




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class 	__lowerCAmelCase			(			__magic_name__				):
							UpperCamelCase__        =						(EulerAncestralDiscreteScheduler,)
							UpperCamelCase__        =						10
							def    lowerCamelCase__							(		self		:List[Any]	,   **__magic_name__		:Tuple       ):







								'''simple docstring'''







								a		       =							{
								    """num_train_timesteps""": 1100,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**__magic_name__       )
								return config
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								for timesteps in [10, 50, 100, 1000]:
									self.check_over_configs(num_train_timesteps=__magic_name__       )
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								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=__magic_name__	,   beta_end=__magic_name__       )
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=__magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=__magic_name__       )
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							self.scheduler_classes[0]
								a		       =							self.get_scheduler_config()
								a		       =							scheduler_class(**__magic_name__       )

								scheduler.set_timesteps(self.num_inference_steps       )

								a		       =							torch.manual_seed(0       )

								a		       =							self.dummy_model()
								a		       =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a		       =							sample.to(__magic_name__       )

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

									a		       =							model(__magic_name__	,   __magic_name__       )

									a		       =							scheduler.step(__magic_name__	,   __magic_name__	,   __magic_name__	,   generator=__magic_name__       )
									a		       =							output.prev_sample

								a		       =							torch.sum(torch.abs(__magic_name__       )       )
								a		       =							torch.mean(torch.abs(__magic_name__       )       )

								assert abs(result_sum.item() - 152.3192       ) < 1E-2
								assert abs(result_mean.item() - 0.1983       ) < 1E-3
							def    lowerCamelCase__							(		self		:Tuple       ):







								'''simple docstring'''







								a		       =							self.scheduler_classes[0]
								a		       =							self.get_scheduler_config(prediction_type="""v_prediction"""       )
								a		       =							scheduler_class(**__magic_name__       )

								scheduler.set_timesteps(self.num_inference_steps       )

								a		       =							torch.manual_seed(0       )

								a		       =							self.dummy_model()
								a		       =							self.dummy_sample_deter * scheduler.init_noise_sigma
								a		       =							sample.to(__magic_name__       )

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

									a		       =							model(__magic_name__	,   __magic_name__       )

									a		       =							scheduler.step(__magic_name__	,   __magic_name__	,   __magic_name__	,   generator=__magic_name__       )
									a		       =							output.prev_sample

								a		       =							torch.sum(torch.abs(__magic_name__       )       )
								a		       =							torch.mean(torch.abs(__magic_name__       )       )

								assert abs(result_sum.item() - 108.4439       ) < 1E-2
								assert abs(result_mean.item() - 0.1412       ) < 1E-3





							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								a		       =							self.scheduler_classes[0]
								a		       =							self.get_scheduler_config()
								a		       =							scheduler_class(**__magic_name__       )

								scheduler.set_timesteps(self.num_inference_steps	,   device=__magic_name__       )
								a		       =							torch.manual_seed(0       )

								a		       =							self.dummy_model()
								a		       =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								a		       =							sample.to(__magic_name__       )

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

									a		       =							model(__magic_name__	,   __magic_name__       )

									a		       =							scheduler.step(__magic_name__	,   __magic_name__	,   __magic_name__	,   generator=__magic_name__       )
									a		       =							output.prev_sample

								a		       =							torch.sum(torch.abs(__magic_name__       )       )
								a		       =							torch.mean(torch.abs(__magic_name__       )       )

								assert abs(result_sum.item() - 152.3192       ) < 1E-2
								assert abs(result_mean.item() - 0.1983       ) < 1E-3







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			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a       ,				a       ,				a		       =							(
								    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(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							[train_info, test_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

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

									a		       =							["""train""", """test[:33%]"""]
									a		       =							[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
									for instructions in [instructionsa, instructionsa]:
										a		       =							[reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) 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	,   __magic_name__       )
										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	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=1000	,   shard_lengths=[100] * 10       )
								a		       =							[train_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   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	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	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	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	a		       =							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	(			)   -> int:
	assert ReadInstruction("""train"""							,    to=10							,    unit="""abs"""					).to_spec() == "train[:10]"
	assert ReadInstruction("""train"""							,    from_=-80							,    to=10							,    unit="""%"""					).to_spec() == "train[-80%:10%]"

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

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

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

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

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

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





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

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

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





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

