



'''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(							lowerCAmelCase__      ):






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							List[Any]=False	):




					"""simple docstring"""







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




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def 				_lowercase	(   self  :							List[str]	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'my_name'
					SCREAMING_SNAKE_CASE_						:int										=      SplitInfo(name='train'      ,       num_examples=100	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SplitInfo(name='test'      ,       num_examples=100	)
					SCREAMING_SNAKE_CASE_						:Any										=      [train_info, test_info]
					SCREAMING_SNAKE_CASE_						:int										=      SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE	)
					split_dict.add(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      DatasetInfo(splits=SCREAMING_SNAKE_CASE	)

					with tempfile.TemporaryDirectory() as tmp_dir:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ReaderTest(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

							SCREAMING_SNAKE_CASE_						:Dict										=      'test[:33%]'
							SCREAMING_SNAKE_CASE_						:List[str]										=      Dataset(**reader.read(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	)
							self.assertEqual(dset['filename'][0]      ,       f'{name}-test'	)
							self.assertEqual(dset.num_rows      ,       33	)
							self.assertEqual(dset.num_columns      ,       1	)

							SCREAMING_SNAKE_CASE_						:List[Any]										=      ['train', 'test[:33%]']
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'	)]
							for instructions in [instructionsa, instructionsa]:
									SCREAMING_SNAKE_CASE_						:Optional[Any]										=      [reader.read(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	) for instr in instructions]
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      (Dataset(**SCREAMING_SNAKE_CASE	) 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      ,       SCREAMING_SNAKE_CASE	)
									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      ,       SCREAMING_SNAKE_CASE	)
									self.assertEqual(str(test_dset.split	)      ,       'test[:33%]'	)
									del train_dset, test_dset






			def 				_lowercase	(   self  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      'my_name'
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      SplitInfo(name='train'      ,       num_examples=1_000      ,       shard_lengths=[100] * 10	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      [train_info]
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      DatasetInfo(splits=SCREAMING_SNAKE_CASE	)

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

							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      'train[:33%]'
							SCREAMING_SNAKE_CASE_						:str										=      Dataset(**reader.read(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)	)
							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  :							int	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      SplitInfo(name='train'      ,       num_examples=100	)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      SplitInfo(name='test'      ,       num_examples=100	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      SplitDict()
					split_dict.add(SCREAMING_SNAKE_CASE	)
					split_dict.add(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:str										=      DatasetInfo(splits=SCREAMING_SNAKE_CASE	)

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

							SCREAMING_SNAKE_CASE_						:Tuple										=      [
							    {'filename': os.path.join(SCREAMING_SNAKE_CASE      ,       'train'	)},
							    {'filename': os.path.join(SCREAMING_SNAKE_CASE      ,       'test'	), 'skip': 10, 'take': 10},
							]
							SCREAMING_SNAKE_CASE_						:Tuple										=      Dataset(**reader.read_files(SCREAMING_SNAKE_CASE      ,       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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      arrow_file
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_						:List[str]										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:int										=      arrow_file
		SCREAMING_SNAKE_CASE_						:int										=      ArrowReader('' ,		SCREAMING_SNAKE_CASE     )
		with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_						:int										=      reader.read_files([{'filename': filename}] ,		in_memory=SCREAMING_SNAKE_CASE     )
		assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
		SCREAMING_SNAKE_CASE_						:List[str]										=      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  SCREAMING_SNAKE_CASE__ (				):
		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_						:List[str]										=      'train+test'
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE     ).to_spec() == spec_train_test

		SCREAMING_SNAKE_CASE_						:List[Any]										=      'train[2:10]'
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE     ).to_spec() == spec_train_abs

		SCREAMING_SNAKE_CASE_						:int										=      'train[15%:-20%]'
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE     ).to_spec() == spec_train_pct

		SCREAMING_SNAKE_CASE_						:List[str]										=      'train[:10%](closest)'
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE     ).to_spec() == "train[:10%]"

		SCREAMING_SNAKE_CASE_						:Dict										=      'train[:10%](pct1_dropremainder)'
		assert ReadInstruction.from_spec(SCREAMING_SNAKE_CASE     ).to_spec() == spec_train_pct_rounding

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







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Tuple										=      'dummy'
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      [SplitInfo(name='train' ,		num_examples=100     )]
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'train[:33%]'
		SCREAMING_SNAKE_CASE_						:Any										=      'arrow'
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      'prefix'

		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      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_						:List[str]										=      [SplitInfo(name='train' ,		num_examples=100 ,		shard_lengths=[10] * 10     )]
		SCREAMING_SNAKE_CASE_						:Any										=      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  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Any										=      [SplitInfo(name='train' ,		num_examples=100     )]
		SCREAMING_SNAKE_CASE_						:int										=      'train'
		SCREAMING_SNAKE_CASE_						:Dict										=      'arrow'
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      'prefix_path'
		with pytest.raises(SCREAMING_SNAKE_CASE     ):
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      make_file_instructions(SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     )





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


SCREAMING_SNAKE_CASE__     : int				=   {"""configuration_bert_generation""": ["""BertGenerationConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : List[Any]				=   ["""BertGenerationTokenizer"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__     : Optional[Any]				=   [
							    """BertGenerationDecoder""",
							    """BertGenerationEncoder""",
							    """BertGenerationPreTrainedModel""",
							    """load_tf_weights_in_bert_generation""",
							]


if TYPE_CHECKING:
							from .configuration_bert_generation import BertGenerationConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bert_generation import BertGenerationTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bert_generation import (
														    BertGenerationDecoder,
														    BertGenerationEncoder,
														    BertGenerationPreTrainedModel,
														    load_tf_weights_in_bert_generation,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__     : Any				=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
