






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				(   snake_case__):





	"""simple docstring"""






	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase=False					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase      ,				_UpperCAmelCase             =    (
							    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()
							_UpperCAmelCase             =    pa.Table.from_pydict({'filename': [Path(UpperCAmelCase					).name] * 100}					)
							if take == -1:
													_UpperCAmelCase             =    len(UpperCAmelCase					) - skip
							if skip is not None and take is not None:
													_UpperCAmelCase             =    pa_table.slice(UpperCAmelCase ,					UpperCAmelCase					)
							return pa_table





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	def  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    'my_name'
							_UpperCAmelCase             =    SplitInfo(name='train' ,					num_examples=100					)
							_UpperCAmelCase             =    SplitInfo(name='test' ,					num_examples=100					)
							_UpperCAmelCase             =    [train_info, test_info]
							_UpperCAmelCase             =    SplitDict()
							split_dict.add(UpperCAmelCase					)
							split_dict.add(UpperCAmelCase					)
							_UpperCAmelCase             =    DatasetInfo(splits=UpperCAmelCase					)

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

													_UpperCAmelCase             =    'test[:33%]'
													_UpperCAmelCase             =    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					)

													_UpperCAmelCase             =    ['train', 'test[:33%]']
													_UpperCAmelCase             =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'					)]
													for instructions in [instructionsa, instructionsa]:
																			_UpperCAmelCase             =    [reader.read(UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase					) for instr in instructions]
																			_UpperCAmelCase      ,				_UpperCAmelCase             =    (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  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    'my_name'
							_UpperCAmelCase             =    SplitInfo(name='train' ,					num_examples=1000 ,					shard_lengths=[100] * 10					)
							_UpperCAmelCase             =    [train_info]
							_UpperCAmelCase             =    SplitDict()
							split_dict.add(UpperCAmelCase					)
							_UpperCAmelCase             =    DatasetInfo(splits=UpperCAmelCase					)

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

													_UpperCAmelCase             =    'train[:33%]'
													_UpperCAmelCase             =    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  UpperCamelCase					(							self					):
							"""simple docstring"""







							_UpperCAmelCase             =    SplitInfo(name='train' ,					num_examples=100					)
							_UpperCAmelCase             =    SplitInfo(name='test' ,					num_examples=100					)
							_UpperCAmelCase             =    SplitDict()
							split_dict.add(UpperCAmelCase					)
							split_dict.add(UpperCAmelCase					)
							_UpperCAmelCase             =    DatasetInfo(splits=UpperCAmelCase					)

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

													_UpperCAmelCase             =    [
													    {'filename': os.path.join(UpperCAmelCase ,					'train'					)},
													    {'filename': os.path.join(UpperCAmelCase ,					'test'					), 'skip': 10, 'take': 10},
													]
													_UpperCAmelCase             =    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					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Union[str, Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    arrow_file
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase             =    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			)->						List[Any]:





						"""simple docstring"""



						_UpperCAmelCase             =    arrow_file
						_UpperCAmelCase             =    ArrowReader(''				,	__lowerCAmelCase			)
						with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
												_UpperCAmelCase             =    reader.read_files([{'filename': filename}]				,	in_memory=__lowerCAmelCase			)
						assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
						_UpperCAmelCase             =    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					(   )->						List[str]:





						"""simple docstring"""



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

						_UpperCAmelCase             =    'train+test'
						assert ReadInstruction.from_spec(__lowerCAmelCase			).to_spec() == spec_train_test

						_UpperCAmelCase             =    'train[2:10]'
						assert ReadInstruction.from_spec(__lowerCAmelCase			).to_spec() == spec_train_abs

						_UpperCAmelCase             =    'train[15%:-20%]'
						assert ReadInstruction.from_spec(__lowerCAmelCase			).to_spec() == spec_train_pct

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

						_UpperCAmelCase             =    'train[:10%](pct1_dropremainder)'
						assert ReadInstruction.from_spec(__lowerCAmelCase			).to_spec() == spec_train_pct_rounding

						_UpperCAmelCase             =    'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
						assert ReadInstruction.from_spec(__lowerCAmelCase			).to_spec() == spec_train_test_pct_rounding


def __A					(   )->						int:





						"""simple docstring"""



						_UpperCAmelCase             =    'dummy'
						_UpperCAmelCase             =    [SplitInfo(name='train'				,	num_examples=100			)]
						_UpperCAmelCase             =    'train[:33%]'
						_UpperCAmelCase             =    'arrow'
						_UpperCAmelCase             =    'prefix'

						_UpperCAmelCase             =    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}
						]

						_UpperCAmelCase             =    [SplitInfo(name='train'				,	num_examples=100				,	shard_lengths=[10] * 10			)]
						_UpperCAmelCase             =    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			)->						Any:





						"""simple docstring"""



						_UpperCAmelCase             =    [SplitInfo(name='train'				,	num_examples=100			)]
						_UpperCAmelCase             =    'train'
						_UpperCAmelCase             =    'arrow'
						_UpperCAmelCase             =    'prefix_path'
						with pytest.raises(__lowerCAmelCase			):
												_UpperCAmelCase             =    make_file_instructions(__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_a   			=						{'''configuration_qdqbert''': ['''QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''QDQBertConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_a   			=						[
		    '''QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''QDQBertForMaskedLM''',
		    '''QDQBertForMultipleChoice''',
		    '''QDQBertForNextSentencePrediction''',
		    '''QDQBertForQuestionAnswering''',
		    '''QDQBertForSequenceClassification''',
		    '''QDQBertForTokenClassification''',
		    '''QDQBertLayer''',
		    '''QDQBertLMHeadModel''',
		    '''QDQBertModel''',
		    '''QDQBertPreTrainedModel''',
		    '''load_tf_weights_in_qdqbert''',
		]


if TYPE_CHECKING:
		from .configuration_qdqbert import QDQBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, QDQBertConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_qdqbert import (
				    QDQBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    QDQBertForMaskedLM,
				    QDQBertForMultipleChoice,
				    QDQBertForNextSentencePrediction,
				    QDQBertForQuestionAnswering,
				    QDQBertForSequenceClassification,
				    QDQBertForTokenClassification,
				    QDQBertLayer,
				    QDQBertLMHeadModel,
				    QDQBertModel,
				    QDQBertPreTrainedModel,
				    load_tf_weights_in_qdqbert,
				)


else:
		import sys

		_a   			=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
