

# 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 						_A     (				snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      torch.FloatTensor
  __lowerCamelCase			:      torch.FloatTensor







class 						_A     (				snake_case						,    snake_case				):





  '''simple docstring'''



  __lowerCamelCase			:      List[str]					=   1





  @register_to_config
  def __init__(  self   ,SCREAMING_SNAKE_CASE_ = 2000   ,SCREAMING_SNAKE_CASE_ = 0.15   ,SCREAMING_SNAKE_CASE_ = 0.01   ,SCREAMING_SNAKE_CASE_ = 13_48.0   ,SCREAMING_SNAKE_CASE_ = 1E-5   ,SCREAMING_SNAKE_CASE_ = 1   ,):

      '''simple docstring'''
      # standard deviation of the initial noise distribution
      snake_case							:		Union[str, Any]        =		sigma_max

      # setable values
      snake_case							:		Any        =		None

      self.set_sigmas(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      return sample





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		Any        =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

      snake_case							:		str        =		torch.linspace(1   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,device=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None    ):

      '''simple docstring'''
      snake_case							:		List[Any]        =		sigma_min if sigma_min is not None else self.config.sigma_min
      snake_case							:		str        =		sigma_max if sigma_max is not None else self.config.sigma_max
      snake_case							:		Any        =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if self.timesteps is None:
          self.set_timesteps(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    )

      snake_case							:		str        =		sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
      snake_case							:		List[Any]        =		torch.exp(torch.linspace(math.log(SCREAMING_SNAKE_CASE_    )   ,math.log(SCREAMING_SNAKE_CASE_    )   ,SCREAMING_SNAKE_CASE_    )    )
      snake_case							:		Optional[Any]        =		torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ):

      '''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   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 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"""    )

      snake_case							:		Optional[Any]        =		timestep * torch.ones(
          sample.shape[0]   ,device=sample.device    )  # torch.repeat_interleave(timestep, sample.shape[0])
      snake_case							:		List[str]        =		(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
      snake_case							:		Dict        =		timesteps.to(self.discrete_sigmas.device    )

      snake_case							:		Any        =		self.discrete_sigmas[timesteps].to(sample.device    )
      snake_case							:		str        =		self.get_adjacent_sigma(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ).to(sample.device    )
      snake_case							:		Tuple        =		torch.zeros_like(SCREAMING_SNAKE_CASE_    )
      snake_case							:		Optional[Any]        =		(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
      snake_case							:		Tuple        =		diffusion.flatten()
      while len(diffusion.shape    ) < len(sample.shape    ):
          snake_case							:		Dict        =		diffusion.unsqueeze(-1    )
      snake_case							:		List[Any]        =		drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      snake_case							:		Tuple        =		randn_tensor(
          sample.shape   ,layout=sample.layout   ,generator=SCREAMING_SNAKE_CASE_   ,device=sample.device   ,dtype=sample.dtype    )
      snake_case							:		Tuple        =		sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      snake_case							:		List[str]        =		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=SCREAMING_SNAKE_CASE_   ,prev_sample_mean=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = 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
      snake_case							:		Dict        =		randn_tensor(sample.shape   ,layout=sample.layout   ,generator=SCREAMING_SNAKE_CASE_    ).to(sample.device    )

      # compute step size from the model_output, the noise, and the snr
      snake_case							:		List[str]        =		torch.norm(model_output.reshape(model_output.shape[0]   ,-1    )   ,dim=-1    ).mean()
      snake_case							:		List[Any]        =		torch.norm(noise.reshape(noise.shape[0]   ,-1    )   ,dim=-1    ).mean()
      snake_case							:		str        =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      snake_case							:		int        =		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
      snake_case							:		Optional[int]        =		step_size.flatten()
      while len(step_size.shape    ) < len(sample.shape    ):
          snake_case							:		Union[str, Any]        =		step_size.unsqueeze(-1    )
      snake_case							:		Optional[Any]        =		sample + step_size * model_output
      snake_case							:		Any        =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
          return (prev_sample,)

      return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_    )





  def    snake_case_       (  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_   ,):

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





  def __len__(  self    ):

      '''simple docstring'''
      return self.config.num_train_timesteps




'''simple docstring'''






import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_UpperCAmelCase    :     Optional[int]         =       '''\
    Text data.
    Second line of data.'''

