"""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    :       int					     ="""\
    Text data.
    Second line of data."""

__lowerCAmelCase    :       int					     ="""file"""


@pytest.fixture(scope="""session"""		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	tmp_path_factory.mktemp("""data"""		) / (FILE_PATH + """.zstd""")
      lowercase														=	bytes(lowerCAmelCase__					,		"""utf-8"""		)
      with zstd.open(lowerCAmelCase__					,		"""wb"""		) as f:
            f.write(lowerCAmelCase__		)
      return path


@pytest.fixture
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Any:
      '''simple docstring'''


      with open(os.path.join(tmpfs.local_root_dir					,		lowerCAmelCase__		)					,		"""w"""		) as f:
            f.write(lowerCAmelCase__		)
      return FILE_PATH


@pytest.mark.parametrize("""compression_format"""					,		["""gzip""", """xz""", """zstd"""]		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :str		) ->    Any:
      '''simple docstring'''


      lowercase														=	{"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
      lowercase														=	input_paths[compression_format]
      lowercase														=	tmp_path / """cache"""
      lowercase														=	DownloadConfig(cache_dir=lowerCAmelCase__					,		extract_compressed_file=lowerCAmelCase__		)
      lowercase														=	cached_path(lowerCAmelCase__					,		download_config=lowerCAmelCase__		)
      with open(lowerCAmelCase__		) as f:
            lowercase														=	f.read()
      with open(lowerCAmelCase__		) as f:
            lowercase														=	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__       (				lowerCAmelCase__     :Optional[Any]					,		lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Dict					,		lowerCAmelCase__     :Union[str, Any]					,		lowerCAmelCase__     :List[str]		) ->    int:
      '''simple docstring'''


      lowercase														=	"""custom_cache"""
      lowercase														=	"""custom_extracted_dir"""
      lowercase														=	tmp_path / """custom_extracted_path"""
      if default_extracted:
            lowercase														=	("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
      else:
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""					,		lowerCAmelCase__		)
            monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""					,		str(lowerCAmelCase__		)		)
            lowercase														=	custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

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


def 			UpperCAmelCase__       (				lowerCAmelCase__     :List[Any]		) ->    Any:
      '''simple docstring'''


      lowercase														=	str(Path(lowerCAmelCase__		).resolve()		)
      assert cached_path(lowerCAmelCase__		) == text_file
      # relative path
      lowercase														=	str(Path(lowerCAmelCase__		).resolve().relative_to(Path(os.getcwd()		)		)		)
      assert cached_path(lowerCAmelCase__		) == text_file


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[int]		) ->    Optional[int]:
      '''simple docstring'''


      lowercase														=	str(tmp_path.resolve() / """__missing_file__.txt"""		)
      with pytest.raises(lowerCAmelCase__		):
            cached_path(lowerCAmelCase__		)
      # relative path
      lowercase														=	"""./__missing_file__.txt"""
      with pytest.raises(lowerCAmelCase__		):
            cached_path(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Optional[Any]		) ->    Optional[Any]:
      '''simple docstring'''


      lowercase														=	get_from_cache(f'tmp://{tmpfs_file}'		)
      with open(lowerCAmelCase__		) as f:
            lowercase														=	f.read()
      assert output_file_content == FILE_CONTENT


@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,		lowerCAmelCase__		)
def 			UpperCAmelCase__       (				) ->    Union[str, Any]:
      '''simple docstring'''


      with pytest.raises(lowerCAmelCase__		):
            cached_path("""https://huggingface.co"""		)


@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,		lowerCAmelCase__		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    Union[str, Any]:
      '''simple docstring'''


      lowercase														=	tmp_path_factory.mktemp("""data"""		) / """file.html"""
      with pytest.raises(lowerCAmelCase__		):
            http_get("""https://huggingface.co"""					,		temp_file=lowerCAmelCase__		)
      with pytest.raises(lowerCAmelCase__		):
            http_head("""https://huggingface.co"""		)


@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,		lowerCAmelCase__		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Dict		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	tmp_path_factory.mktemp("""data"""		) / """file.html"""
      with pytest.raises(lowerCAmelCase__		):
            ftp_get("""ftp://huggingface.co"""					,		temp_file=lowerCAmelCase__		)
      with pytest.raises(lowerCAmelCase__		):
            ftp_head("""ftp://huggingface.co"""		)






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,		lowerCAmelCase__		)
def 			UpperCAmelCase__       (				lowerCAmelCase__     :Tuple		) ->    List[Any]:
      '''simple docstring'''


      lowercase														=	tmp_path_factory.mktemp("""data"""		) / """file.html"""
      with pytest.raises(lowerCAmelCase__		):
            fsspec_get("""s3://huggingface.co"""					,		temp_file=lowerCAmelCase__		)
      with pytest.raises(lowerCAmelCase__		):
            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  _A						(       lowerCAmelCase  ,    lowerCAmelCase	):
      snake_case__			:   Tuple							  =	1

      @register_to_config
      def __init__(						self		,       __lowerCAmelCase = 1000		,       __lowerCAmelCase = None						):







            """simple docstring"""



            self.set_timesteps(__lowerCAmelCase						)

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

            # running values
            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	num_inference_steps
            lowercase														=	torch.linspace(1		,       0		,       num_inference_steps + 1						)[:-1]
            lowercase														=	torch.cat([steps, torch.tensor([0.0]						)]						)

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

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

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

            lowercase														=	[]

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase = True		,       ):







            """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"""						)

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

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

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

            lowercase														=	self._get_prev_sample(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)

            if not return_dict:
                  return (prev_sample,)

            return SchedulerOutput(prev_sample=__lowerCAmelCase						)

      def 				A__       (						self		,       __lowerCAmelCase		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            return sample

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	self.alphas[timestep_index]
            lowercase														=	self.betas[timestep_index]

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

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

            return prev_sample







      def __len__(						self						):







            """simple docstring"""



            return self.config.num_train_timesteps



