





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


a__:      List[str]									=		namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 	UpperCamelCase__(       UpperCamelCase__			:       List[str] ,     UpperCamelCase__			:       List[Any]  )->Optional[int]:
       return (abs(source - target  ) / target) < 0.01


@pytest.mark.integration
def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->List[str]:
       A__											=						_TestCommandArgs(dataset=UpperCamelCase__ ,     all_configs=UpperCamelCase__ ,     save_infos=UpperCamelCase__  )
       A__											=						TestCommand(*UpperCamelCase__  )
       test_command.run()
       A__											=						os.path.join(UpperCamelCase__ ,     '''README.md'''  )
       assert os.path.exists(UpperCamelCase__  )
       A__											=						DatasetInfosDict.from_directory(UpperCamelCase__  )
       A__											=						DatasetInfosDict(
           {
               '''default''': DatasetInfo(
                   features=Features(
                       {
                           '''tokens''': Sequence(Value('''string'''  )  ),
                           '''ner_tags''': Sequence(
                               ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']  )  ),
                           '''langs''': Sequence(Value('''string'''  )  ),
                           '''spans''': Sequence(Value('''string'''  )  ),
                       }  ) ,     splits=[
                       {
                           '''name''': '''train''',
                           '''num_bytes''': 2_35_15_63,
                           '''num_examples''': 1_00_00,
                       },
                       {
                           '''name''': '''validation''',
                           '''num_bytes''': 23_84_18,
                           '''num_examples''': 10_00,
                       },
                   ] ,     download_size=3_94_06_80 ,     dataset_size=2_58_99_81 ,     )
           }  )
       assert dataset_infos.keys() == expected_dataset_infos.keys()
       for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
              A__											=						getattr(dataset_infos['''default'''] ,     UpperCamelCase__  ), getattr(expected_dataset_infos['''default'''] ,     UpperCamelCase__  )
              if key == "num_bytes":
                     assert is_apercent_close(UpperCamelCase__ ,     UpperCamelCase__  )
              elif key == "splits":
                     assert list(UpperCamelCase__  ) == list(UpperCamelCase__  )
                     for split in result:
                            assert result[split].name == expected[split].name
                            assert result[split].num_examples == expected[split].num_examples
                            assert is_apercent_close(result[split].num_bytes ,     expected[split].num_bytes  )
              else:
                     result == expected






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

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

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





