




import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCAmelCase__						:       Optional[int]							=    1000

class      UpperCAmelCase						:






   def __init__(	self   :       List[Any]						,					UpperCamelCase_   :       Dict						,					UpperCamelCase_   :       int=1.2						,					UpperCamelCase_   :       Tuple="g"						,					UpperCamelCase_   :       List[str]=0.5						,					UpperCamelCase_   :       Optional[Any]="-"						,					UpperCamelCase_   :       Optional[Any]="test_out.jpg"						,					UpperCamelCase_   :       int=True						,					UpperCamelCase_   :       Tuple=False						,					UpperCamelCase_   :       Union[str, Any]=None						,					UpperCamelCase_   :       int=None						,					UpperCamelCase_   :       int=0.7						,					)->			List[Any]:
     if isinstance(UpperCamelCase_						,					torch.Tensor     ):
       _lowercase: Any						     =    img.numpy().astype('np.uint8'     )
     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       _lowercase: Any						     =    img_tensorize(UpperCamelCase_     )
     assert isinstance(UpperCamelCase_						,					np.ndarray     )

     _lowercase     ,					_lowercase: List[Any]						     =    img.shape[1], img.shape[0]
     _lowercase: Any						     =    mplfigure.Figure(frameon=UpperCamelCase_     )
     _lowercase: Optional[int]						     =    fig.get_dpi()
     _lowercase: List[Any]						     =    (width * scale + 1E-2) / dpi
     _lowercase: int						     =    (height * scale + 1E-2) / dpi
     fig.set_size_inches(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: List[Any]						     =    fig.add_axes([0.0, 0.0, 1.0, 1.0]     )
     ax.axis('off'     )
     ax.set_xlim(0.0						,					UpperCamelCase_     )
     ax.set_ylim(UpperCamelCase_     )

     _lowercase: str						     =    saveas
     _lowercase: Dict						     =    rgb
     _lowercase: Union[str, Any]						     =    pynb
     _lowercase: List[Any]						     =    img
     _lowercase: Dict						     =    edgecolor
     _lowercase: Union[str, Any]						     =    0.5
     _lowercase: List[Any]						     =    linestyle
     _lowercase: List[Any]						     =    int(np.sqrt(min(UpperCamelCase_						,					UpperCamelCase_     )     ) * scale // 3     )
     _lowercase: Dict						     =    width
     _lowercase: List[Any]						     =    height
     _lowercase: List[Any]						     =    scale
     _lowercase: Optional[int]						     =    fig
     _lowercase: Union[str, Any]						     =    ax
     _lowercase: Dict						     =    pad
     _lowercase: Optional[int]						     =    idaobj
     _lowercase: Tuple						     =    idaattr
     _lowercase: List[str]						     =    FigureCanvasAgg(UpperCamelCase_     )






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       List[str]						,					UpperCamelCase_   :       List[str]=None     )->			List[str]:
     if color is None:
       _lowercase: Tuple						     =    self.edgecolor
     ((_lowercase)     ,					(_lowercase)     ,					(_lowercase)     ,					(_lowercase)): int						     =    box
     _lowercase: Optional[int]						     =    xa - xa
     _lowercase: Tuple						     =    ya - ya
     self.ax.add_patch(
         mpl.patches.Rectangle(
             (xa, ya)						,					UpperCamelCase_						,					UpperCamelCase_						,					fill=UpperCamelCase_						,					edgecolor=UpperCamelCase_						,					linewidth=self.font_size // 3						,					alpha=self.alpha						,					linestyle=self.linestyle						,					)     )






   def  _a						(	self   :       str						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=None						,					UpperCamelCase_   :       Dict=None						,					UpperCamelCase_   :       int=None     )->			Dict:
     if len(boxes.shape     ) > 2:
       _lowercase: int						     =    boxes[0]
     if len(obj_ids.shape     ) > 1:
       _lowercase: Optional[Any]						     =    obj_ids[0]
     if len(obj_scores.shape     ) > 1:
       _lowercase: Any						     =    obj_scores[0]
     if len(attr_ids.shape     ) > 1:
       _lowercase: Any						     =    attr_ids[0]
     if len(attr_scores.shape     ) > 1:
       _lowercase: Optional[int]						     =    attr_scores[0]
     if isinstance(UpperCamelCase_						,					torch.Tensor     ):
       _lowercase: Tuple						     =    boxes.numpy()
     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       _lowercase: Union[str, Any]						     =    np.array(UpperCamelCase_     )
     assert isinstance(UpperCamelCase_						,					np.ndarray     )
     _lowercase: Optional[int]						     =    np.prod(boxes[:, 2:] - boxes[:, :2]						,					axis=1     )
     _lowercase: Optional[int]						     =    np.argsort(-areas     ).tolist()
     _lowercase: List[str]						     =    boxes[sorted_idxs] if boxes is not None else None
     _lowercase: List[str]						     =    obj_ids[sorted_idxs] if obj_ids is not None else None
     _lowercase: Union[str, Any]						     =    obj_scores[sorted_idxs] if obj_scores is not None else None
     _lowercase: Tuple						     =    attr_ids[sorted_idxs] if attr_ids is not None else None
     _lowercase: Optional[Any]						     =    attr_scores[sorted_idxs] if attr_scores is not None else None

