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  snake_case						(__lowercase ,      __lowercase=0.999 ,      __lowercase="cosine" ,      )	->	Any:

      '''simple docstring'''



      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      _snake_case						:					Dict    =   []
      for i in range(lowerCamelCase_						):
            _snake_case						:					List[Any]    =   i / num_diffusion_timesteps
            _snake_case						:					Dict    =   (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_   (					lowerCamelCase__						,			lowerCamelCase__		):
       _lowerCamelCase						=		[e.name for e in KarrasDiffusionSchedulers]
       _lowerCamelCase						=		2



       @register_to_config
       def __init__(						self				,    lowercase_ = 1_000				,    lowercase_ = 0.00_085				,    lowercase_ = 0.012				,    lowercase_ = "linear"				,    lowercase_ = None				,    lowercase_ = "epsilon"				,    lowercase_ = "linspace"				,    lowercase_ = 0				,    ):






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

             _snake_case						:					int    =   1.0 - self.betas
             _snake_case						:					int    =   torch.cumprod(self.alphas				,    dim=0    )

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



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_=None    ):






             if schedule_timesteps is None:
                   _snake_case						:					Tuple    =   self.timesteps

             _snake_case						:					Dict    =   (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:
                   _snake_case						:					Union[str, Any]    =   1 if len(__lowerCamelCase    ) > 1 else 0
             else:
                   _snake_case						:					List[str]    =   timestep.cpu().item() if torch.is_tensor(__lowerCamelCase    ) else timestep
                   _snake_case						:					Optional[int]    =   self._index_counter[timestep_int]

             return indices[pos].item()



       @property
       def 	UpperCamelCase	(						self    ):






             if self.config.timestep_spacing in ["linspace", "trailing"]:
                   return self.sigmas.max()

             return (self.sigmas.max() ** 2 + 1) ** 0.5



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    ):
             _snake_case						:					Optional[Any]    =   self.index_for_timestep(__lowerCamelCase    )

             if self.state_in_first_order:
                   _snake_case						:					Union[str, Any]    =   self.sigmas[step_index]
             else:
                   _snake_case						:					Tuple    =   self.sigmas_interpol[step_index]

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



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    ):
             _snake_case						:					List[Any]    =   num_inference_steps

             _snake_case						:					Optional[Any]    =   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":
                   _snake_case						:					Dict    =   np.linspace(0				,    num_train_timesteps - 1				,    __lowerCamelCase				,    dtype=__lowerCamelCase    )[::-1].copy()
             elif self.config.timestep_spacing == "leading":
                   _snake_case						:					Union[str, 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
                   _snake_case						:					Tuple    =   (np.arange(0				,    __lowerCamelCase    ) * step_ratio).round()[::-1].copy().astype(__lowerCamelCase    )
                   timesteps += self.config.steps_offset
             elif self.config.timestep_spacing == "trailing":
                   _snake_case						:					Tuple    =   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
                   _snake_case						:					Any    =   (np.arange(__lowerCamelCase				,    0				,    -step_ratio    )).round().copy().astype(__lowerCamelCase    )
                   timesteps -= 1
             else:
                   raise ValueError(
                       f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

             _snake_case						:					List[Any]    =   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
             _snake_case						:					str    =   torch.from_numpy(np.log(__lowerCamelCase    )    ).to(__lowerCamelCase    )

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

             # interpolate sigmas
             _snake_case						:					int    =   sigmas.log().lerp(sigmas.roll(1    ).log()				,    0.5    ).exp()

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

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

             # interpolate timesteps
             _snake_case						:					Dict    =   self.sigma_to_t(__lowerCamelCase    ).to(__lowerCamelCase				,    dtype=timesteps.dtype    )
             _snake_case						:					Tuple    =   torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])				,    dim=-1    ).flatten()

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

             _snake_case						:					List[Any]    =   None

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



       def 	UpperCamelCase	(						self				,    lowercase_    ):
             _snake_case						:					Tuple    =   sigma.log()

             # get distribution
             _snake_case						:					Optional[int]    =   log_sigma - self.log_sigmas[:, None]

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

             _snake_case						:					Dict    =   self.log_sigmas[low_idx]
             _snake_case						:					Dict    =   self.log_sigmas[high_idx]

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

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



       @property
       def 	UpperCamelCase	(						self    ):
             return self.sample is None



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    lowercase_ = True				,    ):
             _snake_case						:					Union[str, Any]    =   self.index_for_timestep(__lowerCamelCase    )

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

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

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

                   # 3. delta timestep
                   _snake_case						:					Any    =   sigma_next - sigma_hat

                   _snake_case						:					Dict    =   self.sample
                   _snake_case						:					Optional[Any]    =   None

             _snake_case						:					Union[str, Any]    =   sample + derivative * dt

             if not return_dict:
                   return (prev_sample,)

             return SchedulerOutput(prev_sample=__lowerCamelCase    )



       def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_				,    ):
             _snake_case						:					Dict    =   self.sigmas.to(device=original_samples.device				,    dtype=original_samples.dtype    )
             if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCamelCase    ):
                   # mps does not support float64
                   _snake_case						:					Any    =   self.timesteps.to(original_samples.device				,    dtype=torch.floataa    )
                   _snake_case						:					Union[str, Any]    =   timesteps.to(original_samples.device				,    dtype=torch.floataa    )
             else:
                   _snake_case						:					str    =   self.timesteps.to(original_samples.device    )
                   _snake_case						:					Optional[Any]    =   timesteps.to(original_samples.device    )

             _snake_case						:					Tuple    =   [self.index_for_timestep(__lowerCamelCase				,    __lowerCamelCase    ) for t in timesteps]

             _snake_case						:					Optional[Any]    =   sigmas[step_indices].flatten()
             while len(sigma.shape    ) < len(original_samples.shape    ):
                   _snake_case						:					int    =   sigma.unsqueeze(-1    )

             _snake_case						:					Union[str, Any]    =   original_samples + noise * sigma
             return noisy_samples



       def __len__(						self    ):
             return self.config.num_train_timesteps
import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


__SCREAMING_SNAKE_CASE						:   Any         =			importlib.util.find_spec('s3fs') is not None

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

__SCREAMING_SNAKE_CASE						:   List[compression.BaseCompressedFileFileSystem]         =			[
    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  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						if "://" in dataset_path:
												_snake_case						:					Tuple    =   dataset_path.split("://"						)[1]
						return dataset_path
def  snake_case						(__lowercase						)	->	bool:

						'''simple docstring'''



						if fs is not None and fs.protocol != "file":
												return True
						else:
												return False
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	str:

						'''simple docstring'''



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

def  snake_case						()	->	None:

						'''simple docstring'''



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