



"""simple docstring"""





import os
import zipfile

import pytest

from datasets.utils.extract import (
    BzipaExtractor,
    Extractor,
    GzipExtractor,
    LzaExtractor,
    SevenZipExtractor,
    TarExtractor,
    XzExtractor,
    ZipExtractor,
    ZstdExtractor,
)

from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
    "compression_format, is_archive"      ,     [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]      ,     )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     ):
      UpperCAmelCase_     					=				{
          "7z": (seven_zip_file, SevenZipExtractor),
          "bz2": (bza_file, BzipaExtractor),
          "gzip": (gz_file, GzipExtractor),
          "lz4": (lza_file, LzaExtractor),
          "tar": (tar_file, TarExtractor),
          "xz": (xz_file, XzExtractor),
          "zip": (zip_file, ZipExtractor),
          "zstd": (zstd_file, ZstdExtractor),
      }
      UpperCAmelCase_					,       UpperCAmelCase_     					=				input_paths_and_base_extractors[compression_format]
      if input_path is None:
            UpperCAmelCase_     					=				f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(lowerCAmelCase__ )
      assert base_extractor.is_extractable(lowerCAmelCase__ )
      UpperCAmelCase_     					=				tmp_path / ("extracted" if is_archive else "extracted.txt")
      base_extractor.extract(lowerCAmelCase__      ,     lowerCAmelCase__ )
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase_     					=				file_path.read_text(encoding="utf-8" )
      else:
            UpperCAmelCase_     					=				output_path.read_text(encoding="utf-8" )
      UpperCAmelCase_     					=				text_file.read_text(encoding="utf-8" )
      assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
    "compression_format, is_archive"      ,     [
        ("7z", True),
        ("bz2", False),
        ("gzip", False),
        ("lz4", False),
        ("tar", True),
        ("xz", False),
        ("zip", True),
        ("zstd", False),
    ]      ,     )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     ):
      UpperCAmelCase_     					=				{
          "7z": seven_zip_file,
          "bz2": bza_file,
          "gzip": gz_file,
          "lz4": lza_file,
          "tar": tar_file,
          "xz": xz_file,
          "zip": zip_file,
          "zstd": zstd_file,
      }
      UpperCAmelCase_     					=				input_paths[compression_format]
      if input_path is None:
            UpperCAmelCase_     					=				f"""for '{compression_format}' compression_format, """
            if compression_format == "7z":
                  reason += require_pyazr.kwargs["reason"]
            elif compression_format == "lz4":
                  reason += require_lza.kwargs["reason"]
            elif compression_format == "zstd":
                  reason += require_zstandard.kwargs["reason"]
            pytest.skip(lowerCAmelCase__ )
      UpperCAmelCase_     					=				Extractor.infer_extractor_format(lowerCAmelCase__ )
      assert extractor_format is not None
      UpperCAmelCase_     					=				tmp_path / ("extracted" if is_archive else "extracted.txt")
      Extractor.extract(lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ )
      if is_archive:
            assert output_path.is_dir()
            for file_path in output_path.iterdir():
                  assert file_path.name == text_file.name
                  UpperCAmelCase_     					=				file_path.read_text(encoding="utf-8" )
      else:
            UpperCAmelCase_     					=				output_path.read_text(encoding="utf-8" )
      UpperCAmelCase_     					=				text_file.read_text(encoding="utf-8" )
      assert extracted_file_content == expected_file_content
@pytest.fixture
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__ ):
      import tarfile

      UpperCAmelCase_     					=				tmp_path / "data_dot_dot"
      directory.mkdir()
      UpperCAmelCase_     					=				directory / "tar_file_with_dot_dot.tar"
      with tarfile.TarFile(lowerCAmelCase__      ,     "w" ) as f:
            f.add(lowerCAmelCase__      ,     arcname=os.path.join(".."      ,     text_file.name ) )
      return path
@pytest.fixture
def   a__      ( lowerCAmelCase__ ):
      import tarfile