     _lowercase: Union[str, Any]						     =    [self._random_color(maximum=1     ) for _ in range(len(UpperCamelCase_     )     )]
     _lowercase: Union[str, Any]						     =    [assigned_colors[idx] for idx in sorted_idxs]
     if obj_ids is not None:
       _lowercase: List[str]						     =    self._create_text_labels_attr(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
       for i in range(len(UpperCamelCase_     )     ):
         _lowercase: Tuple						     =    assigned_colors[i]
         self.add_box(boxes[i]						,					UpperCamelCase_     )
         self.draw_labels(labels[i]						,					boxes[i]						,					UpperCamelCase_     )






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       List[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       str     )->			List[Any]:
     _lowercase     ,					_lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    box
     _lowercase: Dict						     =    (xa, ya)
     _lowercase: List[Any]						     =    (ya - ya) * (xa - xa)
     _lowercase: List[Any]						     =    _SMALL_OBJ * self.scale
     if instance_area < small or ya - ya < 40 * self.scale:
       if ya >= self.height - 5:
         _lowercase: Optional[int]						     =    (xa, ya)
       else:
         _lowercase: List[Any]						     =    (xa, ya)

     _lowercase: Dict						     =    (ya - ya) / np.sqrt(self.height * self.width     )
     _lowercase: Optional[Any]						     =    self._change_color_brightness(UpperCamelCase_						,					brightness_factor=0.7     )
     _lowercase: int						     =    np.clip((height_ratio - 0.02) / 0.08 + 1						,					1.2						,					2     )
     font_size *= 0.75 * self.font_size

     self.draw_text(
         text=UpperCamelCase_						,					position=UpperCamelCase_						,					color=UpperCamelCase_						,					)






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       Dict="g"						,					UpperCamelCase_   :       int="left"						,					)->			Tuple:
     _lowercase: str						     =    0
     _lowercase: Union[str, Any]						     =    self.font_size
     _lowercase: str						     =    np.maximum(list(mplc.to_rgb(UpperCamelCase_     )     )						,					0.2     )
     _lowercase: Any						     =    max(0.8						,					np.max(UpperCamelCase_     )     )
     _lowercase: List[Any]						     =    {
         'facecolor': 'black',
         'alpha': self.alpha,
         'pad': self.pad,
         'edgecolor': 'none',
     }
     _lowercase     ,					_lowercase: str						     =    position
     self.ax.text(
         UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					size=font_size * self.scale						,					family='sans-serif'						,					bbox=UpperCamelCase_						,					verticalalignment='top'						,					horizontalalignment=UpperCamelCase_						,					color=UpperCamelCase_						,					zorder=10						,					rotation=UpperCamelCase_						,					)






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       Dict=None     )->			str:
     if saveas is None:
       _lowercase: Optional[Any]						     =    self.saveas
     if saveas.lower().endswith('.jpg'     ) or saveas.lower().endswith('.png'     ):
       cva.imwrite(
           UpperCamelCase_						,					self._get_buffer()[:, :, ::-1]						,					)
     else:
       self.fig.savefig(UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       Tuple						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Optional[int]     )->			int:
     _lowercase: List[str]						     =    [self.idaobj[i] for i in classes]
     _lowercase: Optional[Any]						     =    [self.idaattr[i] for i in attr_classes]
     _lowercase: List[Any]						     =    [
         f'{label} {score:.2f} {attr} {attr_score:.2f}'
         for label, score, attr, attr_score in zip(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     )
     ]
     return labels






   def  _a						(	self   :       int						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[int]     )->			List[str]:
     _lowercase: Optional[Any]						     =    [self.idaobj[i] for i in classes]
     if scores is not None:
       if labels is None:
         _lowercase: str						     =    ['{:.0f}%'.format(s * 1_00     ) for s in scores]
       else:
         _lowercase: str						     =    ['{} {:.0f}%'.format(UpperCamelCase_						,					s * 1_00     ) for li, s in zip(UpperCamelCase_						,					UpperCamelCase_     )]
     return labels






