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,
)


__A      =				'\\n    Text data.\n    Second line of data.'

__A      =				'file'




@pytest.fixture(scope='''session''' )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					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      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			_lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     ):

					'''simple docstring'''







					with pytest.raises(snake_case_ ):
										cached_path('''https://huggingface.co''' )




@patch('''datasets.config.HF_DATASETS_OFFLINE'''					,			snake_case_ )
def      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					_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      __A					(     _lowercase ):

					'''simple docstring'''







					_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''' )











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				( snake_case  ,	snake_case					):





							"""simple docstring"""

							A_						  =					1

							@register_to_config
							def __init__(  self:   Any		,     __A:   int = 10_00		,     __A:   Optional[Union[np.ndarray, List[float]]] = None   )    ->				List[str]:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__A   )

												# standard deviation of the initial noise distribution
												_A								= 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.
												_A								= 4

												# running values
												_A								= []

							def       __A (  self:   str		,     __A:   int		,     __A:   Union[str, torch.device] = None   )    ->				int:
												_A								= num_inference_steps
												_A								= torch.linspace(1		,     0		,     num_inference_steps + 1   )[:-1]
												_A								= torch.cat([steps, torch.tensor([0.0]   )]   )

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

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

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

												_A								= []

							def       __A (  self:   Tuple		,     __A:   torch.FloatTensor		,     __A:   int		,     __A:   torch.FloatTensor		,     __A:   bool = True		,     )    ->				Union[SchedulerOutput, Tuple]:
												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'''   )

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

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

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

												_A								= self._get_prev_sample(__A		,     __A		,     __A		,     __A   )

												if not return_dict:
																	return (prev_sample,)

												return SchedulerOutput(prev_sample=__A   )

							def       __A (  self:   Optional[int]		,     __A:   torch.FloatTensor		,     *__A:   Tuple		,     **__A:   List[Any]   )    ->				torch.FloatTensor:
												return sample

							def       __A (  self:   List[str]		,     __A:   Optional[Any]		,     __A:   Optional[Any]		,     __A:   Any		,     __A:   List[Any]   )    ->				List[Any]:
												_A								= self.alphas[timestep_index]
												_A								= self.betas[timestep_index]

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

												_A								= (sample - sigma * ets) / max(__A		,     1e-8   )
												_A								= next_alpha * pred + ets * next_sigma

												return prev_sample



							def __len__(  self:   List[str]   )    ->				Dict:
												return self.config.num_train_timesteps



