




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


UpperCAmelCase_									=	logging.get_logger(__name__)




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					List[Any]	):





       '''simple docstring'''



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




def 					lowerCamelCase__       (						A__       :					Tuple  ,			A__       :					Dict	):





       '''simple docstring'''



       __lowerCamelCase   =						x.reshape(-1  ,			3	)
       __lowerCamelCase   =						squared_euclidean_distance(__lowercase  ,			__lowercase	)
       return np.argmin(__lowercase  ,			axis=1	)





class     lowerCamelCase__(					__lowerCamelCase):
      UpperCAmelCase__      :	List[Any]    		=   ["pixel_values"]
      def __init__(				self: Union[str, 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_: List[str]    ,       ):
             super().__init__(**__A   )
             __lowerCamelCase   =						size if size is not None else {"""height""": 2_56, """width""": 2_56}
             __lowerCamelCase   =						get_size_dict(__A   )
             __lowerCamelCase   =						np.array(__A   ) if clusters is not None else None
             __lowerCamelCase   =						do_resize
             __lowerCamelCase   =						size
             __lowerCamelCase   =						resample
             __lowerCamelCase   =						do_normalize
             __lowerCamelCase   =						do_color_quantize
      def 			lowerCAmelCase__						(				self: Optional[int]    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Dict[str, int]    ,       UpperCamelCase_: PILImageResampling = PILImageResampling.BILINEAR    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       **UpperCamelCase_: Union[str, Any]    ,       ):
             __lowerCamelCase   =						get_size_dict(__A   )
             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(
                 __A    ,       size=(size["""height"""], size["""width"""])    ,       resample=__A    ,       data_format=__A    ,       **__A   )
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: np.ndarray    ,       UpperCamelCase_: Optional[Union[str, ChannelDimension]] = None    ,       ):
             __lowerCamelCase   =						rescale(image=__A    ,       scale=1 / 127.5    ,       data_format=__A   )
             __lowerCamelCase   =						image - 1
             return image




      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       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_: Optional[int]    ,       ):
             __lowerCamelCase   =						do_resize if do_resize is not None else self.do_resize
             __lowerCamelCase   =						size if size is not None else self.size
             __lowerCamelCase   =						get_size_dict(__A   )
             __lowerCamelCase   =						resample if resample is not None else self.resample
             __lowerCamelCase   =						do_normalize if do_normalize is not None else self.do_normalize
             __lowerCamelCase   =						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
             __lowerCamelCase   =						clusters if clusters is not None else self.clusters
             __lowerCamelCase   =						np.array(__A   )

             __lowerCamelCase   =						make_list_of_images(__A   )

             if not valid_images(__A   ):
                    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.
             __lowerCamelCase   =						[to_numpy_array(__A   ) for image in images]

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

             if do_normalize:
                    __lowerCamelCase   =						[self.normalize(image=__A   ) for image in images]

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

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

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

             __lowerCamelCase   =						{"""input_ids""": images}
             return BatchFeature(data=__A    ,       tensor_type=__A   )








'''simple docstring'''
import pytest

from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
    'split_dict'				,  [
        SplitDict(),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42				,  dataset_name='my_dataset'     )}     ),
        SplitDict({'train': SplitInfo(name='train'				,  num_bytes=1337				,  num_examples=42     )}     ),
        SplitDict({'train': SplitInfo()}     ),
    ]				,  )
def 				lowercase__   (   __lowercase						:  SplitDict     ) ->      int:



       """simple docstring"""






       __UpperCamelCase       =  split_dict._to_yaml_list()
       assert len(__lowercase     ) == len(__lowercase     )
       __UpperCamelCase       =  SplitDict._from_yaml_list(__lowercase     )
       for split_name, split_info in split_dict.items():
              # dataset_name field is deprecated, and is therefore not part of the YAML dump
              __UpperCamelCase       =  None
              # the split name of split_dict takes over the name of the split info object
              __UpperCamelCase       =  split_name
       assert split_dict == reloaded


@pytest.mark.parametrize(
    'split_info'				,  [SplitInfo(), SplitInfo(dataset_name=__lowercase     ), SplitInfo(dataset_name='my_dataset'     )]     )
def 				lowercase__   (   __lowercase						:  Dict     ) ->      Any:



       """simple docstring"""






       __UpperCamelCase       =  asdict(SplitDict({'train': split_info}     )     )
       assert "dataset_name" in split_dict_asdict["train"]
       assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