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       int=2_55     )->			List[Any]:
     _lowercase: List[Any]						     =    np.random.randint(0						,					len(_COLORS     )     )
     _lowercase: Tuple						     =    _COLORS[idx] * maximum
     if not self.rgb:
       _lowercase: str						     =    ret[::-1]
     return ret






   def  _a						(	self   :       Tuple     )->			Union[str, Any]:
     if not self.pynb:
       _lowercase     ,					_lowercase: Tuple						     =    self.canvas.print_to_buffer()
       if (width, height) != (self.width, self.height):
         _lowercase: Optional[Any]						     =    cva.resize(self.img						,					(width, height)     )
       else:
         _lowercase: Optional[Any]						     =    self.img
     else:
       _lowercase: str						     =    io.BytesIO()  # works for cairo backend
       self.canvas.print_rgba(UpperCamelCase_     )
       _lowercase     ,					_lowercase: Union[str, Any]						     =    self.width, self.height
       _lowercase: Optional[Any]						     =    buf.getvalue()
       _lowercase: str						     =    self.img

     _lowercase: Optional[Any]						     =    np.frombuffer(UpperCamelCase_						,					dtype='uint8'     )
     _lowercase: Any						     =    buffer.reshape(UpperCamelCase_						,					UpperCamelCase_						,					4     )
     _lowercase     ,					_lowercase: Optional[int]						     =    np.split(UpperCamelCase_						,					[3]						,					axis=2     )

     try:
       import numexpr as ne  # fuse them with numexpr

       _lowercase: Tuple						     =    ne.evaluate('img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'     )
     except ImportError:
       _lowercase: List[Any]						     =    alpha.astype('float32'     ) / 255.0
       _lowercase: List[str]						     =    img * (1 - alpha) + rgb * alpha

     return visualized_image.astype('uint8'     )






   def  _a						(	self   :       int						,					UpperCamelCase_   :       Optional[Any]						,					UpperCamelCase_   :       str     )->			Union[str, Any]:
     assert brightness_factor >= -1.0 and brightness_factor <= 1.0
     _lowercase: Optional[int]						     =    mplc.to_rgb(UpperCamelCase_     )
     _lowercase: int						     =    colorsys.rgb_to_hls(*mplc.to_rgb(UpperCamelCase_     )     )
     _lowercase: Union[str, Any]						     =    polygon_color[1] + (brightness_factor * polygon_color[1])
     _lowercase: List[Any]						     =    0.0 if modified_lightness < 0.0 else modified_lightness
     _lowercase: str						     =    1.0 if modified_lightness > 1.0 else modified_lightness
     _lowercase: Dict						     =    colorsys.hls_to_rgb(polygon_color[0]						,					UpperCamelCase_						,					polygon_color[2]     )
     return modified_color
UpperCAmelCase__						:       Optional[Any]							=    (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






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						(			A   ):






   def  _a						(	self   :       List[str]						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       Optional[Any]=False     )->			List[str]:
     _lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    (
         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()
     _lowercase: int						     =    pa.Table.from_pydict({'filename': [Path(UpperCamelCase_     ).name] * 1_00}     )
     if take == -1:
       _lowercase: int						     =    len(UpperCamelCase_     ) - skip
     if skip is not None and take is not None:
       _lowercase: Any						     =    pa_table.slice(UpperCamelCase_						,					UpperCamelCase_     )
     return pa_table

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Any     )->			Dict:
     _lowercase: str						     =    'my_name'
     _lowercase: Optional[int]						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: Dict						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: Any						     =    [train_info, test_info]
     _lowercase: Any						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Tuple						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: List[str]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    'test[:33%]'
       _lowercase: Optional[Any]						     =    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     )

