


'''simple docstring'''

# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import torch

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





@dataclass
class   snake_case					(					lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						42
  _lowerCamelCase						=						42





class   snake_case					(					lowercase    ,		lowercase    ):





  """simple docstring"""




  _lowerCamelCase						=						1






  @register_to_config
  def __init__(					self    ,						UpperCamelCase = 2000    ,						UpperCamelCase = 0.15    ,						UpperCamelCase = 0.01    ,						UpperCamelCase = 1_348.0    ,						UpperCamelCase = 1e-5    ,						UpperCamelCase = 1    ,						):







   """simple docstring"""
   # standard deviation of the initial noise distribution
   lowerCamelCase_  				=						sigma_max

   # setable values
   lowerCamelCase_  				=						None

   self.set_sigmas(UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  )






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







   """simple docstring"""
   return sample






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







   """simple docstring"""
   lowerCamelCase_  				=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

   lowerCamelCase_  				=						torch.linspace(1    ,						UpperCamelCase    ,						UpperCamelCase    ,						device=UpperCamelCase  )






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







   """simple docstring"""
   lowerCamelCase_  				=						sigma_min if sigma_min is not None else self.config.sigma_min
   lowerCamelCase_  				=						sigma_max if sigma_max is not None else self.config.sigma_max
   lowerCamelCase_  				=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
   if self.timesteps is None:
    self.set_timesteps(UpperCamelCase    ,						UpperCamelCase  )

   lowerCamelCase_  				=						sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
   lowerCamelCase_  				=						torch.exp(torch.linspace(math.log(UpperCamelCase  )    ,						math.log(UpperCamelCase  )    ,						UpperCamelCase  )  )
   lowerCamelCase_  				=						torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return torch.where(
       timesteps == 0    ,						torch.zeros_like(t.to(timesteps.device  )  )    ,						self.discrete_sigmas[timesteps - 1].to(timesteps.device  )    ,						)






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = True    ,						):







   """simple docstring"""
   if self.timesteps is None:
    raise ValueError(
        "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

   lowerCamelCase_  				=						timestep * torch.ones(
       sample.shape[0]    ,						device=sample.device  )  # torch.repeat_interleave(timestep, sample.shape[0])
   lowerCamelCase_  				=						(timestep * (len(self.timesteps  ) - 1)).long()

   # mps requires indices to be in the same device, so we use cpu as is the default with cuda
   lowerCamelCase_  				=						timesteps.to(self.discrete_sigmas.device  )

   lowerCamelCase_  				=						self.discrete_sigmas[timesteps].to(sample.device  )
   lowerCamelCase_  				=						self.get_adjacent_sigma(UpperCamelCase    ,						UpperCamelCase  ).to(sample.device  )
   lowerCamelCase_  				=						torch.zeros_like(UpperCamelCase  )
   lowerCamelCase_  				=						(sigma**2 - adjacent_sigma**2) ** 0.5

   # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
   # also equation 47 shows the analog from SDE models to ancestral sampling methods
   lowerCamelCase_  				=						diffusion.flatten()
   while len(diffusion.shape  ) < len(sample.shape  ):
    lowerCamelCase_  				=						diffusion.unsqueeze(-1  )
   lowerCamelCase_  				=						drift - diffusion**2 * model_output

   #  equation 6: sample noise for the diffusion term of
   lowerCamelCase_  				=						randn_tensor(
       sample.shape    ,						layout=sample.layout    ,						generator=UpperCamelCase    ,						device=sample.device    ,						dtype=sample.dtype  )
   lowerCamelCase_  				=						sample - drift  # subtract because `dt` is a small negative timestep
   # TODO is the variable diffusion the correct scaling term for the noise?
   lowerCamelCase_  				=						prev_sample_mean + diffusion * noise  # add impact of diffusion field g

   if not return_dict:
    return (prev_sample, prev_sample_mean)

   return SdeVeOutput(prev_sample=UpperCamelCase    ,						prev_sample_mean=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = True    ,						):







   """simple docstring"""
   if self.timesteps is None:
    raise ValueError(
        "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"  )

   # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
   # sample noise for correction
   lowerCamelCase_  				=						randn_tensor(sample.shape    ,						layout=sample.layout    ,						generator=UpperCamelCase  ).to(sample.device  )

