
'''simple docstring'''







import os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=False   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			(
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(_UpperCAmelCase   )       ,    "wb"   ).close()
								_SCREAMING_SNAKE_CASE					=			pa.Table.from_pydict({"filename": [Path(_UpperCAmelCase   ).name] * 100}   )
								if take == -1:
														_SCREAMING_SNAKE_CASE					=			len(_UpperCAmelCase   ) - skip
								if skip is not None and take is not None:
														_SCREAMING_SNAKE_CASE					=			pa_table.slice(_UpperCAmelCase       ,    _UpperCAmelCase   )
								return pa_table


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"my_name"
								_SCREAMING_SNAKE_CASE					=			SplitInfo(name="train"       ,    num_examples=100   )
								_SCREAMING_SNAKE_CASE					=			SplitInfo(name="test"       ,    num_examples=100   )
								_SCREAMING_SNAKE_CASE					=			[train_info, test_info]
								_SCREAMING_SNAKE_CASE					=			SplitDict()
								split_dict.add(_UpperCAmelCase   )
								split_dict.add(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			DatasetInfo(splits=_UpperCAmelCase   )

								with tempfile.TemporaryDirectory() as tmp_dir:
														_SCREAMING_SNAKE_CASE					=			ReaderTest(_UpperCAmelCase       ,    _UpperCAmelCase   )

														_SCREAMING_SNAKE_CASE					=			"test[:33%]"
														_SCREAMING_SNAKE_CASE					=			Dataset(**reader.read(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )   )
														self.assertEqual(dset["filename"][0]       ,    F'{name}-test'   )
														self.assertEqual(dset.num_rows       ,    33   )
														self.assertEqual(dset.num_columns       ,    1   )

														_SCREAMING_SNAKE_CASE					=			["train", "test[:33%]"]
														_SCREAMING_SNAKE_CASE					=			[Split.TRAIN, ReadInstruction.from_spec("test[:33%]"   )]
														for instructions in [instructionsa, instructionsa]:
																				_SCREAMING_SNAKE_CASE					=			[reader.read(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   ) for instr in instructions]
																				_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			(Dataset(**_UpperCAmelCase   ) for dataset_kwargs in datasets_kwargs)
																				self.assertEqual(train_dset["filename"][0]       ,    F'{name}-train'   )
																				self.assertEqual(train_dset.num_rows       ,    100   )
																				self.assertEqual(train_dset.num_columns       ,    1   )
																				self.assertIsInstance(train_dset.split       ,    _UpperCAmelCase   )
																				self.assertEqual(str(train_dset.split   )       ,    "train"   )
																				self.assertEqual(test_dset["filename"][0]       ,    F'{name}-test'   )
																				self.assertEqual(test_dset.num_rows       ,    33   )
																				self.assertEqual(test_dset.num_columns       ,    1   )
																				self.assertIsInstance(test_dset.split       ,    _UpperCAmelCase   )
																				self.assertEqual(str(test_dset.split   )       ,    "test[:33%]"   )
																				del train_dset, test_dset




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			"my_name"
								_SCREAMING_SNAKE_CASE					=			SplitInfo(name="train"       ,    num_examples=1_000       ,    shard_lengths=[100] * 10   )
								_SCREAMING_SNAKE_CASE					=			[train_info]
								_SCREAMING_SNAKE_CASE					=			SplitDict()
								split_dict.add(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			DatasetInfo(splits=_UpperCAmelCase   )

								with tempfile.TemporaryDirectory() as tmp_dir:
														_SCREAMING_SNAKE_CASE					=			ReaderTest(_UpperCAmelCase       ,    _UpperCAmelCase   )

														_SCREAMING_SNAKE_CASE					=			"train[:33%]"
														_SCREAMING_SNAKE_CASE					=			Dataset(**reader.read(_UpperCAmelCase       ,    _UpperCAmelCase       ,    _UpperCAmelCase   )   )
														self.assertEqual(dset["filename"][0]       ,    F'{name}-train-00000-of-00010'   )
														self.assertEqual(dset["filename"][-1]       ,    F'{name}-train-00003-of-00010'   )
														self.assertEqual(dset.num_rows       ,    330   )
														self.assertEqual(dset.num_columns       ,    1   )