       _lowercase: Tuple						     =    ['train', 'test[:33%]']
       _lowercase: Union[str, Any]						     =    [Split.TRAIN, ReadInstruction.from_spec('test[:33%]'     )]
       for instructions in [instructionsa, instructionsa]:
         _lowercase: int						     =    [reader.read(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_     ) for instr in instructions]
         _lowercase     ,					_lowercase: Dict						     =    (Dataset(**UpperCamelCase_     ) for dataset_kwargs in datasets_kwargs)
         self.assertEqual(train_dset['filename'][0]						,					f'{name}-train'     )
         self.assertEqual(train_dset.num_rows						,					1_00     )
         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  _a						(	self   :       Any     )->			List[str]:
     _lowercase: int						     =    'my_name'
     _lowercase: List[str]						     =    SplitInfo(name='train'						,					num_examples=10_00						,					shard_lengths=[1_00] * 10     )
     _lowercase: Optional[Any]						     =    [train_info]
     _lowercase: Optional[int]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     _lowercase: Optional[Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Optional[int]						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    'train[:33%]'
       _lowercase: List[Any]						     =    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						,					3_30     )
       self.assertEqual(dset.num_columns						,					1     )






   def  _a						(	self   :       List[Any]     )->			Dict:
     _lowercase: Any						     =    SplitInfo(name='train'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitInfo(name='test'						,					num_examples=1_00     )
     _lowercase: List[Any]						     =    SplitDict()
     split_dict.add(UpperCamelCase_     )
     split_dict.add(UpperCamelCase_     )
     _lowercase: Union[str, Any]						     =    DatasetInfo(splits=UpperCamelCase_     )

     with tempfile.TemporaryDirectory() as tmp_dir:
       _lowercase: Dict						     =    ReaderTest(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    [
           {'filename': os.path.join(UpperCamelCase_						,					'train'     )},
           {'filename': os.path.join(UpperCamelCase_						,					'test'     ), 'skip': 10, 'take': 10},
       ]
       _lowercase: Tuple						     =    Dataset(**reader.read_files(UpperCamelCase_						,					original_instructions='train+test[10:20]'     )     )
       self.assertEqual(dset.num_rows						,					1_10     )
       self.assertEqual(dset.num_columns						,					1     )
       del dset


@pytest.mark.parametrize('in_memory'					,     [False, True]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    arrow_file
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: List[Any]						     =    ArrowReader.read_table(UpperCamelCase					,     in_memory=UpperCamelCase							)
  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 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: Any						     =    arrow_file
  _lowercase: Union[str, Any]						     =    ArrowReader(''					,     UpperCamelCase							)
  with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
    _lowercase: Tuple						     =    reader.read_files([{'filename': filename}]					,     in_memory=UpperCamelCase							)
  assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
  _lowercase: Any						     =    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 		_lowerCamelCase				(					)			->		int:





  """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: List[str]						     =    'train+test'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_test

  _lowercase: Union[str, Any]						     =    'train[2:10]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_abs

  _lowercase: Any						     =    'train[15%:-20%]'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct

  _lowercase: List[Any]						     =    'train[:10%](closest)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == "train[:10%]"

  _lowercase: int						     =    'train[:10%](pct1_dropremainder)'
  assert ReadInstruction.from_spec(UpperCamelCase							).to_spec() == spec_train_pct_rounding

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


def 		_lowerCamelCase				(					)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: int						     =    'dummy'
  _lowercase: Any						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: Any						     =    'train[:33%]'
  _lowercase: Optional[int]						     =    'arrow'
  _lowercase: Optional[int]						     =    'prefix'

  _lowercase: Optional[int]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train.arrow'							), "skip": 0, "take": 33}
  ]

  _lowercase: int						     =    [SplitInfo(name='train'					,     num_examples=100					,     shard_lengths=[10] * 10							)]
  _lowercase: Dict						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  assert isinstance(UpperCamelCase					,     UpperCamelCase							)
  assert file_instructions.num_examples == 33
  assert file_instructions.file_instructions == [
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00000-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00001-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00002-of-00010.arrow'							), "skip": 0, "take": -1},
      {"filename": os.path.join(UpperCamelCase					,     F'{name}-train-00003-of-00010.arrow'							), "skip": 0, "take": 3},
  ]





@pytest.mark.parametrize('name, expected_exception'					,     [(None, TypeError), ('', ValueError)]							)
def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Tuple:





  """simple docstring"""





  _lowercase: List[str]						     =    [SplitInfo(name='train'					,     num_examples=100							)]
  _lowercase: List[Any]						     =    'train'
  _lowercase: List[str]						     =    'arrow'
  _lowercase: Optional[Any]						     =    'prefix_path'
  with pytest.raises(UpperCamelCase							):
    _lowercase: List[str]						     =    make_file_instructions(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
