

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


snake_case_        =			logging.get_logger(__name__)

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       str				=	b.T
     lowercase__   :       Optional[int]				=	np.sum(np.square(__lowerCAmelCase      )     ,					axis=1      )
     lowercase__   :       Tuple				=	np.sum(np.square(__lowerCAmelCase      )     ,					axis=0      )
     lowercase__   :       List[Any]				=	np.matmul(__lowerCAmelCase     ,					__lowerCAmelCase      )
     lowercase__   :       int				=	aa[:, None] - 2 * ab + ba[None, :]
     return d

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   str      ):



     '''simple docstring'''





     lowercase__   :       Any				=	x.reshape(-1     ,					3      )
     lowercase__   :       Tuple				=	squared_euclidean_distance(__lowerCAmelCase     ,					__lowerCAmelCase      )
     return np.argmin(__lowerCAmelCase     ,					axis=1      )


class 				SCREAMING_SNAKE_CASE__ (lowerCamelCase_					):
    __lowerCamelCase						:	Optional[Any]														=					['''pixel_values''']





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





    def 				snake_case_    (				self						,       a						,       a						,       a = PILImageResampling.BILINEAR						,       a = None						,       **a						,       ):
         lowercase__   :       List[str]				=	get_size_dict(__snake_case)
         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(
             __snake_case						,       size=(size['height'], size['width'])						,       resample=__snake_case						,       data_format=__snake_case						,       **__snake_case)





    def 				snake_case_    (				self						,       a						,       a = None						,       ):
         lowercase__   :       Any				=	rescale(image=__snake_case						,       scale=1 / 127.5						,       data_format=__snake_case)
         lowercase__   :       Dict				=	image - 1
         return image





    def 				snake_case_    (				self						,       a						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = None						,       a = ChannelDimension.FIRST						,       **a						,       ):
         lowercase__   :       List[str]				=	do_resize if do_resize is not None else self.do_resize
         lowercase__   :       Dict				=	size if size is not None else self.size
         lowercase__   :       List[str]				=	get_size_dict(__snake_case)
         lowercase__   :       Any				=	resample if resample is not None else self.resample
         lowercase__   :       Tuple				=	do_normalize if do_normalize is not None else self.do_normalize
         lowercase__   :       Optional[int]				=	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
         lowercase__   :       Union[str, Any]				=	clusters if clusters is not None else self.clusters
         lowercase__   :       Any				=	np.array(__snake_case)

         lowercase__   :       List[str]				=	make_list_of_images(__snake_case)

         if not valid_images(__snake_case):
              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.
         lowercase__   :       List[Any]				=	[to_numpy_array(__snake_case) for image in images]

         if do_resize:
              lowercase__   :       Union[str, Any]				=	[self.resize(image=__snake_case						,       size=__snake_case						,       resample=__snake_case) for image in images]

         if do_normalize:
              lowercase__   :       Dict				=	[self.normalize(image=__snake_case) for image in images]

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

              # flatten to (batch_size, height*width)
              lowercase__   :       List[Any]				=	images.shape[0]
              lowercase__   :       str				=	images.reshape(__snake_case						,       -1)

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

         lowercase__   :       List[Any]				=	{'input_ids': images}
         return BatchFeature(data=__snake_case						,       tensor_type=__snake_case)




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

