






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

UpperCamelCase							       =    """file"""






@pytest.fixture(scope="session"					)
def        lowerCAmelCase (      UpperCamelCase_:							List[str]					)       ->							Optional[int]:







  '''simple docstring'''







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






@pytest.fixture
def        lowerCAmelCase (      UpperCamelCase_:							str					)       ->							Optional[int]:







  '''simple docstring'''







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






@pytest.mark.parametrize("compression_format"     , ["gzip", "xz", "zstd"]					)
def        lowerCAmelCase (      UpperCamelCase_:							str     , UpperCamelCase_:							List[Any]     , UpperCamelCase_:							int     , UpperCamelCase_:							Any     , UpperCamelCase_:							Any     , UpperCamelCase_:							Union[str, Any]					)       ->							Optional[int]:







  '''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=UpperCamelCase_     , extract_compressed_file=UpperCamelCase_					)
  _a							=	cached_path(UpperCamelCase_     , download_config=UpperCamelCase_					)
  with open(UpperCamelCase_					) as f:
    _a							=	f.read()
  with open(UpperCamelCase_					) 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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							Dict     , UpperCamelCase_:							List[Any]					)       ->							List[Any]:







  '''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"     , UpperCamelCase_					)
    monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"     , str(UpperCamelCase_					)					)
    _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=UpperCamelCase_					)
      if default_cache_dir
      else DownloadConfig(cache_dir=tmp_path / custom_cache_dir     , extract_compressed_file=UpperCamelCase_					)
  )
  _a							=	cached_path(UpperCamelCase_     , download_config=UpperCamelCase_					)
  assert Path(UpperCamelCase_					).parent.parts[-2:] == expected






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]					)       ->							List[str]:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							Any					)       ->							str:







  '''simple docstring'''







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






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]					)       ->							Optional[Any]:







  '''simple docstring'''







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






@patch("datasets.config.HF_DATASETS_OFFLINE"     , UpperCamelCase_					)
def        lowerCAmelCase (      )       ->							Tuple:







  '''simple docstring'''







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






@patch("datasets.config.HF_DATASETS_OFFLINE"     , UpperCamelCase_					)
def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							Tuple:







  '''simple docstring'''







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






@patch("datasets.config.HF_DATASETS_OFFLINE"     , UpperCamelCase_					)
def        lowerCAmelCase (      UpperCamelCase_:							List[Any]					)       ->							Tuple:







  '''simple docstring'''







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






@patch("datasets.config.HF_DATASETS_OFFLINE"     , UpperCamelCase_					)
def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							int:







  '''simple docstring'''







  _a							=	tmp_path_factory.mktemp("data"					) / "file.html"
  with pytest.raises(UpperCamelCase_					):
    fsspec_get("s3://huggingface.co"     , temp_file=UpperCamelCase_					)
  with pytest.raises(UpperCamelCase_					):
    fsspec_head("s3://huggingface.co"					)










"""simple docstring"""




import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Union[str, Any]       			=				(PNDMScheduler,)
      A__	:		Optional[int]       			=				(('''num_inference_steps''', 50),)






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	{
            "num_train_timesteps": 1_0_0_0,
            "beta_start": 0.0_001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**a_	)
        return config






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config(**a_	)
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)
          # copy over dummy past residuals
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            new_scheduler.set_timesteps(a_	)
            # copy over dummy past residuals
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        pass






      def 			lowerCamelCase__    (    self     ,     a_=0     ,     **a_	)	->Tuple:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)
        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)
        _a							=	self.dummy_sample
        _a							=	0.1 * sample
        _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)
          scheduler.set_timesteps(a_	)

          # copy over dummy past residuals (must be after setting timesteps)
          _a							=	dummy_past_residuals[:]

          with tempfile.TemporaryDirectory() as tmpdirname:
            scheduler.save_config(a_	)
            _a							=	scheduler_class.from_pretrained(a_	)
            # copy over dummy past residuals
            new_scheduler.set_timesteps(a_	)