   # compute step size from the model_output, the noise, and the snr
   lowerCamelCase_  				=						torch.norm(model_output.reshape(model_output.shape[0]    ,						-1  )    ,						dim=-1  ).mean()
   lowerCamelCase_  				=						torch.norm(noise.reshape(noise.shape[0]    ,						-1  )    ,						dim=-1  ).mean()
   lowerCamelCase_  				=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
   lowerCamelCase_  				=						step_size * torch.ones(sample.shape[0]  ).to(sample.device  )
   # self.repeat_scalar(step_size, sample.shape[0])

   # compute corrected sample: model_output term and noise term
   lowerCamelCase_  				=						step_size.flatten()
   while len(step_size.shape  ) < len(sample.shape  ):
    lowerCamelCase_  				=						step_size.unsqueeze(-1  )
   lowerCamelCase_  				=						sample + step_size * model_output
   lowerCamelCase_  				=						prev_sample_mean + ((step_size * 2) ** 0.5) * noise

   if not return_dict:
    return (prev_sample,)

   return SchedulerOutput(prev_sample=UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						):







   """simple docstring"""
   # Make sure sigmas and timesteps have the same device and dtype as original_samples
   lowerCamelCase_  				=						timesteps.to(original_samples.device  )
   lowerCamelCase_  				=						self.discrete_sigmas.to(original_samples.device  )[timesteps]
   lowerCamelCase_  				=						(
       noise * sigmas[:, None, None, None]
       if noise is not None
       else torch.randn_like(UpperCamelCase  ) * sigmas[:, None, None, None]
   )
   lowerCamelCase_  				=						noise + original_samples
   return noisy_samples







  def __len__(					self  ):







   """simple docstring"""
   return self.config.num_train_timesteps







'''simple docstring'''

# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


a_						:       Dict      			=				get_logger(__name__)





class   snake_case					:





  """simple docstring"""




  _lowerCamelCase						=						"dummy_data"
  _lowerCamelCase						=						"datasets"
  _lowerCamelCase						=						False






  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase = None    ,						UpperCamelCase = False    ,						UpperCamelCase = True    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						0
   lowerCamelCase_  				=						dataset_name
   lowerCamelCase_  				=						cache_dir
   lowerCamelCase_  				=						use_local_dummy_data
   lowerCamelCase_  				=						config
   # download_callbacks take a single url as input
   lowerCamelCase_  				=						download_callbacks or []
   # if False, it doesn't load existing files and it returns the paths of the dummy files relative
   # to the dummy_data zip file root
   lowerCamelCase_  				=						load_existing_dummy_data

   # TODO(PVP, QL) might need to make this more general
   lowerCamelCase_  				=						str(UpperCamelCase  )
   # to be downloaded
   lowerCamelCase_  				=						None
   lowerCamelCase_  				=						None






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._dummy_file is None:
    lowerCamelCase_  				=						self.download_dummy_data()
   return self._dummy_file






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.config is not None:
    # structure is dummy / config_name / version_name
    return os.path.join("dummy"    ,						self.config.name    ,						self.version_name  )
   # structure is dummy / version_name
   return os.path.join("dummy"    ,						self.version_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.dummy_data_folder    ,						"dummy_data.zip"  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						(
       self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
   )

   lowerCamelCase_  				=						cached_path(
       UpperCamelCase    ,						cache_dir=self.cache_dir    ,						extract_compressed_file=UpperCamelCase    ,						force_extract=UpperCamelCase  )

   return os.path.join(UpperCamelCase    ,						self.dummy_file_name  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return os.path.join(self.datasets_scripts_dir    ,						self.dataset_name    ,						self.dummy_zip_file  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self._bucket_url is None:
    lowerCamelCase_  				=						hf_github_url(self.dataset_name    ,						self.dummy_zip_file.replace(os.sep    ,						"/"  )  )
   return self._bucket_url






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   # return full path if its a dir
   if os.path.isdir(self.dummy_file  ):
    return self.dummy_file
   # else cut off path to file -> example `xsum`.
   return "/".join(self.dummy_file.replace(os.sep    ,						"/"  ).split("/"  )[:-1]  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   if self.load_existing_dummy_data:
    # dummy data is downloaded and tested
    lowerCamelCase_  				=						self.dummy_file
   else:
    # dummy data cannot be downloaded and only the path to dummy file is returned
    lowerCamelCase_  				=						self.dummy_file_name

   # special case when data_url is a dict
   if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    return self.create_dummy_data_dict(UpperCamelCase    ,						UpperCamelCase  )
   elif isinstance(UpperCamelCase    ,						(list, tuple)  ):
    return self.create_dummy_data_list(UpperCamelCase    ,						UpperCamelCase  )
   else:
    return self.create_dummy_data_single(UpperCamelCase    ,						UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   return self.download_and_extract(UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return path






  def 						snake_case	(					self  ):







