'''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


lowercase_     		= logging.get_logger(__name__)



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






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



def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=x.reshape(-1			,      3      )
						_lowerCAmelCase										=squared_euclidean_distance(__lowerCAmelCase			,      __lowerCAmelCase      )
						return np.argmin(__lowerCAmelCase			,      axis=1      )







class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      int       =							['pixel_values']






		def __init__(   self		,						__A = None		,						__A = True		,						__A = None		,						__A = PILImageResampling.BILINEAR		,						__A = True		,						__A = True		,						**__A		,						) ->     int:
								super().__init__(**_lowerCAmelCase				)
								_lowerCAmelCase										=size if size is not None else {'height': 256, 'width': 256}
								_lowerCAmelCase										=get_size_dict(_lowerCAmelCase				)
								_lowerCAmelCase										=np.array(_lowerCAmelCase				) if clusters is not None else None
								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=size
								_lowerCAmelCase										=resample
								_lowerCAmelCase										=do_normalize
								_lowerCAmelCase										=do_color_quantize






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A = PILImageResampling.BILINEAR		,						__A = None		,						**__A		,						) ->     List[str]:
								_lowerCAmelCase										=get_size_dict(_lowerCAmelCase				)
								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(
								    _lowerCAmelCase		,						size=(size['height'], size['width'])		,						resample=_lowerCAmelCase		,						data_format=_lowerCAmelCase		,						**_lowerCAmelCase				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						) ->     List[str]:
								_lowerCAmelCase										=rescale(image=_lowerCAmelCase		,						scale=1 / 127.5		,						data_format=_lowerCAmelCase				)
								_lowerCAmelCase										=image - 1
								return image






		def 		UpperCamelCase__       (   self		,						__A		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = None		,						__A = ChannelDimension.FIRST		,						**__A		,						) ->     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(_lowerCAmelCase				)
								_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(_lowerCAmelCase				)

								_lowerCAmelCase										=make_list_of_images(_lowerCAmelCase				)

								if not valid_images(_lowerCAmelCase				):
														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(_lowerCAmelCase				) for image in images]

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

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

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

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

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

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






'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase_     		= {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ViTMAEForPreTraining''',
							    '''ViTMAELayer''',
							    '''ViTMAEModel''',
							    '''ViTMAEPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_     		= [
							    '''TFViTMAEForPreTraining''',
							    '''TFViTMAEModel''',
							    '''TFViTMAEPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_vit_mae import (
														    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ViTMAEForPreTraining,
														    ViTMAELayer,
														    ViTMAEModel,
														    ViTMAEPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
							import sys

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





