

'''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,
)


_lowerCAmelCase         =						"\\n    Text data.\n    Second line of data."

_lowerCAmelCase         =						"file"




@pytest.fixture(scope='''session'''			)
def 						UpperCamelCase	(			a			)				->     Dict:






     '''simple docstring'''
     __magic_name__						       =    tmp_path_factory.mktemp('''data'''			) / (FILE_PATH + '''.zstd''')
     __magic_name__						       =    bytes(a		,						'''utf-8'''			)
     with zstd.open(a		,						'''wb'''			) as f:
          f.write(a			)
     return path




@pytest.fixture
def 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     with open(os.path.join(tmpfs.local_root_dir		,						a			)		,						'''w'''			) as f:
          f.write(a			)
     return FILE_PATH




@pytest.mark.parametrize('''compression_format'''		,						['''gzip''', '''xz''', '''zstd''']			)
def 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a		,						a			)				->     Optional[int]:






     '''simple docstring'''
     __magic_name__						       =    {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
     __magic_name__						       =    input_paths[compression_format]
     __magic_name__						       =    tmp_path / '''cache'''
     __magic_name__						       =    DownloadConfig(cache_dir=a		,						extract_compressed_file=a			)
     __magic_name__						       =    cached_path(a		,						download_config=a			)
     with open(a			) as f:
          __magic_name__						       =    f.read()
     with open(a			) as f:
          __magic_name__						       =    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 						UpperCamelCase	(			a		,						a		,						a		,						a		,						a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    '''custom_cache'''
     __magic_name__						       =    '''custom_extracted_dir'''
     __magic_name__						       =    tmp_path / '''custom_extracted_path'''
     if default_extracted:
          __magic_name__						       =    ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
     else:
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''		,						a			)
          monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''		,						str(a			)			)
          __magic_name__						       =    custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

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




def 						UpperCamelCase	(			a			)				->     List[Any]:






     '''simple docstring'''
     __magic_name__						       =    str(Path(a			).resolve()			)
     assert cached_path(a			) == text_file
     # relative path
     __magic_name__						       =    str(Path(a			).resolve().relative_to(Path(os.getcwd()			)			)			)
     assert cached_path(a			) == text_file




def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    str(tmp_path.resolve() / '''__missing_file__.txt'''			)
     with pytest.raises(a			):
          cached_path(a			)
     # relative path
     __magic_name__						       =    '''./__missing_file__.txt'''
     with pytest.raises(a			):
          cached_path(a			)




def 						UpperCamelCase	(			a			)				->     Optional[Any]:






     '''simple docstring'''
     __magic_name__						       =    get_from_cache(F'''tmp://{tmpfs_file}'''			)
     with open(a			) as f:
          __magic_name__						       =    f.read()
     assert output_file_content == FILE_CONTENT




@patch('''datasets.config.HF_DATASETS_OFFLINE'''		,						a			)
def 						UpperCamelCase	(			)				->     List[Any]:






     '''simple docstring'''
     with pytest.raises(a			):
          cached_path('''https://huggingface.co'''			)




@patch('''datasets.config.HF_DATASETS_OFFLINE'''		,						a			)
def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    tmp_path_factory.mktemp('''data'''			) / '''file.html'''
     with pytest.raises(a			):
          http_get('''https://huggingface.co'''		,						temp_file=a			)
     with pytest.raises(a			):
          http_head('''https://huggingface.co'''			)




@patch('''datasets.config.HF_DATASETS_OFFLINE'''		,						a			)
def 						UpperCamelCase	(			a			)				->     List[str]:






     '''simple docstring'''
     __magic_name__						       =    tmp_path_factory.mktemp('''data'''			) / '''file.html'''
     with pytest.raises(a			):
          ftp_get('''ftp://huggingface.co'''		,						temp_file=a			)
     with pytest.raises(a			):
          ftp_head('''ftp://huggingface.co'''			)




