


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       _a			(				SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					):


		"""simple docstring"""




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


		"""simple docstring"""




		lowercase__						=				x.reshape(-1						,    3					)
		lowercase__						=				squared_euclidean_distance(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE					)
		return np.argmin(SCREAMING_SNAKE_CASE						,    axis=1					)


class 	_a							(      UpperCamelCase__      ):
							_lowercase     :      str  				=			['''pixel_values''']

							def __init__(							self:     int   ,						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_:     Dict   ,						)      ->       None:





									"""simple docstring"""







									super().__init__(**UpperCamelCase_							)
									lowercase__						=				size if size is not None else {'''height''': 256, '''width''': 256}
									lowercase__						=				get_size_dict(UpperCamelCase_							)
									lowercase__						=				np.array(UpperCamelCase_							) if clusters is not None else None
									lowercase__						=				do_resize
									lowercase__						=				size
									lowercase__						=				resample
									lowercase__						=				do_normalize
									lowercase__						=				do_color_quantize

							def 				lowerCamelCase_			(							self:     Optional[Any]   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Dict[str, int]   ,						UpperCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						**UpperCamelCase_:     Any   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				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:     Any   ,						UpperCamelCase_:     np.ndarray   ,						UpperCamelCase_:     Optional[Union[str, ChannelDimension]] = None   ,						)      ->       np.ndarray:





									"""simple docstring"""







									lowercase__						=				rescale(image=UpperCamelCase_   ,						scale=1 / 127.5   ,						data_format=UpperCamelCase_							)
									lowercase__						=				image - 1
									return image


							def 				lowerCamelCase_			(							self:     List[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_:     Tuple   ,						)      ->       PIL.Image.Image:





									"""simple docstring"""







									lowercase__						=				do_resize if do_resize is not None else self.do_resize
									lowercase__						=				size if size is not None else self.size
									lowercase__						=				get_size_dict(UpperCamelCase_							)
									lowercase__						=				resample if resample is not None else self.resample
									lowercase__						=				do_normalize if do_normalize is not None else self.do_normalize
									lowercase__						=				do_color_quantize if do_color_quantize is not None else self.do_color_quantize
									lowercase__						=				clusters if clusters is not None else self.clusters
									lowercase__						=				np.array(UpperCamelCase_							)

									lowercase__						=				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.
									lowercase__						=				[to_numpy_array(UpperCamelCase_							) for image in images]

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

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

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

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

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

									lowercase__						=				{'''input_ids''': images}
									return BatchFeature(data=UpperCamelCase_   ,						tensor_type=UpperCamelCase_							)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


