



'''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     :    int      						=					logging.get_logger(__name__)



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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    int:
							UpperCAmelCase  :	Dict							  =	x.reshape(-1			,       3	)
							UpperCAmelCase  :	Union[str, Any]							  =	squared_euclidean_distance(_lowercase			,       _lowercase	)
							return np.argmin(_lowercase			,       axis=1	)


class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				['pixel_values']





	def __init__(						self	,	A = None	,	A = True	,	A = None	,	A = PILImageResampling.BILINEAR	,	A = True	,	A = True	,	**A	,	)    ->    None:
								super().__init__(**A					)
								UpperCAmelCase  :	Optional[Any]							  =	size if size is not None else {"""height""": 256, """width""": 256}
								UpperCAmelCase  :	int							  =	get_size_dict(A					)
								UpperCAmelCase  :	Union[str, Any]							  =	np.array(A					) if clusters is not None else None
								UpperCAmelCase  :	Optional[Any]							  =	do_resize
								UpperCAmelCase  :	str							  =	size
								UpperCAmelCase  :	Dict							  =	resample
								UpperCAmelCase  :	Union[str, Any]							  =	do_normalize
								UpperCAmelCase  :	Any							  =	do_color_quantize





	def 				_lowercase(						self	,	A	,	A	,	A = PILImageResampling.BILINEAR	,	A = None	,	**A	,	)    ->    np.ndarray:
								UpperCAmelCase  :	int							  =	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 				_lowercase(						self	,	A	,	A = None	,	)    ->    np.ndarray:
								UpperCAmelCase  :	Union[str, Any]							  =	rescale(image=A	,	scale=1 / 1_2_7.5	,	data_format=A					)
								UpperCAmelCase  :	Optional[Any]							  =	image - 1
								return image






	def 				_lowercase(						self	,	A	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = None	,	A = ChannelDimension.FIRST	,	**A	,	)    ->    PIL.Image.Image:
								UpperCAmelCase  :	int							  =	do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase  :	Dict							  =	size if size is not None else self.size
								UpperCAmelCase  :	Optional[int]							  =	get_size_dict(A					)
								UpperCAmelCase  :	Dict							  =	resample if resample is not None else self.resample
								UpperCAmelCase  :	Optional[Any]							  =	do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase  :	List[Any]							  =	do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								UpperCAmelCase  :	str							  =	clusters if clusters is not None else self.clusters
								UpperCAmelCase  :	str							  =	np.array(A					)

								UpperCAmelCase  :	Dict							  =	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.
								UpperCAmelCase  :	Optional[int]							  =	[to_numpy_array(A					) for image in images]

								if do_resize:
															UpperCAmelCase  :	Tuple							  =	[self.resize(image=A	,	size=A	,	resample=A					) for image in images]

								if do_normalize:
															UpperCAmelCase  :	Any							  =	[self.normalize(image=A					) for image in images]

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

															# flatten to (batch_size, height*width)
															UpperCAmelCase  :	List[str]							  =	images.shape[0]
															UpperCAmelCase  :	Optional[int]							  =	images.reshape(A	,	-1					)

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

								UpperCAmelCase  :	Union[str, Any]							  =	{"""input_ids""": images}
								return BatchFeature(data=A	,	tensor_type=A					)






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

