


"""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


_lowerCamelCase     =  logging.get_logger(__name__)


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				b.T
				SCREAMING_SNAKE_CASE__     							=				np.sum(np.square(lowerCamelCase_  )						,					axis=1  )
				SCREAMING_SNAKE_CASE__     							=				np.sum(np.square(lowerCamelCase_  )						,					axis=0  )
				SCREAMING_SNAKE_CASE__     							=				np.matmul(lowerCamelCase_						,					lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				aa[:, None] - 2 * ab + ba[None, :]
				return d


def __lowercase     (      lowerCamelCase_  :      Optional[int]						,					lowerCamelCase_  :      Optional[Any]  ):
				SCREAMING_SNAKE_CASE__     							=				x.reshape(-1						,					3  )
				SCREAMING_SNAKE_CASE__     							=				squared_euclidean_distance(lowerCamelCase_						,					lowerCamelCase_  )
				return np.argmin(lowerCamelCase_						,					axis=1  )


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[str]      							=					["pixel_values"]



				def __init__(						self       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = PILImageResampling.BILINEAR       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		**UpperCAmelCase__       ,		):
								super().__init__(**UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				size if size is not None else {"height": 256, "width": 256}
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				np.array(UpperCAmelCase__     ) if clusters is not None else None
								SCREAMING_SNAKE_CASE__     							=				do_resize
								SCREAMING_SNAKE_CASE__     							=				size
								SCREAMING_SNAKE_CASE__     							=				resample
								SCREAMING_SNAKE_CASE__     							=				do_normalize
								SCREAMING_SNAKE_CASE__     							=				do_color_quantize



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = PILImageResampling.BILINEAR       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				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       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		):
								SCREAMING_SNAKE_CASE__     							=				rescale(image=UpperCAmelCase__       ,		scale=1 / 127.5       ,		data_format=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				image - 1
								return image






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = ChannelDimension.FIRST       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE__     							=				size if size is not None else self.size
								SCREAMING_SNAKE_CASE__     							=				get_size_dict(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE__     							=				do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE__     							=				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								SCREAMING_SNAKE_CASE__     							=				clusters if clusters is not None else self.clusters
								SCREAMING_SNAKE_CASE__     							=				np.array(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				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.
								SCREAMING_SNAKE_CASE__     							=				[to_numpy_array(UpperCAmelCase__     ) for image in images]

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

								if do_normalize:
												SCREAMING_SNAKE_CASE__     							=				[self.normalize(image=UpperCAmelCase__     ) for image in images]

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

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

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

								SCREAMING_SNAKE_CASE__     							=				{"input_ids": images}
								return BatchFeature(data=UpperCAmelCase__       ,		tensor_type=UpperCAmelCase__     )




"""simple docstring"""






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					CustomTokenizer
				pass