@patch('''datasets.config.HF_DATASETS_OFFLINE'''		,						a			)
def 						UpperCamelCase	(			a			)				->     Union[str, Any]:






     '''simple docstring'''
     __magic_name__						       =    tmp_path_factory.mktemp('''data'''			) / '''file.html'''
     with pytest.raises(a			):
          fsspec_get('''s3://huggingface.co'''		,						temp_file=a			)
     with pytest.raises(a			):
          fsspec_head('''s3://huggingface.co'''			)









'''simple docstring'''




import math
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 SchedulerMixin, SchedulerOutput






class   _SCREAMING_SNAKE_CASE   (   __a		,__a     ):
 __SCREAMING_SNAKE_CASE					:Optional[Any]      	=					1



 @register_to_config
 def __init__(   self		:   Optional[int]			,				a__		:   int = 1000			,				a__		:   Optional[Union[np.ndarray, List[float]]] = None      ):
      # set `betas`, `alphas`, `timesteps`
      self.set_timesteps(a__      )

      # standard deviation of the initial noise distribution
      __magic_name__						       =    1.0

      # For now we only support F-PNDM, i.e. the runge-kutta method
      # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
      # mainly at formula (9), (12), (13) and the Algorithm 2.
      __magic_name__						       =    4

      # running values
      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Dict			,				a__		:   int			,				a__		:   Union[str, torch.device] = None      ):
      __magic_name__						       =    num_inference_steps
      __magic_name__						       =    torch.linspace(1			,				0			,				num_inference_steps + 1      )[:-1]
      __magic_name__						       =    torch.cat([steps, torch.tensor([0.0]      )]      )

      if self.config.trained_betas is not None:
           __magic_name__						       =    torch.tensor(self.config.trained_betas			,				dtype=torch.floataa      )
      else:
           __magic_name__						       =    torch.sin(steps * math.pi / 2      ) ** 2

      __magic_name__						       =    (1.0 - self.betas**2) ** 0.5

      __magic_name__						       =    (torch.atana(self.betas			,				self.alphas      ) / math.pi * 2)[:-1]
      __magic_name__						       =    timesteps.to(a__      )

      __magic_name__						       =    []



 def 			snake_case__  (   self		:   Union[str, Any]			,				a__		:   torch.FloatTensor			,				a__		:   int			,				a__		:   torch.FloatTensor			,				a__		:   bool = True			,				):
      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'''      )

      __magic_name__						       =    (self.timesteps == timestep).nonzero().item()
      __magic_name__						       =    timestep_index + 1

      __magic_name__						       =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
      self.ets.append(a__      )

      if len(self.ets      ) == 1:
           __magic_name__						       =    self.ets[-1]
      elif len(self.ets      ) == 2:
           __magic_name__						       =    (3 * self.ets[-1] - self.ets[-2]) / 2
      elif len(self.ets      ) == 3:
           __magic_name__						       =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
      else:
           __magic_name__						       =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

      __magic_name__						       =    self._get_prev_sample(a__			,				a__			,				a__			,				a__      )

      if not return_dict:
           return (prev_sample,)

      return SchedulerOutput(prev_sample=a__      )



 def 			snake_case__  (   self		:   Any			,				a__		:   torch.FloatTensor			,				*a__		:   Optional[int]			,				**a__		:   Union[str, Any]      ):
      return sample



 def 			snake_case__  (   self		:   str			,				a__		:   Optional[int]			,				a__		:   int			,				a__		:   List[Any]			,				a__		:   List[str]      ):
      __magic_name__						       =    self.alphas[timestep_index]
      __magic_name__						       =    self.betas[timestep_index]

      __magic_name__						       =    self.alphas[prev_timestep_index]
      __magic_name__						       =    self.betas[prev_timestep_index]

      __magic_name__						       =    (sample - sigma * ets) / max(a__			,				1E-8      )
      __magic_name__						       =    next_alpha * pred + ets * next_sigma

      return prev_sample



 def __len__(   self		:   int      ):
      return self.config.num_train_timesteps






