'''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       lowerCAmelCase_							(   snake_case_			:					List[Any]   ,       snake_case_			:					Union[str, Any]=0.999   ,       snake_case_			:					Tuple="cosine"   ,       )       -> Optional[Any]:


     '''simple docstring'''




     if alpha_transform_type == "cosine":

          def alpha_bar_fn(snake_case_			:					Optional[int]     ):
               return math.cos((t + 0.008) / 1.008 * math.pi / 2     ) ** 2

     elif alpha_transform_type == "exp":

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

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

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






class 			__A (					UpperCamelCase__						,      UpperCamelCase__      ):
    a__			:		Tuple			  =    [e.name for e in KarrasDiffusionSchedulers]
    a__			:		Optional[Any]			  =    2



    @register_to_config
    def __init__(self       :       Union[str, Any]  ,     __a       :       int = 1000  ,     __a       :       float = 0.0_00_85  ,     __a       :       float = 0.0_12  ,     __a       :       str = "linear"  ,     __a       :       Optional[Union[np.ndarray, List[float]]] = None  ,     __a       :       str = "epsilon"  ,     __a       :       Optional[bool] = False  ,     __a       :       Optional[bool] = False  ,     __a       :       float = 1.0  ,     __a       :       str = "linspace"  ,     __a       :       int = 0  ,     ):
         if trained_betas is not None:
              UpperCAmelCase_			       =  torch.tensor(__a  ,     dtype=torch.floataa					)
         elif beta_schedule == "linear":
              UpperCAmelCase_			       =  torch.linspace(__a  ,     __a  ,     __a  ,     dtype=torch.floataa					)
         elif beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase_			       =  (
                  torch.linspace(beta_start**0.5  ,     beta_end**0.5  ,     __a  ,     dtype=torch.floataa					) ** 2
              )
         elif beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCAmelCase_			       =  betas_for_alpha_bar(__a  ,     alpha_transform_type="cosine"					)
         elif beta_schedule == "exp":
              UpperCAmelCase_			       =  betas_for_alpha_bar(__a  ,     alpha_transform_type="exp"					)
         else:
              raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""					)

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

         #  set all values
         self.set_timesteps(__a  ,     __a  ,     __a					)
         UpperCAmelCase_			       =  use_karras_sigmas



    def    _lowercase		(self       :       Optional[Any]  ,     __a       :       Union[str, Any]  ,     __a       :       Tuple=None					):
         if schedule_timesteps is None:
              UpperCAmelCase_			       =  self.timesteps

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

         return indices[pos].item()



    @property
    def    _lowercase		(self       :       List[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    _lowercase		(self       :       Optional[Any]  ,     __a       :       torch.FloatTensor  ,     __a       :       Union[float, torch.FloatTensor]  ,     ):
         UpperCAmelCase_			       =  self.index_for_timestep(__a					)

         UpperCAmelCase_			       =  self.sigmas[step_index]
         UpperCAmelCase_			       =  sample / ((sigma**2 + 1) ** 0.5)
         return sample



    def    _lowercase		(self       :       Any  ,     __a       :       int  ,     __a       :       Union[str, torch.device] = None  ,     __a       :       Optional[int] = None  ,     ):
         UpperCAmelCase_			       =  num_inference_steps

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

         UpperCAmelCase_			       =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
         UpperCAmelCase_			       =  np.log(__a					)
         UpperCAmelCase_			       =  np.interp(__a  ,     np.arange(0  ,     len(__a					)					)  ,     __a					)

         if self.config.use_karras_sigmas:
              UpperCAmelCase_			       =  self._convert_to_karras(in_sigmas=__a  ,     num_inference_steps=self.num_inference_steps					)
              UpperCAmelCase_			       =  np.array([self._sigma_to_t(__a  ,     __a					) for sigma in sigmas]					)

         UpperCAmelCase_			       =  np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
         UpperCAmelCase_			       =  torch.from_numpy(__a					).to(device=__a					)
         UpperCAmelCase_			       =  torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

         UpperCAmelCase_			       =  torch.from_numpy(__a					)
         UpperCAmelCase_			       =  torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

         if str(__a					).startswith("mps"					):
              # mps does not support float64
              UpperCAmelCase_			       =  timesteps.to(__a  ,     dtype=torch.floataa					)
         else:
              UpperCAmelCase_			       =  timesteps.to(device=__a					)

         # empty dt and derivative
         UpperCAmelCase_			       =  None
         UpperCAmelCase_			       =  None

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



    def    _lowercase		(self       :       int  ,     __a       :       Optional[Any]  ,     __a       :       List[str]					):
         # get log sigma
         UpperCAmelCase_			       =  np.log(__a					)

         # get distribution
         UpperCAmelCase_			       =  log_sigma - log_sigmas[:, np.newaxis]

         # get sigmas range
         UpperCAmelCase_			       =  np.cumsum((dists >= 0)  ,     axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
         UpperCAmelCase_			       =  low_idx + 1

         UpperCAmelCase_			       =  log_sigmas[low_idx]
         UpperCAmelCase_			       =  log_sigmas[high_idx]

         # interpolate sigmas
         UpperCAmelCase_			       =  (low - log_sigma) / (low - high)
         UpperCAmelCase_			       =  np.clip(__a  ,     0  ,     1					)

