






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						Union[str, Any]								=		(HeunDiscreteScheduler,)
     UpperCamelCase_							:						Optional[int]								=		1_0


     def  _A				( self				:		str    ,       **UpperCAmelCase_				:		Tuple					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			{
           "num_train_timesteps": 1100,
           "beta_start": 0.0_001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**UpperCAmelCase_					)
       return config


     def  _A				( self				:		Tuple					):
       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=UpperCAmelCase_					)


     def  _A				( self				:		Union[str, Any]					):
       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,       [0.0_002, 0.002, 0.02]					):
         self.check_over_configs(beta_start=UpperCAmelCase_    ,       beta_end=UpperCAmelCase_					)


     def  _A				( self				:		List[Any]					):
       for schedule in ["linear", "scaled_linear", "exp"]:
         self.check_over_configs(beta_schedule=UpperCAmelCase_					)


     def  _A				( self				:		Optional[int]					):
       for clip_sample_range in [1.0, 2.0, 3.0]:
         self.check_over_configs(clip_sample_range=UpperCAmelCase_    ,       clip_sample=UpperCAmelCase_					)


     def  _A				( self				:		int					):
       for prediction_type in ["epsilon", "v_prediction", "sample"]:
         self.check_over_configs(prediction_type=UpperCAmelCase_					)


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.get_scheduler_config()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			scheduler_class(**UpperCAmelCase_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE				:					Dict		  =			self.dummy_model()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE				:					Tuple		  =			sample.to(UpperCAmelCase_					)

       for i, t in enumerate(scheduler.timesteps					):
         SCREAMING_SNAKE_CASE				:					Any		  =			scheduler.scale_model_input(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					Dict		  =			model(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					List[Any]		  =			scheduler.step(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Dict		  =			output.prev_sample

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

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 0.1_233					) < 1E-2
         assert abs(result_mean.item() - 0.0_002					) < 1E-3
       else:
         # CUDA
         assert abs(result_sum.item() - 0.1_233					) < 1E-2
         assert abs(result_mean.item() - 0.0_002					) < 1E-3


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE				:					Dict		  =			self.get_scheduler_config(prediction_type="v_prediction"					)
       SCREAMING_SNAKE_CASE				:					Tuple		  =			scheduler_class(**UpperCAmelCase_					)

       scheduler.set_timesteps(self.num_inference_steps					)

       SCREAMING_SNAKE_CASE				:					str		  =			self.dummy_model()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.dummy_sample_deter * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			sample.to(UpperCAmelCase_					)

       for i, t in enumerate(scheduler.timesteps					):
         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			scheduler.scale_model_input(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					int		  =			model(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					List[str]		  =			scheduler.step(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					List[str]		  =			output.prev_sample

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

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6_934E-07					) < 1E-2
         assert abs(result_mean.item() - 6.1_112E-10					) < 1E-3
       else:
         # CUDA
         assert abs(result_sum.item() - 4.693_428_650_170_972E-07					) < 1E-2
         assert abs(result_mean.item() - 0.0_002					) < 1E-3


     def  _A				( self				:		List[str]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.get_scheduler_config()
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			scheduler_class(**UpperCAmelCase_					)

       scheduler.set_timesteps(self.num_inference_steps    ,       device=UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.dummy_model()
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         SCREAMING_SNAKE_CASE				:					int		  =			scheduler.scale_model_input(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					str		  =			model(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					Any		  =			scheduler.step(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Dict		  =			output.prev_sample

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

       if str(UpperCAmelCase_					).startswith("cpu"					):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 0.1_233					) < 1E-2
         assert abs(result_mean.item() - 0.0_002					) < 1E-3
       elif str(UpperCAmelCase_					).startswith("mps"					):
         # Larger tolerance on mps
         assert abs(result_mean.item() - 0.0_002					) < 1E-2
       else:
         # CUDA
         assert abs(result_sum.item() - 0.1_233					) < 1E-2
         assert abs(result_mean.item() - 0.0_002					) < 1E-3






     def  _A				( self				:		Optional[Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.scheduler_classes[0]
       SCREAMING_SNAKE_CASE				:					Any		  =			self.get_scheduler_config()
       SCREAMING_SNAKE_CASE				:					Tuple		  =			scheduler_class(**UpperCAmelCase_    ,       use_karras_sigmas=UpperCAmelCase_					)

       scheduler.set_timesteps(self.num_inference_steps    ,       device=UpperCAmelCase_					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			self.dummy_model()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.dummy_sample_deter.to(UpperCAmelCase_					) * scheduler.init_noise_sigma
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			sample.to(UpperCAmelCase_					)

       for t in scheduler.timesteps:
         SCREAMING_SNAKE_CASE				:					Optional[int]		  =			scheduler.scale_model_input(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			model(UpperCAmelCase_    ,       UpperCAmelCase_					)