      UpperCAmelCase_     					=				tmp_path / "data_sym_link"
      directory.mkdir()
      UpperCAmelCase_     					=				directory / "tar_file_with_sym_link.tar"
      os.symlink(".."      ,     directory / "subdir"      ,     target_is_directory=lowerCAmelCase__ )
      with tarfile.TarFile(lowerCAmelCase__      ,     "w" ) as f:
            f.add(str(directory / "subdir" )      ,     arcname="subdir" )  # str required by os.readlink on Windows and Python < 3.8
      return path
@pytest.mark.parametrize(
    "insecure_tar_file, error_log"      ,     [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")]      ,     )
def   a__      ( lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__      ,     lowerCAmelCase__ ):
      UpperCAmelCase_     					=				{
          "tar_file_with_dot_dot": tar_file_with_dot_dot,
          "tar_file_with_sym_link": tar_file_with_sym_link,
      }
      UpperCAmelCase_     					=				insecure_tar_files[insecure_tar_file]
      UpperCAmelCase_     					=				tmp_path / "extracted"
      TarExtractor.extract(lowerCAmelCase__      ,     lowerCAmelCase__ )
      assert caplog.text
      for record in caplog.records:
            assert record.levelname == "ERROR"
            assert error_log in record.msg


def   a__      ( lowerCAmelCase__ ):
      # We should have less false positives than zipfile.is_zipfile
      # We do that by checking only the magic number
      UpperCAmelCase_     					=				tmpdir / "not_a_zip_file"
      # From: https://github.com/python/cpython/pull/5053
      UpperCAmelCase_     					=				(
          b"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
          b"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
          b"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
          b"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
      )
      with not_a_zip_file.open("wb" ) as f:
            f.write(lowerCAmelCase__ )
      assert zipfile.is_zipfile(str(lowerCAmelCase__ ) )  # is a false positive for `zipfile`
      assert not ZipExtractor.is_extractable(lowerCAmelCase__ )  # but we're right











'''simple docstring'''




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(UnCLIPScheduler,)


					def lowerCamelCase_			(						self				:			List[str]      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """num_train_timesteps""": 10_00,
							    """variance_type""": """fixed_small_log""",
							    """clip_sample""": True,
							    """clip_sample_range""": 1.0,
							    """prediction_type""": """epsilon""",
							}

							config.update(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for timesteps in [1, 5, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for variance in ["fixed_small_log", "learned_range"]:
									self.check_over_configs(variance_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							for clip_sample_range in [1, 5, 10, 20]:
									self.check_over_configs(clip_sample_range=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for prediction_type in ["epsilon", "sample"]:
									self.check_over_configs(prediction_type=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							for time_step in [0, 5_00, 9_99]:
									for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
											if prev_timestep is not None and prev_timestep >= time_step:
													continue

											self.check_over_forward(time_step=lowerCamelCase_      ,						prev_timestep=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config(variance_type="""fixed_small_log"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler_class(**lowerCamelCase_						)

							assert torch.sum(torch.abs(scheduler._get_variance(0						) - 1.0000e-10						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(4_87						) - 0.0_549_625						)						) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(9_99						) - 0.9_994_987						)						) < 1e-5


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.get_scheduler_config(variance_type="""learned_range"""						)
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				List[str]		=			0.5

							assert scheduler._get_variance(1      ,						predicted_variance=lowerCamelCase_						) - -10.1_712_790 < 1e-5
							assert scheduler._get_variance(4_87      ,						predicted_variance=lowerCamelCase_						) - -5.7_998_052 < 1e-5
							assert scheduler._get_variance(9_99      ,						predicted_variance=lowerCamelCase_						) - -0.0_010_011 < 1e-5


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				str		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				List[str]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				Optional[int]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Optional[int]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 252.2_682_495						) < 1e-2
							assert abs(result_mean.item() - 0.3_284_743						) < 1e-3


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				Dict		=			self.get_scheduler_config()
							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							scheduler.set_timesteps(25						)

							SCREAMING_SNAKE_CASE :				List[str]		=			scheduler.timesteps

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample_deter
							SCREAMING_SNAKE_CASE :				Any		=			torch.manual_seed(0						)

							for i, t in enumerate(lowerCamelCase_						):
									# 1. predict noise residual
									SCREAMING_SNAKE_CASE :				int		=			model(lowerCamelCase_      ,						lowerCamelCase_						)

									if i + 1 == timesteps.shape[0]:
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			None
									else:
											SCREAMING_SNAKE_CASE :				int		=			timesteps[i + 1]

									# 2. predict previous mean of sample x_t-1
									SCREAMING_SNAKE_CASE :				int		=			scheduler.step(
									    lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						prev_timestep=lowerCamelCase_      ,						generator=lowerCamelCase_						).prev_sample

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			pred_prev_sample

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.sum(torch.abs(lowerCamelCase_						)						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_sum.item() - 258.2_044_983						) < 1e-2
							assert abs(result_mean.item() - 0.3_362_038						) < 1e-3


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							pass



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							pass