            # copy over dummy past residual (must be after setting timesteps)
            _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_prk(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample
          _a							=	new_scheduler.step_plms(a_     ,     a_     ,     a_     ,     **a_	).prev_sample

          assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"






      def 			lowerCamelCase__    (    self     ,     **a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(**a_	)
        _a							=	scheduler_class(**a_	)

        _a							=	1_0
        _a							=	self.dummy_model()
        _a							=	self.dummy_sample_deter
        scheduler.set_timesteps(a_	)

        for i, t in enumerate(scheduler.prk_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps	):
          _a							=	model(a_     ,     a_	)
          _a							=	scheduler.step_plms(a_     ,     a_     ,     a_	).prev_sample

        return sample






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        _a							=	dict(self.forward_default_kwargs	)

        _a							=	kwargs.pop("num_inference_steps"     ,     a_	)

        for scheduler_class in self.scheduler_classes:
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          if num_inference_steps is not None and hasattr(a_     ,     "set_timesteps"	):
            scheduler.set_timesteps(a_	)
          elif num_inference_steps is not None and not hasattr(a_     ,     "set_timesteps"	):
            _a							=	num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _a							=	[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
          _a							=	dummy_past_residuals[:]

          _a							=	scheduler.step_prk(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_prk(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)

          _a							=	scheduler.step_plms(a_     ,     0     ,     a_     ,     **a_	).prev_sample
          _a							=	scheduler.step_plms(a_     ,     1     ,     a_     ,     **a_	).prev_sample

          self.assertEqual(output_a.shape     ,     sample.shape	)
          self.assertEqual(output_a.shape     ,     output_a.shape	)






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        for timesteps in [1_0_0, 1_0_0_0]:
          self.check_over_configs(num_train_timesteps=a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=a_	)

        _a							=	self.scheduler_classes[0]
        _a							=	self.get_scheduler_config(steps_offset=1	)
        _a							=	scheduler_class(**a_	)
        scheduler.set_timesteps(1_0	)
        assert torch.equal(
            scheduler.timesteps     ,     torch.LongTensor(
                [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]	)     ,     )






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        for beta_start, beta_end in zip([0.0_001, 0.001]     ,     [0.002, 0.02]	):
          self.check_over_configs(beta_start=a_     ,     beta_end=a_	)






      def 			lowerCamelCase__    (    self	)	->Dict:







        '''simple docstring'''


        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=a_	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        for t in [1, 5, 1_0]:
          self.check_over_forward(time_step=a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        for t, num_inference_steps in zip([1, 5, 1_0]     ,     [1_0, 5_0, 1_0_0]	):
          self.check_over_forward(num_inference_steps=a_	)






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	2_7

        for scheduler_class in self.scheduler_classes:
          _a							=	self.dummy_sample
          _a							=	0.1 * sample

          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.set_timesteps(a_	)

          # before power of 3 fix, would error on first step, so we only need to do two
          for i, t in enumerate(scheduler.prk_timesteps[:2]	):
            _a							=	scheduler.step_prk(a_     ,     a_     ,     a_	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        with self.assertRaises(a_	):
          _a							=	self.scheduler_classes[0]
          _a							=	self.get_scheduler_config()
          _a							=	scheduler_class(**a_	)

          scheduler.step_plms(self.dummy_sample     ,     1     ,     self.dummy_sample	).prev_sample






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        _a							=	self.full_loop()
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 198.1_318	) < 1E-2
        assert abs(result_mean.item() - 0.2_580	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.full_loop(prediction_type="v_prediction"	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 67.3_986	) < 1E-2
        assert abs(result_mean.item() - 0.0_878	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 230.0_399	) < 1E-2
        assert abs(result_mean.item() - 0.2_995	) < 1E-3






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        _a							=	self.full_loop(set_alpha_to_one=a_     ,     beta_start=0.01	)
        _a							=	torch.sum(torch.abs(a_	)	)
        _a							=	torch.mean(torch.abs(a_	)	)

        assert abs(result_sum.item() - 186.9_482	) < 1E-2
        assert abs(result_mean.item() - 0.2_434	) < 1E-3