_UpperCAmelCase    :     Optional[Any]         =       '''file'''





@pytest.fixture(scope='session'				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  tmp_path_factory.mktemp('data'				) / (FILE_PATH + '.zstd')
     _A    		=  bytes(__snake_case   ,		'utf-8'				)
     with zstd.open(__snake_case   ,		'wb'				) as f:
          f.write(__snake_case				)
     return path





@pytest.fixture
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     with open(os.path.join(tmpfs.local_root_dir   ,		__snake_case				)   ,		'w'				) as f:
          f.write(__snake_case				)
     return FILE_PATH





@pytest.mark.parametrize('compression_format'   ,		['gzip', 'xz', 'zstd']				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Dict   ,		__snake_case							:			str   ,		__snake_case							:			Any   ,		__snake_case							:			Any   ,		__snake_case							:			List[str]   ,		__snake_case							:			List[str]				):
     _A    		=  {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_path}
     _A    		=  input_paths[compression_format]
     _A    		=  tmp_path / 'cache'
     _A    		=  DownloadConfig(cache_dir=__snake_case   ,		extract_compressed_file=__snake_case				)
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert extracted_file_content == expected_file_content





@pytest.mark.parametrize('default_extracted'   ,		[True, False]				)
@pytest.mark.parametrize('default_cache_dir'   ,		[True, False]				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]   ,		__snake_case							:			List[str]   ,		__snake_case							:			Union[str, Any]   ,		__snake_case							:			int   ,		__snake_case							:			List[str]				):
     _A    		=  'custom_cache'
     _A    		=  'custom_extracted_dir'
     _A    		=  tmp_path / 'custom_extracted_path'
     if default_extracted:
          _A    		=  ('downloads' if default_cache_dir else custom_cache_dir, 'extracted')
     else:
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'   ,		__snake_case				)
          monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'   ,		str(__snake_case				)				)
          _A    		=  custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

     _A    		=  xz_file
     _A    		=  (
         DownloadConfig(extract_compressed_file=__snake_case				)
         if default_cache_dir
         else DownloadConfig(cache_dir=tmp_path / custom_cache_dir   ,		extract_compressed_file=__snake_case				)
     )
     _A    		=  cached_path(__snake_case   ,		download_config=__snake_case				)
     assert Path(__snake_case				).parent.parts[-2:] == expected





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str				):
     # absolute path
     _A    		=  str(Path(__snake_case				).resolve()				)
     assert cached_path(__snake_case				) == text_file
     # relative path
     _A    		=  str(Path(__snake_case				).resolve().relative_to(Path(os.getcwd()				)				)				)
     assert cached_path(__snake_case				) == text_file





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     # absolute path
     _A    		=  str(tmp_path.resolve() / '__missing_file__.txt'				)
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)
     # relative path
     _A    		=  './__missing_file__.txt'
     with pytest.raises(__snake_case				):
          cached_path(__snake_case				)





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  get_from_cache(F'tmp://{tmpfs_file}'				)
     with open(__snake_case				) as f:
          _A    		=  f.read()
     assert output_file_content == FILE_CONTENT





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      ):
     with pytest.raises(__snake_case				):
          cached_path('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Tuple				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          http_get('https://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          http_head('https://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          ftp_get('ftp://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          ftp_head('ftp://huggingface.co'				)





@patch('datasets.config.HF_DATASETS_OFFLINE'   ,		__snake_case				)
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			List[str]				):
     _A    		=  tmp_path_factory.mktemp('data'				) / 'file.html'
     with pytest.raises(__snake_case				):
          fsspec_get('s3://huggingface.co'   ,		temp_file=__snake_case				)
     with pytest.raises(__snake_case				):
          fsspec_head('s3://huggingface.co'				)



