


"""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=0.999,		a="cosine",		):





       """simple docstring"""







       if alpha_transform_type == "cosine":

              def alpha_bar_fn(a       ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(a       ):
                     return math.exp(t * -12.0       )

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

       _a							 =					[]
       for i in range(_UpperCamelCase       ):
              _a							 =					i / num_diffusion_timesteps
              _a							 =					(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 __snake_case		(       lowerCAmelCase__					,							lowerCAmelCase__   ):







   """simple docstring"""




   lowerCAmelCase_					: str  						=      [e.name for e in KarrasDiffusionSchedulers]
   lowerCAmelCase_					: Union[str, Any]  						=      2




   @register_to_config
   def __init__(		self				:Union[str, Any]					,	UpperCamelCase__				:Tuple = 1_000					,	UpperCamelCase__				:Tuple = 0.00085					,	UpperCamelCase__				:Dict = 0.012					,	UpperCamelCase__				:List[str] = "linear"					,	UpperCamelCase__				:Optional[Any] = None					,	UpperCamelCase__				:Optional[Any] = "epsilon"					,	UpperCamelCase__				:Optional[int] = "linspace"					,	UpperCamelCase__				:Optional[Any] = 0					,	):
          if trained_betas is not None:
                 _a							 =					torch.tensor(__UpperCAmelCase					,	dtype=torch.floataa							)
          elif beta_schedule == "linear":
                 _a							 =					torch.linspace(__UpperCAmelCase					,	__UpperCAmelCase					,	__UpperCAmelCase					,	dtype=torch.floataa							)
          elif beta_schedule == "scaled_linear":
                 # this schedule is very specific to the latent diffusion model.
                 _a							 =					(
                     torch.linspace(beta_start**0.5					,	beta_end**0.5					,	__UpperCAmelCase					,	dtype=torch.floataa							) ** 2
                 )
          elif beta_schedule == "squaredcos_cap_v2":
                 # Glide cosine schedule
                 _a							 =					betas_for_alpha_bar(__UpperCAmelCase							)
          else:
                 raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'							)

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

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




   def SCREAMING_SNAKE_CASE_   (		self				:List[Any]					,	UpperCamelCase__				:Dict					,	UpperCamelCase__				:int=None							):
          if schedule_timesteps is None:
                 _a							 =					self.timesteps

          _a							 =					(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:
                 _a							 =					1 if len(__UpperCAmelCase							) > 1 else 0
          else:
                 _a							 =					timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase							) else timestep
                 _a							 =					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				:Dict					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:str					,	):
          _a							 =					self.index_for_timestep(__UpperCAmelCase							)

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

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




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[Any]					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[Any] = None					,	UpperCamelCase__				:str = None					,	):
          _a							 =					num_inference_steps

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

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

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

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

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

          if str(__UpperCAmelCase							).startswith("mps"							):
                 # mps does not support float64
                 _a							 =					torch.from_numpy(__UpperCAmelCase							).to(__UpperCAmelCase					,	dtype=torch.floataa							)
          else:
                 _a							 =					torch.from_numpy(__UpperCAmelCase							).to(__UpperCAmelCase							)

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

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

          _a							 =					None

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




   def SCREAMING_SNAKE_CASE_   (		self				:Any					,	UpperCamelCase__				:List[Any]							):
          _a							 =					sigma.log()

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

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

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

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

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




   @property
   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]							):
          return self.sample is None




   def SCREAMING_SNAKE_CASE_   (		self				:Optional[int]					,	UpperCamelCase__				:str					,	UpperCamelCase__				:Any					,	UpperCamelCase__				:Tuple					,	UpperCamelCase__				:Optional[int] = True					,	):
          _a							 =					self.index_for_timestep(__UpperCAmelCase							)

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

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

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

                 # 3. delta timestep
                 _a							 =					sigma_next - sigma_hat

                 _a							 =					self.sample
                 _a							 =					None

          _a							 =					sample + derivative * dt

          if not return_dict:
                 return (prev_sample,)

          return SchedulerOutput(prev_sample=__UpperCAmelCase							)




   def SCREAMING_SNAKE_CASE_   (		self				:Dict					,	UpperCamelCase__				:int					,	UpperCamelCase__				:Optional[Any]					,	UpperCamelCase__				:Union[str, Any]					,	):
          _a							 =					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
                 _a							 =					self.timesteps.to(original_samples.device					,	dtype=torch.floataa							)
                 _a							 =					timesteps.to(original_samples.device					,	dtype=torch.floataa							)
          else:
                 _a							 =					self.timesteps.to(original_samples.device							)
                 _a							 =					timesteps.to(original_samples.device							)

          _a							 =					[self.index_for_timestep(__UpperCAmelCase					,	__UpperCAmelCase							) for t in timesteps]

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

          _a							 =					original_samples + noise * sigma
          return noisy_samples




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





from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					__UpperCAmelCase = False						,					__UpperCAmelCase = False						,					__UpperCAmelCase = None						,					__UpperCAmelCase = None						,					**__UpperCAmelCase						,					):



    '''simple docstring'''
    super().__init__(
        features=__UpperCAmelCase						,					cache_dir=__UpperCAmelCase						,					keep_in_memory=__UpperCAmelCase						,					streaming=__UpperCAmelCase						,					num_proc=__UpperCAmelCase						,					**__UpperCAmelCase						,					)
    __lowerCamelCase    				=     Generator(
        cache_dir=__UpperCAmelCase						,					features=__UpperCAmelCase						,					generator=__UpperCAmelCase						,					gen_kwargs=__UpperCAmelCase						,					**__UpperCAmelCase						,					)




 def 							lowerCamelCase   (    self					):



    '''simple docstring'''
    # Build iterable dataset
    if self.streaming:
       __lowerCamelCase    				=     self.builder.as_streaming_dataset(split='''train'''					)
    # Build regular (map-style) dataset
    else:
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None
       __lowerCamelCase    				=     None

       self.builder.download_and_prepare(
           download_config=__UpperCAmelCase						,					download_mode=__UpperCAmelCase						,					verification_mode=__UpperCAmelCase						,					base_path=__UpperCAmelCase						,					num_proc=self.num_proc						,					)
       __lowerCamelCase    				=     self.builder.as_dataset(
           split='''train'''						,					verification_mode=__UpperCAmelCase						,					in_memory=self.keep_in_memory					)
    return dataset






