
"""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 	UpperCamelCase      (			snake_case__ ):

				"""simple docstring"""



				def 				A   (							self						,      lowercase__						,      lowercase__=False     )       ->       List[Any]:
									"""simple docstring"""







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






class 	UpperCamelCase      (			snake_case__ ):

				"""simple docstring"""



				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									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(_A     )
									split_dict.add(_A     )
									SCREAMING_SNAKE_CASE           =      DatasetInfo(splits=_A     )

									with tempfile.TemporaryDirectory() as tmp_dir:
														SCREAMING_SNAKE_CASE           =      ReaderTest(_A						,      _A     )

														SCREAMING_SNAKE_CASE           =      'test[:33%]'
														SCREAMING_SNAKE_CASE           =      Dataset(**reader.read(_A						,      _A						,      _A     )     )
														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(_A						,      _A						,      _A     ) for instr in instructions]
																			SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      (Dataset(**_A     ) 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						,      _A     )
																			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						,      _A     )
																			self.assertEqual(str(test_dset.split     )						,      'test[:33%]'     )
																			del train_dset, test_dset


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







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

									with tempfile.TemporaryDirectory() as tmp_dir:
														SCREAMING_SNAKE_CASE           =      ReaderTest(_A						,      _A     )

														SCREAMING_SNAKE_CASE           =      'train[:33%]'
														SCREAMING_SNAKE_CASE           =      Dataset(**reader.read(_A						,      _A						,      _A     )     )
														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 				A   (							self     )       ->       str:
									"""simple docstring"""







									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(_A     )
									split_dict.add(_A     )
									SCREAMING_SNAKE_CASE           =      DatasetInfo(splits=_A     )

									with tempfile.TemporaryDirectory() as tmp_dir:
														SCREAMING_SNAKE_CASE           =      ReaderTest(_A						,      _A     )

														SCREAMING_SNAKE_CASE           =      [
														    {'filename': os.path.join(_A						,      'train'     )},
														    {'filename': os.path.join(_A						,      'test'     ), 'skip': 10, 'take': 10},
														]
														SCREAMING_SNAKE_CASE           =      Dataset(**reader.read_files(_A						,      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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					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     UpperCamelCase_    (						):
					assert ReadInstruction('train',	to=1_0,	unit='abs'     ).to_spec() == "train[:10]"
					assert ReadInstruction('train',	from_=-8_0,	to=1_0,	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     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      'dummy'
					SCREAMING_SNAKE_CASE           =      [SplitInfo(name='train',	num_examples=1_0_0     )]
					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 == 3_3
					assert file_instructions.file_instructions == [
					    {"filename": os.path.join(SCREAMING_SNAKE_CASE_,	f'''{name}-train.arrow'''     ), "skip": 0, "take": 3_3}
					]

					SCREAMING_SNAKE_CASE           =      [SplitInfo(name='train',	num_examples=1_0_0,	shard_lengths=[1_0] * 1_0     )]
					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 == 3_3
					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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      [SplitInfo(name='train',	num_examples=1_0_0     )]
					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_     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__            =     {
    """configuration_marian""": ["""MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarianConfig""", """MarianOnnxConfig"""],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""MarianTokenizer"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     [
					    """MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """MarianForCausalLM""",
					    """MarianModel""",
					    """MarianMTModel""",
					    """MarianPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""TFMarianModel""", """TFMarianMTModel""", """TFMarianPreTrainedModel"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__            =     ["""FlaxMarianModel""", """FlaxMarianMTModel""", """FlaxMarianPreTrainedModel"""]

if TYPE_CHECKING:
					from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_marian import MarianTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_marian import (
										    MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MarianForCausalLM,
										    MarianModel,
										    MarianMTModel,
										    MarianPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
					import sys

					UpperCamelCase__            =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




