
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 						snake_case_     ( self						:    Optional[int]			,    _snake_case						:    Any			,    _snake_case						:    Optional[int]=False			):
          __lowercase						:     List[str]		      =    (
              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						:     Dict		      =    pa.Table.from_pydict({'''filename''': [Path(_A			).name] * 100}			)
          if take == -1:
              __lowercase						:     Union[str, Any]		      =    len(_A			) - skip
          if skip is not None and take is not None:
              __lowercase						:     int		      =    pa_table.slice(_A			,    _A			)
          return pa_table






class   __lowerCAmelCase		(		snake_case__ ):



      """simple docstring"""







      def 						snake_case_     ( self						:    Dict			):
          __lowercase						:     Dict		      =    'my_name'
          __lowercase						:     str		      =    SplitInfo(name='''train'''			,    num_examples=100			)
          __lowercase						:     List[Any]		      =    SplitInfo(name='''test'''			,    num_examples=100			)
          __lowercase						:     Any		      =    [train_info, test_info]
          __lowercase						:     Tuple		      =    SplitDict()
          split_dict.add(_A			)
          split_dict.add(_A			)
          __lowercase						:     Optional[Any]		      =    DatasetInfo(splits=_A			)

          with tempfile.TemporaryDirectory() as tmp_dir:
              __lowercase						:     Optional[Any]		      =    ReaderTest(_A			,    _A			)

              __lowercase						:     int		      =    'test[:33%]'
              __lowercase						:     Optional[int]		      =    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						:     List[Any]		      =    ['train', 'test[:33%]']
              __lowercase						:     List[Any]		      =    [Split.TRAIN, ReadInstruction.from_spec('''test[:33%]'''			)]
              for instructions in [instructionsa, instructionsa]:
                  __lowercase						:     Union[str, Any]		      =    [reader.read(_A			,    _A			,    _A			) for instr in instructions]
                  __lowercase						:     Tuple		      =    (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						:    List[Any]			):
          __lowercase						:     Any		      =    'my_name'
          __lowercase						:     List[Any]		      =    SplitInfo(name='''train'''			,    num_examples=1000			,    shard_lengths=[100] * 10			)
          __lowercase						:     Dict		      =    [train_info]
          __lowercase						:     Optional[Any]		      =    SplitDict()
          split_dict.add(_A			)
          __lowercase						:     Dict		      =    DatasetInfo(splits=_A			)

          with tempfile.TemporaryDirectory() as tmp_dir:
              __lowercase						:     List[Any]		      =    ReaderTest(_A			,    _A			)

              __lowercase						:     Any		      =    'train[:33%]'
              __lowercase						:     int		      =    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						:    int			):
          __lowercase						:     str		      =    SplitInfo(name='''train'''			,    num_examples=100			)
          __lowercase						:     List[str]		      =    SplitInfo(name='''test'''			,    num_examples=100			)
          __lowercase						:     Tuple		      =    SplitDict()
          split_dict.add(_A			)
          split_dict.add(_A			)
          __lowercase						:     Any		      =    DatasetInfo(splits=_A			)

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

              __lowercase						:     int		      =    [
                  {'filename': os.path.join(_A			,    '''train'''			)},
                  {'filename': os.path.join(_A			,    '''test'''			), 'skip': 10, 'take': 10},
              ]
              __lowercase						:     List[str]		      =    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_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  Union[str, Any]:
    __lowercase						:     List[str]		      =    arrow_file
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        __lowercase						:     Tuple		      =    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       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)	->  Dict:
    __lowercase						:     List[str]		      =    arrow_file
    __lowercase						:     int		      =    ArrowReader(''''''		,   __lowerCAmelCase		)
    with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
        __lowercase						:     Dict		      =    reader.read_files([{'''filename''': filename}]		,   in_memory=__lowerCAmelCase		)
    assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
    __lowercase						:     Tuple		      =    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_					(  )	->  Optional[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%]"

    __lowercase						:     Dict		      =    'train+test'
    assert ReadInstruction.from_spec(__lowerCAmelCase		).to_spec() == spec_train_test

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

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

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

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

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


def       UpperCAmelCase_					(  )	->  Dict:
    __lowercase						:     Dict		      =    'dummy'
    __lowercase						:     Any		      =    [SplitInfo(name='''train'''		,   num_examples=100		)]
    __lowercase						:     Any		      =    'train[:33%]'
    __lowercase						:     List[str]		      =    'arrow'
    __lowercase						:     Tuple		      =    'prefix'

    __lowercase						:     Any		      =    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}
    ]

    __lowercase						:     str		      =    [SplitInfo(name='''train'''		,   num_examples=100		,   shard_lengths=[10] * 10		)]
    __lowercase						:     Optional[int]		      =    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       UpperCAmelCase_					(  __lowerCAmelCase		,   __lowerCAmelCase		)	->  int:
    __lowercase						:     List[str]		      =    [SplitInfo(name='''train'''		,   num_examples=100		)]
    __lowercase						:     str		      =    'train'
    __lowercase						:     Dict		      =    'arrow'
    __lowercase						:     List[str]		      =    'prefix_path'
    with pytest.raises(__lowerCAmelCase		):
        __lowercase						:     List[Any]		      =    make_file_instructions(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		)