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



    def    _lowercase		(self       :       Dict  ,     __a       :       torch.FloatTensor  ,     __a       :       Optional[int]					):
         UpperCAmelCase_			       =  in_sigmas[-1].item()
         UpperCAmelCase_			       =  in_sigmas[0].item()

         UpperCAmelCase_			       =  7.0  # 7.0 is the value used in the paper
         UpperCAmelCase_			       =  np.linspace(0  ,     1  ,     __a					)
         UpperCAmelCase_			       =  sigma_min ** (1 / rho)
         UpperCAmelCase_			       =  sigma_max ** (1 / rho)
         UpperCAmelCase_			       =  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
         return sigmas



    @property
    def    _lowercase		(self       :       List[str]					):
         return self.dt is None



    def    _lowercase		(self       :       List[Any]  ,     __a       :       Union[torch.FloatTensor, np.ndarray]  ,     __a       :       Union[float, torch.FloatTensor]  ,     __a       :       Union[torch.FloatTensor, np.ndarray]  ,     __a       :       bool = True  ,     ):
         UpperCAmelCase_			       =  self.index_for_timestep(__a					)

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

         if self.state_in_first_order:
              UpperCAmelCase_			       =  self.sigmas[step_index]
              UpperCAmelCase_			       =  self.sigmas[step_index + 1]
         else:
              # 2nd order / Heun's method
              UpperCAmelCase_			       =  self.sigmas[step_index - 1]
              UpperCAmelCase_			       =  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
         UpperCAmelCase_			       =  0
         UpperCAmelCase_			       =  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":
              UpperCAmelCase_			       =  sigma_hat if self.state_in_first_order else sigma_next
              UpperCAmelCase_			       =  sample - sigma_input * model_output
         elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase_			       =  sigma_hat if self.state_in_first_order else sigma_next
              UpperCAmelCase_			       =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
         elif self.config.prediction_type == "sample":
              UpperCAmelCase_			       =  model_output
         else:
              raise ValueError(
                  f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""					)

         if self.config.clip_sample:
              UpperCAmelCase_			       =  pred_original_sample.clamp(
                  -self.config.clip_sample_range  ,     self.config.clip_sample_range					)

         if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              UpperCAmelCase_			       =  (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              UpperCAmelCase_			       =  sigma_next - sigma_hat

              # store for 2nd order step
              UpperCAmelCase_			       =  derivative
              UpperCAmelCase_			       =  dt
              UpperCAmelCase_			       =  sample
         else:
              # 2. 2nd order / Heun's method
              UpperCAmelCase_			       =  (sample - pred_original_sample) / sigma_next
              UpperCAmelCase_			       =  (self.prev_derivative + derivative) / 2

              # 3. take prev timestep & sample
              UpperCAmelCase_			       =  self.dt
              UpperCAmelCase_			       =  self.sample

              # free dt and derivative
              # Note, this puts the scheduler in "first order mode"
              UpperCAmelCase_			       =  None
              UpperCAmelCase_			       =  None
              UpperCAmelCase_			       =  None

         UpperCAmelCase_			       =  sample + derivative * dt

         if not return_dict:
              return (prev_sample,)

         return SchedulerOutput(prev_sample=__a					)



    def    _lowercase		(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
         UpperCAmelCase_			       =  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
              UpperCAmelCase_			       =  self.timesteps.to(original_samples.device  ,     dtype=torch.floataa					)
              UpperCAmelCase_			       =  timesteps.to(original_samples.device  ,     dtype=torch.floataa					)
         else:
              UpperCAmelCase_			       =  self.timesteps.to(original_samples.device					)
              UpperCAmelCase_			       =  timesteps.to(original_samples.device					)

         UpperCAmelCase_			       =  [self.index_for_timestep(__a  ,     __a					) for t in timesteps]

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

         UpperCAmelCase_			       =  original_samples + noise * sigma
         return noisy_samples






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









"""simple docstring"""







import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


A		=						importlib.util.find_spec('s3fs') is not None

if _has_safs:
			from .safilesystem import SaFileSystem  # noqa: F401

A		=						[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
			if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
						warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
			fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						if "://" in dataset_path:
												snake_case   :			List[str]						    = dataset_path.split("://" )[1]
						return dataset_path

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem ):
						"""simple docstring"""






						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							fsspec.AbstractFileSystem     ,		lowerCamelCase_:							str     ,		lowerCamelCase_:							str ):
						"""simple docstring"""






						snake_case   :			int						    = not is_remote_filesystem(lowerCamelCase_ )
						if is_local:
												# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
												shutil.move(fs._strip_protocol(lowerCamelCase_ )     ,		fs._strip_protocol(lowerCamelCase_ ) )
						else:
												fs.mv(lowerCamelCase_     ,		lowerCamelCase_     ,		recursive=lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			):
						"""simple docstring"""






						if hasattr(fsspec.asyn     ,		"reset_lock" ):
												# for future fsspec>2022.05.0
												fsspec.asyn.reset_lock()
						else:
												snake_case   :			Union[str, Any]						    = None
												snake_case   :			Optional[int]						    = None
												snake_case   :			List[str]						    = threading.Lock()



