





'''simple docstring'''

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 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		assert isinstance(A_,    A_   )
		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 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				List[Any]					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				str					   =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				__UpperCAmelCase     :				Union[str, Any]					   =			CsvDatasetReader(A_,    cache_dir=A_,    keep_in_memory=A_   ).read()
		_check_csv_dataset(A_,    A_   )


@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 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				str					   =			tmp_path / 'cache'
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		__UpperCAmelCase     :				Optional[int]					   =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		__UpperCAmelCase     :				Dict					   =			features.copy() if features else default_expected_features
		__UpperCAmelCase     :				Dict					   =			(
		    Features({feature: Value(A_   ) for feature, dtype in features.items()}   ) if features is not None else None
		)
		__UpperCAmelCase     :				Tuple					   =			CsvDatasetReader(A_,    features=A_,    cache_dir=A_   ).read()
		_check_csv_dataset(A_,    A_   )


@pytest.mark.parametrize("split",    [None, NamedSplit("train"   ), "train", "test"]   )
def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				str					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				Tuple					   =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		__UpperCAmelCase     :				List[str]					   =			CsvDatasetReader(A_,    cache_dir=A_,    split=A_   ).read()
		_check_csv_dataset(A_,    A_   )
		assert dataset.split == split if split else "train"


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

		if issubclass(A_,    A_   ):
				__UpperCAmelCase     :				Dict					   =			csv_path
		elif issubclass(A_,    A_   ):
				__UpperCAmelCase     :				Optional[Any]					   =			[csv_path]
		__UpperCAmelCase     :				Tuple					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				str					   =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		__UpperCAmelCase     :				Any					   =			CsvDatasetReader(A_,    cache_dir=A_   ).read()
		_check_csv_dataset(A_,    A_   )


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase=("train",)   ):
		assert isinstance(A_,    A_   )
		for split in splits:
				__UpperCAmelCase     :				List[Any]					   =			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 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				Any					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				int					   =			{'col_1': 'int64', 'col_2': 'int64', 'col_3': 'float64'}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				__UpperCAmelCase     :				str					   =			CsvDatasetReader({"train": csv_path},    cache_dir=A_,    keep_in_memory=A_   ).read()
		_check_csv_datasetdict(A_,    A_   )


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


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

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


def 		__UpperCamelCase		(       _UpperCAmelCase   ):

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


def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				str					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				List[str]					   =			os.path.join(A_,    "tmp.csv"   )
		__UpperCAmelCase     :				Union[str, Any]					   =			CsvDatasetReader({"train": csv_path},    cache_dir=A_   ).read()
		CsvDatasetWriter(dataset["train"],    A_,    num_proc=1   ).write()

		__UpperCAmelCase     :				int					   =			iter_csv_file(A_   )
		__UpperCAmelCase     :				Optional[Any]					   =			iter_csv_file(A_   )

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


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

		__UpperCAmelCase     :				Dict					   =			iter_csv_file(A_   )
		__UpperCAmelCase     :				List[Any]					   =			iter_csv_file(A_   )

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






def 		__UpperCamelCase		(       _UpperCAmelCase,    _UpperCAmelCase   ):
		__UpperCAmelCase     :				List[Any]					   =			tmp_path / 'cache'
		__UpperCAmelCase     :				Dict					   =			os.path.join(A_,    "tmp.csv"   )
		__UpperCAmelCase     :				Union[str, Any]					   =			CsvDatasetReader({"train": csv_path},    cache_dir=A_   ).read()
		with pytest.raises(A_   ):
				CsvDatasetWriter(dataset["train"],    A_,    num_proc=0   )












"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     (LMSDiscreteScheduler,)
    __magic_name__         =     10





    def 				lowerCamelCase__      (							self	:					Any				,		**A	:					Dict			):


      '''simple docstring'''




      a     :			Optional[Any]				=   {
          'num_train_timesteps': 1_1_0_0,
          'beta_start': 0.00_01,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
      }

      config.update(**A			)
      return config





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=A			)





    def 				lowerCamelCase__      (							self	:					List[str]			):


      '''simple docstring'''




      for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]				,		[0.00_02, 0.0_02, 0.02]			):
        self.check_over_configs(beta_start=A				,		beta_end=A			)





    def 				lowerCamelCase__      (							self	:					List[Any]			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




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





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      for t in [0, 5_0_0, 8_0_0]:
        self.check_over_forward(time_step=A			)





    def 				lowerCamelCase__      (							self	:					Any			):


      '''simple docstring'''




      a     :			Any				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			List[Any]				=   self.dummy_model()
      a     :			str				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			int				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Union[str, Any]				=   output.prev_sample

      a     :			Any				=   torch.sum(torch.abs(A			)			)
      a     :			List[str]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Optional[int]			):


      '''simple docstring'''




      a     :			List[str]				=   self.scheduler_classes[0]
      a     :			Any				=   self.get_scheduler_config(prediction_type='v_prediction'			)
      a     :			str				=   scheduler_class(**A			)

      scheduler.set_timesteps(self.num_inference_steps			)

      a     :			Tuple				=   self.dummy_model()
      a     :			Tuple				=   self.dummy_sample_deter * scheduler.init_noise_sigma

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Any				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[int]				=   scheduler.step(A				,		A				,		A			)
        a     :			Tuple				=   output.prev_sample

      a     :			Optional[Any]				=   torch.sum(torch.abs(A			)			)
      a     :			Optional[int]				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 0.00_17			) < 1E-2
      assert abs(result_mean.item() - 2.2676E-06			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Tuple			):


      '''simple docstring'''




      a     :			Optional[Any]				=   self.scheduler_classes[0]
      a     :			Union[str, Any]				=   self.get_scheduler_config()
      a     :			Union[str, Any]				=   scheduler_class(**A			)

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

      a     :			Optional[Any]				=   self.dummy_model()
      a     :			Dict				=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      a     :			Union[str, Any]				=   sample.to(A			)

      for i, t in enumerate(scheduler.timesteps			):
        a     :			Optional[int]				=   scheduler.scale_model_input(A				,		A			)

        a     :			List[str]				=   model(A				,		A			)

        a     :			Optional[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			str				=   output.prev_sample

      a     :			Dict				=   torch.sum(torch.abs(A			)			)
      a     :			Tuple				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 10_06.3_88			) < 1E-2
      assert abs(result_mean.item() - 1.31			) < 1E-3





    def 				lowerCamelCase__      (							self	:					Dict			):


      '''simple docstring'''




      a     :			Tuple				=   self.scheduler_classes[0]
      a     :			Dict				=   self.get_scheduler_config()
      a     :			Optional[Any]				=   scheduler_class(**A				,		use_karras_sigmas=A			)

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

      a     :			Any				=   self.dummy_model()
      a     :			Union[str, Any]				=   self.dummy_sample_deter.to(A			) * scheduler.init_noise_sigma
      a     :			Any				=   sample.to(A			)

      for t in scheduler.timesteps:
        a     :			int				=   scheduler.scale_model_input(A				,		A			)

        a     :			Tuple				=   model(A				,		A			)

        a     :			List[Any]				=   scheduler.step(A				,		A				,		A			)
        a     :			int				=   output.prev_sample

      a     :			Tuple				=   torch.sum(torch.abs(A			)			)
      a     :			Any				=   torch.mean(torch.abs(A			)			)

      assert abs(result_sum.item() - 38_12.99_27			) < 2E-2
      assert abs(result_mean.item() - 4.96_48			) < 1E-3