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCAmelCase   :     Dict      =						logging.get_logger(__name__)


class   __lowerCAmelCase		(		lowerCAmelCase_ ):



      """simple docstring"""







      A__		:     Union[str, Any]       				=      ['''pixel_values''']




      def __init__( self						:    int			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Dict[str, int]] = None			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    bool = True			,    _snake_case						:    Union[int, float] = 1 / 255			,    _snake_case						:    bool = True			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    **_snake_case						:    List[str]			,    ):
          super().__init__(**_snake_case			)
          __lowercase						:     Tuple		      =    size if size is not None else {'''height''': 224, '''width''': 224}
          __lowercase						:     List[str]		      =    get_size_dict(_snake_case			)
          __lowercase						:     Union[str, Any]		      =    do_resize
          __lowercase						:     int		      =    do_rescale
          __lowercase						:     List[Any]		      =    do_normalize
          __lowercase						:     Dict		      =    size
          __lowercase						:     Any		      =    resample
          __lowercase						:     int		      =    rescale_factor
          __lowercase						:     Optional[Any]		      =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          __lowercase						:     Optional[Any]		      =    image_std if image_std is not None else IMAGENET_STANDARD_STD




      def 						snake_case_     ( self						:    int			,    _snake_case						:    np.ndarray			,    _snake_case						:    Dict[str, int]			,    _snake_case						:    PILImageResampling = PILImageResampling.BILINEAR			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Optional[int]			,    ):
          __lowercase						:     List[Any]		      =    get_size_dict(_snake_case			)
          if "height" not in size or "width" not in size:
              raise ValueError(F'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'			)
          return resize(
              _snake_case			,    size=(size['''height'''], size['''width'''])			,    resample=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Union[str, Any]			,    _snake_case						:    np.ndarray			,    _snake_case						:    float			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Dict			):
          return rescale(_snake_case			,    scale=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Any			,    _snake_case						:    np.ndarray			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Union[float, List[float]]			,    _snake_case						:    Optional[Union[str, ChannelDimension]] = None			,    **_snake_case						:    Any			,    ):
          return normalize(_snake_case			,    mean=_snake_case			,    std=_snake_case			,    data_format=_snake_case			,    **_snake_case			)




      def 						snake_case_     ( self						:    Dict			,    _snake_case						:    ImageInput			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Dict[str, int] = None			,    _snake_case						:    PILImageResampling = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[float] = None			,    _snake_case						:    Optional[bool] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[float, List[float]]] = None			,    _snake_case						:    Optional[Union[str, TensorType]] = None			,    _snake_case						:    Union[str, ChannelDimension] = ChannelDimension.FIRST			,    **_snake_case						:    str			,    ):
          __lowercase						:     List[Any]		      =    do_resize if do_resize is not None else self.do_resize
          __lowercase						:     Any		      =    do_rescale if do_rescale is not None else self.do_rescale
          __lowercase						:     Optional[Any]		      =    do_normalize if do_normalize is not None else self.do_normalize
          __lowercase						:     Optional[Any]		      =    resample if resample is not None else self.resample
          __lowercase						:     Tuple		      =    rescale_factor if rescale_factor is not None else self.rescale_factor
          __lowercase						:     Dict		      =    image_mean if image_mean is not None else self.image_mean
          __lowercase						:     Any		      =    image_std if image_std is not None else self.image_std

          __lowercase						:     Optional[int]		      =    size if size is not None else self.size
          __lowercase						:     Tuple		      =    get_size_dict(_snake_case			)

          __lowercase						:     Optional[int]		      =    make_list_of_images(_snake_case			)

          if not valid_images(_snake_case			):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.'''			)

          if do_resize and size is None:
              raise ValueError('''Size must be specified if do_resize is True.'''			)

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.'''			)

          # All transformations expect numpy arrays.
          __lowercase						:     Optional[Any]		      =    [to_numpy_array(_snake_case			) for image in images]

          if do_resize:
              __lowercase						:     List[Any]		      =    [self.resize(image=_snake_case			,    size=_snake_case			,    resample=_snake_case			) for image in images]

          if do_rescale:
              __lowercase						:     Tuple		      =    [self.rescale(image=_snake_case			,    scale=_snake_case			) for image in images]

          if do_normalize:
              __lowercase						:     Union[str, Any]		      =    [self.normalize(image=_snake_case			,    mean=_snake_case			,    std=_snake_case			) for image in images]

          __lowercase						:     Tuple		      =    [to_channel_dimension_format(_snake_case			,    _snake_case			) for image in images]

          __lowercase						:     List[str]		      =    {'''pixel_values''': images}
          return BatchFeature(data=_snake_case			,    tensor_type=_snake_case			)


