from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			8
def  snake_case						(__lowercase ,      __lowercase=BITS						)	->	List[str]:

      '''simple docstring'''



      _snake_case						:					str    =   x.device

      _snake_case						:					Optional[Any]    =   (x * 255).int().clamp(0 ,      255						)

      _snake_case						:					Tuple    =   2 ** torch.arange(bits - 1 ,      -1 ,      -1 ,      device=__lowercase						)
      _snake_case						:					Dict    =   rearrange(__lowercase ,      "d -> d 1 1"						)
      _snake_case						:					Optional[Any]    =   rearrange(__lowercase ,      "b c h w -> b c 1 h w"						)

      _snake_case						:					List[str]    =   ((x & mask) != 0).float()
      _snake_case						:					Optional[int]    =   rearrange(__lowercase ,      "b c d h w -> b (c d) h w"						)
      _snake_case						:					List[Any]    =   bits * 2 - 1
      return bits
def  snake_case						(__lowercase ,      __lowercase=BITS						)	->	Any:

      '''simple docstring'''



      _snake_case						:					Dict    =   x.device

      _snake_case						:					Optional[int]    =   (x > 0).int()
      _snake_case						:					Any    =   2 ** torch.arange(bits - 1 ,      -1 ,      -1 ,      device=__lowercase ,      dtype=torch.intaa						)

      _snake_case						:					Tuple    =   rearrange(__lowercase ,      "d -> d 1 1"						)
      _snake_case						:					Dict    =   rearrange(__lowercase ,      "b (c d) h w -> b c d h w" ,      d=8						)
      _snake_case						:					Dict    =   reduce(x * mask ,      "b c d h w -> b c h w" ,      "sum"						)
      return (dec / 255).clamp(0.0 ,      1.0						)
def  snake_case						(self ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase = 0.0 ,      __lowercase = True ,      __lowercase=None ,      __lowercase = True ,      )	->	Union[DDIMSchedulerOutput, Tuple]:

      '''simple docstring'''



      if self.num_inference_steps is None:
            raise ValueError(
                "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"						)

      # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
      # Ideally, read DDIM paper in-detail understanding

      # Notation (<variable name> -> <name in paper>
      # - pred_noise_t -> e_theta(x_t, t)
      # - pred_original_sample -> f_theta(x_t, t) or x_0
      # - std_dev_t -> sigma_t
      # - eta -> η
      # - pred_sample_direction -> "direction pointing to x_t"
      # - pred_prev_sample -> "x_t-1"

      # 1. get previous step value (=t-1)
      _snake_case						:					int    =   timestep - self.config.num_train_timesteps // self.num_inference_steps

      # 2. compute alphas, betas
      _snake_case						:					List[Any]    =   self.alphas_cumprod[timestep]
      _snake_case						:					Optional[Any]    =   self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

      _snake_case						:					str    =   1 - alpha_prod_t

      # 3. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _snake_case						:					Tuple    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

      # 4. Clip "predicted x_0"
      _snake_case						:					Dict    =   self.bit_scale
      if self.config.clip_sample:
            _snake_case						:					Dict    =   torch.clamp(__lowercase ,      -scale ,      __lowercase						)

      # 5. compute variance: "sigma_t(η)" -> see formula (16)
      # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
      _snake_case						:					Optional[Any]    =   self._get_variance(__lowercase ,      __lowercase						)
      _snake_case						:					Optional[int]    =   eta * variance ** 0.5

      if use_clipped_model_output:
            # the model_output is always re-derived from the clipped x_0 in Glide
            _snake_case						:					Tuple    =   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

      # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _snake_case						:					Optional[int]    =   (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

      # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
      _snake_case						:					Tuple    =   alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

      if eta > 0:
            # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
            _snake_case						:					str    =   model_output.device if torch.is_tensor(__lowercase						) else "cpu"
            _snake_case						:					Optional[int]    =   torch.randn(model_output.shape ,      dtype=model_output.dtype ,      generator=__lowercase						).to(__lowercase						)
            _snake_case						:					List[Any]    =   self._get_variance(__lowercase ,      __lowercase						) ** 0.5 * eta * noise