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			SplitInfo(name="train"       ,    num_examples=100   )
								_SCREAMING_SNAKE_CASE					=			SplitInfo(name="test"       ,    num_examples=100   )
								_SCREAMING_SNAKE_CASE					=			SplitDict()
								split_dict.add(_UpperCAmelCase   )
								split_dict.add(_UpperCAmelCase   )
								_SCREAMING_SNAKE_CASE					=			DatasetInfo(splits=_UpperCAmelCase   )

								with tempfile.TemporaryDirectory() as tmp_dir:
														_SCREAMING_SNAKE_CASE					=			ReaderTest(_UpperCAmelCase       ,    _UpperCAmelCase   )

														_SCREAMING_SNAKE_CASE					=			[
														    {"filename": os.path.join(_UpperCAmelCase       ,    "train"   )},
														    {"filename": os.path.join(_UpperCAmelCase       ,    "test"   ), "skip": 10, "take": 10},
														]
														_SCREAMING_SNAKE_CASE					=			Dataset(**reader.read_files(_UpperCAmelCase       ,    original_instructions="train+test[10:20]"   )   )
														self.assertEqual(dset.num_rows       ,    110   )
														self.assertEqual(dset.num_columns       ,    1   )
														del dset



@pytest.mark.parametrize("in_memory"						,      [False, True]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: Union[str, Any]						,      _SCREAMING_SNAKE_CASE		: Optional[int]	):
						_SCREAMING_SNAKE_CASE					=			arrow_file
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												_SCREAMING_SNAKE_CASE					=			ArrowReader.read_table(_SCREAMING_SNAKE_CASE						,      in_memory=_SCREAMING_SNAKE_CASE	)
						assert table.shape == dataset.data.shape
						assert set(table.column_names	) == set(dataset.data.column_names	)
						assert dict(table.to_pydict()	) == dict(dataset.data.to_pydict()	)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory"						,      [False, True]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: List[str]						,      _SCREAMING_SNAKE_CASE		: Dict	):
						_SCREAMING_SNAKE_CASE					=			arrow_file
						_SCREAMING_SNAKE_CASE					=			ArrowReader(""						,      _SCREAMING_SNAKE_CASE	)
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												_SCREAMING_SNAKE_CASE					=			reader.read_files([{"filename": filename}]						,      in_memory=_SCREAMING_SNAKE_CASE	)
						assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
						_SCREAMING_SNAKE_CASE					=			dataset_kwargs["arrow_table"]
						assert table.shape == dataset.data.shape
						assert set(table.column_names	) == set(dataset.data.column_names	)
						assert dict(table.to_pydict()	) == dict(dataset.data.to_pydict()	)  # to_pydict returns OrderedDict



def       _a		(						):
						assert ReadInstruction("train"						,      to=10						,      unit="abs"	).to_spec() == "train[:10]"
						assert ReadInstruction("train"						,      from_=-80						,      to=10						,      unit="%"	).to_spec() == "train[-80%:10%]"

						_SCREAMING_SNAKE_CASE					=			"train+test"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == spec_train_test

						_SCREAMING_SNAKE_CASE					=			"train[2:10]"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == spec_train_abs

						_SCREAMING_SNAKE_CASE					=			"train[15%:-20%]"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == spec_train_pct

						_SCREAMING_SNAKE_CASE					=			"train[:10%](closest)"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == "train[:10%]"

						_SCREAMING_SNAKE_CASE					=			"train[:10%](pct1_dropremainder)"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == spec_train_pct_rounding

						_SCREAMING_SNAKE_CASE					=			"train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
						assert ReadInstruction.from_spec(_SCREAMING_SNAKE_CASE	).to_spec() == spec_train_test_pct_rounding



def       _a		(						):
						_SCREAMING_SNAKE_CASE					=			"dummy"
						_SCREAMING_SNAKE_CASE					=			[SplitInfo(name="train"						,      num_examples=100	)]
						_SCREAMING_SNAKE_CASE					=			"train[:33%]"
						_SCREAMING_SNAKE_CASE					=			"arrow"
						_SCREAMING_SNAKE_CASE					=			"prefix"

						_SCREAMING_SNAKE_CASE					=			make_file_instructions(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						assert isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						assert file_instructions.num_examples == 33
						assert file_instructions.file_instructions == [
						    {"filename": os.path.join(_SCREAMING_SNAKE_CASE						,      F'{name}-train.arrow'	), "skip": 0, "take": 33}
						]