   """simple docstring"""
   return {}






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   for key, single_urls in data_url.items():
    for download_callback in self.download_callbacks:
     if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
      for single_url in single_urls:
       download_callback(UpperCamelCase  )
     else:
      lowerCamelCase_  				=						single_urls
      download_callback(UpperCamelCase  )
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    if isinstance(UpperCamelCase    ,						UpperCamelCase  ):
     lowerCamelCase_  				=						[os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  ) for x in single_urls]
    else:
     lowerCamelCase_  				=						single_urls
     lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(Path(UpperCamelCase  ).name  )  )
    lowerCamelCase_  				=						value

   # make sure that values are unique
   if all(isinstance(UpperCamelCase    ,						UpperCamelCase  ) for i in dummy_data_dict.values()  ) and len(set(dummy_data_dict.values()  )  ) < len(
       dummy_data_dict.values()  ):
    # append key to value to make its name unique
    lowerCamelCase_  				=						{key: value + key for key, value in dummy_data_dict.items()}

   return dummy_data_dict






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
   lowerCamelCase_  				=						all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"    ,						UpperCamelCase  )  ) for url in data_url  )
   lowerCamelCase_  				=						all(
       url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"  ) for url in data_url  )
   if data_url and (is_tf_records or is_pubmed_records):
    lowerCamelCase_  				=						[data_url[0]] * len(UpperCamelCase  )
   for single_url in data_url:
    for download_callback in self.download_callbacks:
     download_callback(UpperCamelCase  )
    # we force the name of each key to be the last file / folder name of the url path
    # if the url has arguments, we need to encode them with urllib.parse.quote_plus
    lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(single_url.split("/"  )[-1]  )  )
    dummy_data_list.append(UpperCamelCase  )
   return dummy_data_list






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   for download_callback in self.download_callbacks:
    download_callback(UpperCamelCase  )
   # we force the name of each key to be the last file / folder name of the url path
   # if the url has arguments, we need to encode them with urllib.parse.quote_plus
   lowerCamelCase_  				=						os.path.join(UpperCamelCase    ,						urllib.parse.quote_plus(data_url.split("/"  )[-1]  )  )
   if os.path.exists(UpperCamelCase  ) or not self.load_existing_dummy_data:
    return value
   else:
    # Backward compatibility, maybe deprecate at one point.
    # For many datasets with single url calls to dl_manager.download_and_extract,
    # the dummy_data.zip file is actually the zipped downloaded file
    # while now we expected the dummy_data.zip file to be a directory containing
    # the downloaded file.
    return path_to_dummy_data






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self  ):







   """simple docstring"""
   pass






  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   def _iter_archive_members(UpperCamelCase  ):
    # this preserves the order of the members inside the ZIP archive
    lowerCamelCase_  				=						Path(self.dummy_file  ).parent
    lowerCamelCase_  				=						path.relative_to(UpperCamelCase  )
    with ZipFile(self.local_path_to_dummy_data  ) as zip_file:
     lowerCamelCase_  				=						zip_file.namelist()
    for member in members:
     if member.startswith(relative_path.as_posix()  ):
      yield dummy_parent_path.joinpath(UpperCamelCase  )

   lowerCamelCase_  				=						Path(UpperCamelCase  )
   lowerCamelCase_  				=						_iter_archive_members(UpperCamelCase  ) if self.use_local_dummy_data else path.rglob("*"  )
   for file_path in file_paths:
    if file_path.is_file() and not file_path.name.startswith((".", "__")  ):
     yield file_path.relative_to(UpperCamelCase  ).as_posix(), file_path.open("rb"  )







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   if not isinstance(UpperCamelCase    ,						UpperCamelCase  ):
    lowerCamelCase_  				=						[paths]
   for path in paths:
    if os.path.isfile(UpperCamelCase  ):
     if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
      return
     yield path
    else:
     for dirpath, dirnames, filenames in os.walk(UpperCamelCase  ):
      if os.path.basename(UpperCamelCase  ).startswith((".", "__")  ):
       continue
      dirnames.sort()
      for filename in sorted(UpperCamelCase  ):
       if filename.startswith((".", "__")  ):
        continue
       yield os.path.join(UpperCamelCase    ,						UpperCamelCase  )



