




"""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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    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


a							      =     logging.get_logger(__name__)






def 		_snake_case	(   _snake_case				:   Optional[int]							,    _snake_case				:   str				)       ->   List[Any]:






      '''simple docstring'''


      _A     =  b.T
      _A     =  np.sum(np.square(_snake_case				)							,    axis=1				)
      _A     =  np.sum(np.square(_snake_case				)							,    axis=0				)
      _A     =  np.matmul(_snake_case							,    _snake_case				)
      _A     =  aa[:, None] - 2 * ab + ba[None, :]
      return d






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






      '''simple docstring'''


      _A     =  x.reshape(-1							,    3				)
      _A     =  squared_euclidean_distance(_snake_case							,    _snake_case				)
      return np.argmin(_snake_case							,    axis=1				)







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      ['''pixel_values''']

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			bool = True       ,				_UpperCAmelCase			:			bool = True       ,				**_UpperCAmelCase			:			int       ,				):
       super().__init__(**_UpperCAmelCase						)
       _A     =  size if size is not None else {'height': 256, 'width': 256}
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  np.array(_UpperCAmelCase						) if clusters is not None else None
       _A     =  do_resize
       _A     =  size
       _A     =  resample
       _A     =  do_normalize
       _A     =  do_color_quantize

 def      lowerCAmelCase_				(					self			:			Dict       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Dict[str, int]       ,				_UpperCAmelCase			:			PILImageResampling = PILImageResampling.BILINEAR       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  get_size_dict(_UpperCAmelCase						)
       if "height" not in size or "width" not in size:
             raise ValueError(F'''Size dictionary must contain both height and width keys. Got {size.keys()}'''						)
       return resize(
           _UpperCAmelCase       ,				size=(size['height'], size['width'])       ,				resample=_UpperCAmelCase       ,				data_format=_UpperCAmelCase       ,				**_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			np.ndarray       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = None       ,				):
       _A     =  rescale(image=_UpperCAmelCase       ,				scale=1 / 127.5       ,				data_format=_UpperCAmelCase						)
       _A     =  image - 1
       return image



 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			ImageInput       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Dict[str, int] = None       ,				_UpperCAmelCase			:			PILImageResampling = None       ,				_UpperCAmelCase			:			bool = None       ,				_UpperCAmelCase			:			Optional[bool] = None       ,				_UpperCAmelCase			:			Optional[Union[List[List[int]], np.ndarray]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, TensorType]] = None       ,				_UpperCAmelCase			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST       ,				**_UpperCAmelCase			:			List[Any]       ,				):
       _A     =  do_resize if do_resize is not None else self.do_resize
       _A     =  size if size is not None else self.size
       _A     =  get_size_dict(_UpperCAmelCase						)
       _A     =  resample if resample is not None else self.resample
       _A     =  do_normalize if do_normalize is not None else self.do_normalize
       _A     =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
       _A     =  clusters if clusters is not None else self.clusters
       _A     =  np.array(_UpperCAmelCase						)

       _A     =  make_list_of_images(_UpperCAmelCase						)

       if not valid_images(_UpperCAmelCase						):
             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_color_quantize and clusters is None:
             raise ValueError('Clusters must be specified if do_color_quantize is True.'						)

       # All transformations expect numpy arrays.
       _A     =  [to_numpy_array(_UpperCAmelCase						) for image in images]

       if do_resize:
             _A     =  [self.resize(image=_UpperCAmelCase       ,				size=_UpperCAmelCase       ,				resample=_UpperCAmelCase						) for image in images]

       if do_normalize:
             _A     =  [self.normalize(image=_UpperCAmelCase						) for image in images]

       if do_color_quantize:
             _A     =  [to_channel_dimension_format(_UpperCAmelCase       ,				ChannelDimension.LAST						) for image in images]
             # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
             _A     =  np.array(_UpperCAmelCase						)
             _A     =  color_quantize(_UpperCAmelCase       ,				_UpperCAmelCase						).reshape(images.shape[:-1]						)

             # flatten to (batch_size, height*width)
             _A     =  images.shape[0]
             _A     =  images.reshape(_UpperCAmelCase       ,				-1						)

             # We need to convert back to a list of images to keep consistent behaviour across processors.
             _A     =  list(_UpperCAmelCase						)
       else:
             _A     =  [to_channel_dimension_format(_UpperCAmelCase       ,				_UpperCAmelCase						) for image in images]

       _A     =  {'input_ids': images}
       return BatchFeature(data=_UpperCAmelCase       ,				tensor_type=_UpperCAmelCase						)





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

a			:			Dict												=					'''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

a			:			Optional[int]												=					concatenate_datasets
a			:			List[Any]												=					DownloadConfig
a			:			List[Any]												=					DownloadManager
a			:			str												=					DownloadMode
a			:			int												=					DownloadConfig
a			:			List[str]												=					DownloadMode
a			:			Optional[int]												=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