						_SCREAMING_SNAKE_CASE					=			[SplitInfo(name="train"						,      num_examples=100						,      shard_lengths=[10] * 10	)]
						_SCREAMING_SNAKE_CASE					=			make_file_instructions(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						assert isinstance(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)
						assert file_instructions.num_examples == 33
						assert file_instructions.file_instructions == [
						    {"filename": os.path.join(_SCREAMING_SNAKE_CASE						,      F'{name}-train-00000-of-00010.arrow'	), "skip": 0, "take": -1},
						    {"filename": os.path.join(_SCREAMING_SNAKE_CASE						,      F'{name}-train-00001-of-00010.arrow'	), "skip": 0, "take": -1},
						    {"filename": os.path.join(_SCREAMING_SNAKE_CASE						,      F'{name}-train-00002-of-00010.arrow'	), "skip": 0, "take": -1},
						    {"filename": os.path.join(_SCREAMING_SNAKE_CASE						,      F'{name}-train-00003-of-00010.arrow'	), "skip": 0, "take": 3},
						]




@pytest.mark.parametrize("name, expected_exception"						,      [(None, TypeError), ("", ValueError)]	)
def       _a		(						_SCREAMING_SNAKE_CASE		: Any						,      _SCREAMING_SNAKE_CASE		: int	):
						_SCREAMING_SNAKE_CASE					=			[SplitInfo(name="train"						,      num_examples=100	)]
						_SCREAMING_SNAKE_CASE					=			"train"
						_SCREAMING_SNAKE_CASE					=			"arrow"
						_SCREAMING_SNAKE_CASE					=			"prefix_path"
						with pytest.raises(_SCREAMING_SNAKE_CASE	):
												_SCREAMING_SNAKE_CASE					=			make_file_instructions(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE	)

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			UpperCamelCase_				(  A     ):




      '''simple docstring'''







      a		:Any						 =      (LMSDiscreteScheduler,)
      a		:Dict						 =      10







      def lowercase__		( self					,					**_UpperCAmelCase):
            lowerCAmelCase_						=      {
                '''num_train_timesteps''': 1_100,
                '''beta_start''': 0.0001,
                '''beta_end''': 0.02,
                '''beta_schedule''': '''linear''',
            }

            config.update(**_UpperCAmelCase)
            return config







      def lowercase__		( self):
            for timesteps in [10, 50, 100, 1_000]:
                  self.check_over_configs(num_train_timesteps=_UpperCAmelCase)







      def lowercase__		( self):
            for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]					,					[0.0002, 0.002, 0.02]):
                  self.check_over_configs(beta_start=_UpperCAmelCase					,					beta_end=_UpperCAmelCase)







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







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







      def lowercase__		( self):
            for t in [0, 500, 800]:
                  self.check_over_forward(time_step=_UpperCAmelCase)







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config(prediction_type='''v_prediction''')
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

            scheduler.set_timesteps(self.num_inference_steps)

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 0.0017) < 1E-2
            assert abs(result_mean.item() - 2.2676E-06) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for i, t in enumerate(scheduler.timesteps):
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 1006.388) < 1E-2
            assert abs(result_mean.item() - 1.31) < 1E-3







      def lowercase__		( self):
            lowerCAmelCase_						=      self.scheduler_classes[0]
            lowerCAmelCase_						=      self.get_scheduler_config()
            lowerCAmelCase_						=      scheduler_class(**_UpperCAmelCase					,					use_karras_sigmas=_UpperCAmelCase)

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

            lowerCAmelCase_						=      self.dummy_model()
            lowerCAmelCase_						=      self.dummy_sample_deter.to(_UpperCAmelCase) * scheduler.init_noise_sigma
            lowerCAmelCase_						=      sample.to(_UpperCAmelCase)

            for t in scheduler.timesteps:
                  lowerCAmelCase_						=      scheduler.scale_model_input(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      model(_UpperCAmelCase					,					_UpperCAmelCase)

                  lowerCAmelCase_						=      scheduler.step(_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase)
                  lowerCAmelCase_						=      output.prev_sample

            lowerCAmelCase_						=      torch.sum(torch.abs(_UpperCAmelCase))
            lowerCAmelCase_						=      torch.mean(torch.abs(_UpperCAmelCase))

            assert abs(result_sum.item() - 3812.9927) < 2E-2
            assert abs(result_mean.item() - 4.9648) < 1E-3