         SCREAMING_SNAKE_CASE				:					Dict		  =			scheduler.step(UpperCAmelCase_    ,       UpperCAmelCase_    ,       UpperCAmelCase_					)
         SCREAMING_SNAKE_CASE				:					Dict		  =			output.prev_sample

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

       assert abs(result_sum.item() - 0.00_015					) < 1E-2
       assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07					) < 1E-2














import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  assert dataset.num_rows == 4
  assert dataset.num_columns == 3
  assert dataset.column_names == ["col_1", "col_2", "col_3"]
  for feature, expected_dtype in expected_features.items():
    assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Any		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Dict		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase  ,   split=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )
  assert dataset.split == split if split else "train"


@pytest.mark.parametrize("path_type"  ,   [str, list] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					Tuple		  =			csv_path
  elif issubclass(lowercase  ,   lowercase ):
    SCREAMING_SNAKE_CASE				:					int		  =			[csv_path]
  SCREAMING_SNAKE_CASE				:					Any		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_dataset(lowercase  ,   lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase=("train",) ):

  """simple docstring"""






  assert isinstance(lowercase  ,   lowercase )
  for split in splits:
    SCREAMING_SNAKE_CASE				:					str		  =			dataset_dict[split]
    assert dataset.num_rows == 4
    assert dataset.num_columns == 3
    assert dataset.column_names == ["col_1", "col_2", "col_3"]
    for feature, expected_dtype in expected_features.items():
      assert dataset.features[feature].dtype == expected_dtype


@pytest.mark.parametrize("keep_in_memory"  ,   [False, True] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase  ,   keep_in_memory=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize(
    "features"  ,   [
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]  ,   )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					int		  =			tmp_path / "cache"
  # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
  SCREAMING_SNAKE_CASE				:					Any		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			features.copy() if features else default_expected_features
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			(
      Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
  )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			CsvDatasetReader({"train": csv_path}  ,   features=lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase )


@pytest.mark.parametrize("split"  ,   [None, NamedSplit("train" ), "train", "test"] )
def      lowerCamelCase__	(   lowercase  ,   lowercase  ,   lowercase ):

  """simple docstring"""






  if split:
    SCREAMING_SNAKE_CASE				:					Dict		  =			{split: csv_path}
  else:
    SCREAMING_SNAKE_CASE				:					str		  =			{"train": csv_path, "test": csv_path}
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Dict		  =			{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
  SCREAMING_SNAKE_CASE				:					Tuple		  =			CsvDatasetReader(lowercase  ,   cache_dir=lowercase ).read()
  _check_csv_datasetdict(lowercase  ,   lowercase  ,   splits=list(path.keys() ) )
  assert all(dataset[split].split == split for split in path.keys() )


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  with open(lowercase  ,   encoding="utf-8" ) as csvfile:
    yield from csv.reader(lowercase )


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					List[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					Optional[int]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=1 ).write()

  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					List[Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=2 ).write()

  SCREAMING_SNAKE_CASE				:					Dict		  =			iter_csv_file(lowercase )
  SCREAMING_SNAKE_CASE				:					Tuple		  =			iter_csv_file(lowercase )

  for rowa, rowa in zip(lowercase  ,   lowercase ):
    assert rowa == rowa


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			tmp_path / "cache"
  SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			os.path.join(lowercase  ,   "tmp.csv" )
  SCREAMING_SNAKE_CASE				:					List[str]		  =			CsvDatasetReader({"train": csv_path}  ,   cache_dir=lowercase ).read()
  with pytest.raises(lowercase ):
    CsvDatasetWriter(dataset["train"]  ,   lowercase  ,   num_proc=0 )






