




'''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 	SCREAMING_SNAKE_CASE(    snake_case__					):

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__=False					)   -> Dict:

           """simple docstring"""



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






class 	SCREAMING_SNAKE_CASE(    snake_case__					):

       def    snake_case__      (   self					)   -> List[str]:

           """simple docstring"""



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

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

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

               __lowercase          =						['train', 'test[:33%]']
               __lowercase          =						[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""					)]
               for instructions in [instructionsa, instructionsa]:
                   __lowercase          =						[reader.read(_A ,		_A ,		_A					) for instr in instructions]
                   __lowercase          =						(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    snake_case__      (   self					)   -> Tuple:

           """simple docstring"""



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

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

               __lowercase          =						'train[:33%]'
               __lowercase          =						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    snake_case__      (   self					)   -> Any:

           """simple docstring"""



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

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

               __lowercase          =						[
                   {'filename': os.path.join(_A ,		"""train"""					)},
                   {'filename': os.path.join(_A ,		"""test"""					), 'skip': 10, 'take': 10},
               ]
               __lowercase          =						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     snake_case_  (       a__		:     List[str]   ,a__		:     Dict   ,a__		:     List[Any]      ):



    """simple docstring"""




    __lowercase          =						arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        __lowercase          =						ArrowReader.read_table(a__   ,in_memory=a__      )
    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     snake_case_  (       a__		:     int   ,a__		:     Tuple   ,a__		:     Tuple      ):



    """simple docstring"""




    __lowercase          =						arrow_file
    __lowercase          =						ArrowReader(""""""   ,a__      )
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        __lowercase          =						reader.read_files([{"""filename""": filename}]   ,in_memory=a__      )
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    __lowercase          =						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     snake_case_  (       ):



    """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%]"

    __lowercase          =						'train+test'
    assert ReadInstruction.from_spec(a__      ).to_spec() == spec_train_test

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

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

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

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

    __lowercase          =						'train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)'
    assert ReadInstruction.from_spec(a__      ).to_spec() == spec_train_test_pct_rounding
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						'dummy'
    __lowercase          =						[SplitInfo(name="""train"""   ,num_examples=1_00      )]
    __lowercase          =						'train[:33%]'
    __lowercase          =						'arrow'
    __lowercase          =						'prefix'

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

    __lowercase          =						[SplitInfo(name="""train"""   ,num_examples=1_00   ,shard_lengths=[10] * 10      )]
    __lowercase          =						make_file_instructions(a__   ,a__   ,a__   ,a__   ,a__      )
    assert isinstance(a__   ,a__      )
    assert file_instructions.num_examples == 33
    assert file_instructions.file_instructions == [
        {"filename": os.path.join(a__   ,f'{name}-train-00000-of-00010.arrow'      ), "skip": 0, "take": -1},
        {"filename": os.path.join(a__   ,f'{name}-train-00001-of-00010.arrow'      ), "skip": 0, "take": -1},
        {"filename": os.path.join(a__   ,f'{name}-train-00002-of-00010.arrow'      ), "skip": 0, "take": -1},
        {"filename": os.path.join(a__   ,f'{name}-train-00003-of-00010.arrow'      ), "skip": 0, "take": 3},
    ]



@pytest.mark.parametrize("""name, expected_exception"""   ,[(None, TypeError), ("""""", ValueError)]      )
def     snake_case_  (       a__		:     Optional[int]   ,a__		:     Dict      ):



    """simple docstring"""




    __lowercase          =						[SplitInfo(name="""train"""   ,num_examples=1_00      )]
    __lowercase          =						'train'
    __lowercase          =						'arrow'
    __lowercase          =						'prefix_path'
    with pytest.raises(a__      ):
        __lowercase          =						make_file_instructions(a__   ,a__   ,a__   ,a__   ,a__      )










'''simple docstring'''
from typing import TYPE_CHECKING

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


A							:				Tuple			     =	{
    """configuration_dpr""": ["""DPR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPRConfig"""],
    """tokenization_dpr""": [
        """DPRContextEncoderTokenizer""",
        """DPRQuestionEncoderTokenizer""",
        """DPRReaderOutput""",
        """DPRReaderTokenizer""",
    ],
}


try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				str			     =	[
      """DPRContextEncoderTokenizerFast""",
      """DPRQuestionEncoderTokenizerFast""",
      """DPRReaderTokenizerFast""",
  ]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DPRContextEncoder""",
      """DPRPretrainedContextEncoder""",
      """DPRPreTrainedModel""",
      """DPRPretrainedQuestionEncoder""",
      """DPRPretrainedReader""",
      """DPRQuestionEncoder""",
      """DPRReader""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  A							:				Tuple			     =	[
      """TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFDPRContextEncoder""",
      """TFDPRPretrainedContextEncoder""",
      """TFDPRPretrainedQuestionEncoder""",
      """TFDPRPretrainedReader""",
      """TFDPRQuestionEncoder""",
      """TFDPRReader""",
  ]


if TYPE_CHECKING:
  from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
  from .tokenization_dpr import (
      DPRContextEncoderTokenizer,
      DPRQuestionEncoderTokenizer,
      DPRReaderOutput,
      DPRReaderTokenizer,
  )

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_dpr_fast import (
        DPRContextEncoderTokenizerFast,
        DPRQuestionEncoderTokenizerFast,
        DPRReaderTokenizerFast,
    )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dpr import (
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPRContextEncoder,
        DPRPretrainedContextEncoder,
        DPRPreTrainedModel,
        DPRPretrainedQuestionEncoder,
        DPRPretrainedReader,
        DPRQuestionEncoder,
        DPRReader,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_dpr import (
        TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDPRContextEncoder,
        TFDPRPretrainedContextEncoder,
        TFDPRPretrainedQuestionEncoder,
        TFDPRPretrainedReader,
        TFDPRQuestionEncoder,
        TFDPRReader,
    )

else:
  import sys

  A							:				int			     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