            _snake_case						:					Optional[Any]    =   prev_sample + variance

      if not return_dict:
            return (prev_sample,)

      return DDIMSchedulerOutput(prev_sample=__lowercase ,      pred_original_sample=__lowercase						)
def  snake_case						(self ,      __lowercase ,      __lowercase ,      __lowercase ,      __lowercase="epsilon" ,      __lowercase=None ,      __lowercase = True ,      )	->	Union[DDPMSchedulerOutput, Tuple]:

      '''simple docstring'''



      _snake_case						:					List[str]    =   timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
            _snake_case						:					Optional[int]    =   torch.split(__lowercase ,      sample.shape[1] ,      dim=1						)
      else:
            _snake_case						:					str    =   None

      # 1. compute alphas, betas
      _snake_case						:					Optional[Any]    =   self.alphas_cumprod[t]
      _snake_case						:					int    =   self.alphas_cumprod[t - 1] if t > 0 else self.one
      _snake_case						:					Optional[Any]    =   1 - alpha_prod_t
      _snake_case						:					Any    =   1 - alpha_prod_t_prev

      # 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 prediction_type == "epsilon":
            _snake_case						:					Dict    =   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif prediction_type == "sample":
            _snake_case						:					Optional[Any]    =   model_output
      else:
            raise ValueError(F"""Unsupported prediction_type {prediction_type}."""						)

      # 3. Clip "predicted x_0"
      _snake_case						:					List[Any]    =   self.bit_scale
      if self.config.clip_sample:
            _snake_case						:					int    =   torch.clamp(__lowercase ,      -scale ,      __lowercase						)

      # 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						:					int    =   (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
      _snake_case						:					Tuple    =   self.alphas[t] ** 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						:					Union[str, Any]    =   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      _snake_case						:					str    =   0
      if t > 0:
            _snake_case						:					List[str]    =   torch.randn(
                model_output.size() ,      dtype=model_output.dtype ,      layout=model_output.layout ,      generator=__lowercase						).to(model_output.device						)
            _snake_case						:					List[Any]    =   (self._get_variance(__lowercase ,      predicted_variance=__lowercase						) ** 0.5) * noise

      _snake_case						:					Optional[Any]    =   pred_prev_sample + variance

      if not return_dict:
            return (pred_prev_sample,)

      return DDPMSchedulerOutput(prev_sample=__lowercase ,      pred_original_sample=__lowercase						)



class        lowercase_   (					__snake_case		):



       def __init__(						self				,    lowercase_				,    lowercase_				,    lowercase_ = 1.0				,    ):
             super().__init__()
             _snake_case						:					Union[str, Any]    =   bit_scale
             _snake_case						:					List[str]    =   (
                 ddim_bit_scheduler_step if isinstance(lowercase_				,    lowercase_    ) else ddpm_bit_scheduler_step
             )

             self.register_modules(unet=lowercase_				,    scheduler=lowercase_    )



       @torch.no_grad()
       def __call__(						self				,    lowercase_ = 256				,    lowercase_ = 256				,    lowercase_ = 50				,    lowercase_ = None				,    lowercase_ = 1				,    lowercase_ = "pil"				,    lowercase_ = True				,    **lowercase_				,    ):
             _snake_case						:					Dict    =   torch.randn(
                 (batch_size, self.unet.config.in_channels, height, width)				,    generator=lowercase_				,    )
             _snake_case						:					Optional[int]    =   decimal_to_bits(lowercase_    ) * self.bit_scale
             _snake_case						:					Union[str, Any]    =   latents.to(self.device    )

             self.scheduler.set_timesteps(lowercase_    )

             for t in self.progress_bar(self.scheduler.timesteps    ):
                   # predict the noise residual
                   _snake_case						:					int    =   self.unet(lowercase_				,    lowercase_    ).sample

                   # compute the previous noisy sample x_t -> x_t-1
                   _snake_case						:					Any    =   self.scheduler.step(lowercase_				,    lowercase_				,    lowercase_    ).prev_sample

             _snake_case						:					Dict    =   bits_to_decimal(lowercase_    )

             if output_type == "pil":
                   _snake_case						:					Optional[Any]    =   self.numpy_to_pil(lowercase_    )

             if not return_dict:
                   return (image,)

             return ImagePipelineOutput(images=lowercase_    )
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()