





"""simple docstring"""
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 center_crop, 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, is_vision_available, logging


if is_vision_available():
 import PIL


lowercase__ 	=     logging.get_logger(__name__)




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		["""pixel_values"""]



      def __init__(						self   ,      lowercase = True   ,      lowercase = None   ,      lowercase = PIL.Image.BICUBIC   ,      lowercase = True   ,      lowercase = None   ,      lowercase = 1 / 2_55   ,      lowercase = True   ,      lowercase = True   ,      lowercase = None   ,      lowercase = None   ,      **lowercase   ,      )     ->      None:





        '''simple docstring'''





        super().__init__(**lowercase)
        a__:					str      							=     size if size is not None else {'height': 2_56, 'width': 2_56}
        a__:					str      							=     get_size_dict(lowercase)
        a__:					List[str]      							=     crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
        a__:					List[str]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					Tuple      							=     do_resize
        a__:					Tuple      							=     size
        a__:					Union[str, Any]      							=     resample
        a__:					int      							=     do_center_crop
        a__:					Tuple      							=     crop_size
        a__:					Optional[int]      							=     do_rescale
        a__:					Optional[Any]      							=     rescale_factor
        a__:					Optional[int]      							=     do_normalize
        a__:					Any      							=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        a__:					Optional[Any]      							=     image_std if image_std is not None else IMAGENET_STANDARD_STD



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = PIL.Image.BICUBIC   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					int      							=     get_size_dict(lowercase)
        if "height" not in size or "width" not in size:
          raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
        return resize(
            lowercase   ,      size=(size['height'], size['width'])   ,      resample=lowercase   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        a__:					Union[str, Any]      							=     get_size_dict(lowercase)
        if "height" not in size or "width" not in size:
          raise ValueError(f'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}')
        return center_crop(lowercase   ,      size=(size['height'], size['width'])   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      Dict:





        '''simple docstring'''





        return rescale(lowercase   ,      scale=lowercase   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      **lowercase   ,      )     ->      np.ndarray:





        '''simple docstring'''





        return normalize(lowercase   ,      mean=lowercase   ,      std=lowercase   ,      data_format=lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase=None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      lowercase = ChannelDimension.FIRST   ,      **lowercase   ,      )     ->      PIL.Image.Image:





        '''simple docstring'''





        a__:					Any      							=     do_resize if do_resize is not None else self.do_resize
        a__:					Union[str, Any]      							=     resample if resample is not None else self.resample
        a__:					int      							=     do_center_crop if do_center_crop is not None else self.do_center_crop
        a__:					Optional[Any]      							=     do_rescale if do_rescale is not None else self.do_rescale
        a__:					Dict      							=     rescale_factor if rescale_factor is not None else self.rescale_factor
        a__:					Optional[Any]      							=     do_normalize if do_normalize is not None else self.do_normalize
        a__:					Union[str, Any]      							=     image_mean if image_mean is not None else self.image_mean
        a__:					Any      							=     image_std if image_std is not None else self.image_std

        a__:					str      							=     size if size is not None else self.size
        a__:					Optional[int]      							=     get_size_dict(lowercase)
        a__:					str      							=     crop_size if crop_size is not None else self.crop_size
        a__:					Optional[int]      							=     get_size_dict(lowercase   ,      param_name='crop_size')

        a__:					List[Any]      							=     make_list_of_images(lowercase)

        if not valid_images(lowercase):
          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 or resample is None:
          raise ValueError('Size and resample must be specified if do_resize is True.')

        if do_center_crop and crop_size is None:
          raise ValueError('Crop size must be specified if do_center_crop is True.')

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

        if do_normalize and (image_mean is None or image_std is None):
          raise ValueError('Image mean and std must be specified if do_normalize is True.')

        # All transformations expect numpy arrays.
        a__:					Optional[int]      							=     [to_numpy_array(lowercase) for image in images]

        if do_resize:
          a__:					str      							=     [self.resize(image=lowercase   ,      size=lowercase   ,      resample=lowercase) for image in images]

        if do_center_crop:
          a__:					List[str]      							=     [self.center_crop(image=lowercase   ,      size=lowercase) for image in images]

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

        if do_normalize:
          a__:					Any      							=     [self.normalize(image=lowercase   ,      mean=lowercase   ,      std=lowercase) for image in images]

        a__:					Dict      							=     [to_channel_dimension_format(lowercase   ,      lowercase) for image in images]

        a__:					Union[str, Any]      							=     {'pixel_values': images}
        return BatchFeature(data=lowercase   ,      tensor_type=lowercase)



"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowercase__ 	=     '2.13.1'

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('3.7'):
 raise ImportWarning(
     'To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'
 )

if version.parse(pyarrow.__version__).major < 8:
 raise ImportWarning(
     'To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'
     'If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'
 )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowercase__ 	=     concatenate_datasets
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadManager
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadConfig
lowercase__ 	=     DownloadMode
lowercase__ 	=     DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


