.state_in_first_order:
    __lowercase							  = self.sigmas[step_index]
    __lowercase							  = self.sigmas_interpol[step_index + 1]
    __lowercase							  = self.sigmas[step_index + 1]
   else:
    # 2nd order / KDPM2's method
    __lowercase							  = self.sigmas[step_index - 1]
    __lowercase							  = self.sigmas_interpol[step_index]
    __lowercase							  = 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
   __lowercase							  = 0
   __lowercase							  = 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":
    __lowercase							  = sigma_hat if self.state_in_first_order else sigma_interpol
    __lowercase							  = sample - sigma_input * model_output
   elif self.config.prediction_type == "v_prediction":
    __lowercase							  = sigma_hat if self.state_in_first_order else sigma_interpol
    __lowercase							  = 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
    __lowercase							  = (sample - pred_original_sample) / sigma_hat
    # 3. delta timestep
    __lowercase							  = sigma_interpol - sigma_hat

    # store for 2nd order step
    __lowercase							  = sample
   else:
    # DPM-Solver-2
    # 2. Convert to an ODE derivative for 2nd order
    __lowercase							  = (sample - pred_original_sample) / sigma_interpol

    # 3. delta timestep
    __lowercase							  = sigma_next - sigma_hat

    __lowercase							  = self.sample
    __lowercase							  = None

   __lowercase							  = sample + derivative * dt

   if not return_dict:
    return (prev_sample,)

   return SchedulerOutput(prev_sample=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     str					,lowercase__   :     Optional[int]					,lowercase__   :     str					,):
   # Make sure sigmas and timesteps have the same device and dtype as original_samples
   __lowercase							  = self.sigmas.to(device=original_samples.device					,dtype=original_samples.dtype			)
   if original_samples.device.type == "mps" and torch.is_floating_point(lowercase__			):
    # mps does not support float64
    __lowercase							  = self.timesteps.to(original_samples.device					,dtype=torch.floataa			)
    __lowercase							  = timesteps.to(original_samples.device					,dtype=torch.floataa			)
   else:
    __lowercase							  = self.timesteps.to(original_samples.device			)
    __lowercase							  = timesteps.to(original_samples.device			)

   __lowercase							  = [self.index_for_timestep(lowercase__					,lowercase__			) for t in timesteps]

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

   __lowercase							  = original_samples + noise * sigma
   return noisy_samples






  def __len__(      self   :     Any			):
   return self.config.num_train_timesteps





from __future__ import annotations







def        _lowerCamelCase(	lowercase__   , lowercase__	)						->	bool:

		'''simple docstring'''







		__lowercase=			get_failure_array(lowercase__	)

		# 2) Step through text searching for pattern
		__lowercase,							__lowercase=			0, 0  # index into text, pattern
		while i < len(lowercase__	):
				if pattern[j] == text[i]:
						if j == (len(lowercase__	) - 1):
								return True
						j += 1

				# if this is a prefix in our pattern
				# just go back far enough to continue
				elif j > 0:
						__lowercase=			failure[j - 1]
						continue
				i += 1
		return False







def        _lowerCamelCase(	lowercase__	)						->	list[int]:

		'''simple docstring'''







		__lowercase=			[0]
		__lowercase=			0
		__lowercase=			1
		while j < len(lowercase__	):
				if pattern[i] == pattern[j]:
						i += 1
				elif i > 0:
						__lowercase=			failure[i - 1]
						continue
				j += 1
				failure.append(lowercase__	)
		return failure


if __name__ == "__main__":
	# Test 1)
	lowerCAmelCase           =		'''abc1abc12'''
	lowerCAmelCase           =		'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
	lowerCAmelCase           =		'''alskfjaldsk23adsfabcabc'''
	assert kmp(pattern, texta) and not kmp(pattern, texta)

	# Test 2)
	lowerCAmelCase           =		'''ABABX'''
	lowerCAmelCase           =		'''ABABZABABYABABX'''
	assert kmp(pattern, text)

	# Test 3)
	lowerCAmelCase           =		'''AAAB'''
	lowerCAmelCase           =		'''ABAAAAAB'''
	assert kmp(pattern, text)

	# Test 4)
	lowerCAmelCase           =		'''abcdabcy'''
	lowerCAmelCase           =		'''abcxabcdabxabcdabcdabcy'''
	assert kmp(pattern, text)

	# Test 5)
	lowerCAmelCase           =		'''aabaabaaa'''
	assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]






