"""simple docstring"""


import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      42
       SCREAMING_SNAKE_CASE_			=      None







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=0.999						,					UpperCAmelCase="cosine"						,					)				->      List[str]:
     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}'					)

     snake_case_					       =					[]
     for i in range(UpperCAmelCase					):
          snake_case_					       =					i / num_diffusion_timesteps
          snake_case_					       =					(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  UpperCamelCase					(				lowerCAmelCase__    ,		lowerCAmelCase__   ):







       @register_to_config
       def __init__( self,							lowerCAmelCase__ = 1000,							lowerCAmelCase__ = "fixed_small_log",							lowerCAmelCase__ = True,							lowerCAmelCase__ = 1.0,							lowerCAmelCase__ = "epsilon",							lowerCAmelCase__ = "squaredcos_cap_v2",							)      ->      int:
            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

            snake_case_					       =					betas_for_alpha_bar(lowerCAmelCase__)

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

            # standard deviation of the initial noise distribution
            snake_case_					       =					1.0

            # setable values
            snake_case_					       =					None
            snake_case_					       =					torch.from_numpy(np.arange(0,							lowerCAmelCase__)[::-1].copy())

            snake_case_					       =					variance_type







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      torch.FloatTensor:
            return sample







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = None)      ->      Any:
            snake_case_					       =					num_inference_steps
            snake_case_					       =					(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            snake_case_					       =					(np.arange(0,							lowerCAmelCase__) * step_ratio).round()[::-1].copy().astype(np.intaa)
            snake_case_					       =					torch.from_numpy(lowerCAmelCase__).to(lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None)      ->      List[str]:
            if prev_timestep is None:
                 snake_case_					       =					t - 1

            snake_case_					       =					self.alphas_cumprod[t]
            snake_case_					       =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            snake_case_					       =					1 - alpha_prod_t
            snake_case_					       =					1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 snake_case_					       =					self.betas[t]
            else:
                 snake_case_					       =					1 - alpha_prod_t / alpha_prod_t_prev

            # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
            # and sample from it to get previous sample
            # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
            snake_case_					       =					beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 snake_case_					       =					self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small_log":
                 snake_case_					       =					torch.log(torch.clamp(lowerCAmelCase__,							min=1e-20))
                 snake_case_					       =					torch.exp(0.5 * variance)
            elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 snake_case_					       =					variance.log()
                 snake_case_					       =					beta.log()

                 snake_case_					       =					(predicted_variance + 1) / 2
                 snake_case_					       =					frac * max_log + (1 - frac) * min_log

            return variance







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__ = None,							lowerCAmelCase__=None,							lowerCAmelCase__ = True,							)      ->      Union[UnCLIPSchedulerOutput, Tuple]:
            snake_case_					       =					timestep

            if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 snake_case_       ,	snake_case_					       =					torch.split(lowerCAmelCase__,							sample.shape[1],							dim=1)
            else:
                 snake_case_					       =					None

            # 1. compute alphas, betas
            if prev_timestep is None:
                 snake_case_					       =					t - 1

            snake_case_					       =					self.alphas_cumprod[t]
            snake_case_					       =					self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            snake_case_					       =					1 - alpha_prod_t
            snake_case_					       =					1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 snake_case_					       =					self.betas[t]
                 snake_case_					       =					self.alphas[t]
            else:
                 snake_case_					       =					1 - alpha_prod_t / alpha_prod_t_prev
                 snake_case_					       =					1 - beta

            # 2. compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
            if self.config.prediction_type == "epsilon":
                 snake_case_					       =					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 snake_case_					       =					model_output
            else:
                 raise ValueError(
                     f'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                     ' for the UnCLIPScheduler.')

            # 3. Clip "predicted x_0"
            if self.config.clip_sample:
                 snake_case_					       =					torch.clamp(
                     lowerCAmelCase__,							-self.config.clip_sample_range,							self.config.clip_sample_range)

            # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            snake_case_					       =					(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
            snake_case_					       =					alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

            # 5. Compute predicted previous sample µ_t
            # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
            snake_case_					       =					pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            snake_case_					       =					0
            if t > 0:
                 snake_case_					       =					randn_tensor(
                     model_output.shape,							dtype=model_output.dtype,							generator=lowerCAmelCase__,							device=model_output.device)

                 snake_case_					       =					self._get_variance(
                     lowerCAmelCase__,							predicted_variance=lowerCAmelCase__,							prev_timestep=lowerCAmelCase__,							)

                 if self.variance_type == "fixed_small_log":
                      snake_case_					       =					variance
                 elif self.variance_type == "learned_range":
                      snake_case_					       =					(0.5 * variance).exp()
                 else:
                      raise ValueError(
                          f'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                          ' for the UnCLIPScheduler.')

                 snake_case_					       =					variance * variance_noise

            snake_case_					       =					pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__,							pred_original_sample=lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							)      ->      torch.FloatTensor:
            # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
            snake_case_					       =					self.alphas_cumprod.to(device=original_samples.device,							dtype=original_samples.dtype)
            snake_case_					       =					timesteps.to(original_samples.device)

            snake_case_					       =					alphas_cumprod[timesteps] ** 0.5
            snake_case_					       =					sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
                 snake_case_					       =					sqrt_alpha_prod.unsqueeze(-1)

            snake_case_					       =					(1 - alphas_cumprod[timesteps]) ** 0.5
            snake_case_					       =					sqrt_one_minus_alpha_prod.flatten()
            while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
                 snake_case_					       =					sqrt_one_minus_alpha_prod.unsqueeze(-1)

            snake_case_					       =					sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
            return noisy_samples








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

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


_snake_case  :    Tuple						    =  importlib.util.find_spec('s3fs') is not None

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

_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   a_    ( lowerCAmelCase_      :			str	):
   if "://" in dataset_path:
      __lowerCAmelCase								=	dataset_path.split('://'	)[1]
   return dataset_path

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem	):
   if fs is not None and fs.protocol != "file":
      return True
   else:
      return False

def   a_    ( lowerCAmelCase_      :			fsspec.AbstractFileSystem,	lowerCAmelCase_      :			str,	lowerCAmelCase_      :			str	):
   __lowerCAmelCase								=	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   a_    ( ):
   if hasattr(fsspec.asyn,	'reset_lock'	):
      # for future fsspec>2022.05.0
      fsspec.asyn.reset_lock()
   else:
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	None
      __lowerCAmelCase								=	threading.Lock()

