






from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase								=						logging.get_logger(__name__)



class   lowerCamelCase      (UpperCAmelCase__			):







		'''simple docstring'''



		_snake_case    :    int       						=  ["""pixel_values"""]



		def __init__(							self				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = PILImageResampling.BICUBIC				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = True				,		_UpperCamelCase = 1 / 2_5_5				,		_UpperCamelCase = True				,		_UpperCamelCase = IMAGENET_DEFAULT_MEAN				,		_UpperCamelCase = IMAGENET_DEFAULT_STD				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**__lowercase   )
							UpperCAmelCase_    :      List[str]            =						size if size is not None else {'''shortest_edge''': 2_2_4}
							UpperCAmelCase_    :      Union[str, Any]            =						get_size_dict(__lowercase				,		default_to_square=__lowercase   )
							UpperCAmelCase_    :      List[str]            =						crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							UpperCAmelCase_    :      List[Any]            =						get_size_dict(__lowercase				,		param_name='crop_size'   )

							UpperCAmelCase_    :      List[str]            =						do_resize
							UpperCAmelCase_    :      List[Any]            =						size
							UpperCAmelCase_    :      Tuple            =						resample
							UpperCAmelCase_    :      Any            =						do_center_crop
							UpperCAmelCase_    :      List[Any]            =						crop_size
							UpperCAmelCase_    :      int            =						do_rescale
							UpperCAmelCase_    :      Optional[int]            =						rescale_factor
							UpperCAmelCase_    :      str            =						do_normalize
							UpperCAmelCase_    :      Dict            =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
							UpperCAmelCase_    :      int            =						image_std if image_std is not None else IMAGENET_DEFAULT_STD



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = PILImageResampling.BICUBIC				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							UpperCAmelCase_    :      Optional[int]            =						get_size_dict(__lowercase				,		default_to_square=__lowercase   )
							# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
							if "shortest_edge" in size:
												UpperCAmelCase_    :      Any            =						int((2_5_6 / 2_2_4) * size['shortest_edge']   )
												UpperCAmelCase_    :      str            =						get_resize_output_image_size(__lowercase				,		size=__lowercase				,		default_to_square=__lowercase   )
												UpperCAmelCase_    :      int            =						{'''height''': output_size[0], '''width''': output_size[1]}
							if "height" not in size_dict or "width" not in size_dict:
												raise ValueError(
												    f"Size dict must have keys 'height' and 'width' or 'shortest_edge'. Got {size_dict.keys()}"   )
							return resize(
							    __lowercase				,		size=(size_dict['height'], size_dict['width'])				,		resample=__lowercase				,		data_format=__lowercase				,		**__lowercase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							UpperCAmelCase_    :      Tuple            =						get_size_dict(__lowercase   )
							if "height" not in size or "width" not in size:
												raise ValueError(f"Size dict must have keys 'height' and 'width'. Got {size.keys()}"   )
							return center_crop(__lowercase				,		size=(size['height'], size['width'])				,		data_format=__lowercase				,		**__lowercase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							return rescale(__lowercase				,		scale=__lowercase				,		data_format=__lowercase				,		**__lowercase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							return normalize(__lowercase				,		mean=__lowercase				,		std=__lowercase				,		data_format=__lowercase				,		**__lowercase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = ChannelDimension.FIRST				,		**_UpperCamelCase				,		)	->							BatchFeature:
							UpperCAmelCase_    :      Any            =						do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_    :      Union[str, Any]            =						resample if resample is not None else self.resample
							UpperCAmelCase_    :      int            =						do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCAmelCase_    :      List[Any]            =						do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_    :      Optional[int]            =						rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_    :      int            =						do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_    :      List[Any]            =						image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_    :      Union[str, Any]            =						image_std if image_std is not None else self.image_std

							UpperCAmelCase_    :      Optional[Any]            =						size if size is not None else self.size
							UpperCAmelCase_    :      Any            =						get_size_dict(__lowercase				,		default_to_square=__lowercase   )
							UpperCAmelCase_    :      List[str]            =						crop_size if crop_size is not None else self.crop_size
							UpperCAmelCase_    :      Dict            =						get_size_dict(__lowercase				,		param_name='crop_size'   )

							UpperCAmelCase_    :      Tuple            =						make_list_of_images(__lowercase   )

							if not valid_images(__lowercase   ):
												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:
												raise ValueError('Size must be specified if do_resize is True.'   )

							if do_center_crop and crop_size is None:
												raise ValueError('Crop size must be specified if do_center_crop is True.'   )

							if do_rescale and rescale_factor is None:
												raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

							if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

							# All transformations expect numpy arrays.
							UpperCAmelCase_    :      Tuple            =						[to_numpy_array(__lowercase   ) for image in images]

							if do_resize:
												UpperCAmelCase_    :      Dict            =						[self.resize(__lowercase				,		__lowercase				,		__lowercase   ) for image in images]

							if do_center_crop:
												UpperCAmelCase_    :      int            =						[self.center_crop(__lowercase				,		__lowercase   ) for image in images]

							if do_rescale:
												UpperCAmelCase_    :      Dict            =						[self.rescale(__lowercase				,		__lowercase   ) for image in images]

							if do_normalize:
												UpperCAmelCase_    :      List[str]            =						[self.normalize(__lowercase				,		__lowercase				,		__lowercase   ) for image in images]

							UpperCAmelCase_    :      int            =						[to_channel_dimension_format(__lowercase				,		__lowercase   ) for image in images]

							UpperCAmelCase_    :      Union[str, Any]            =						{'''pixel_values''': images}
							return BatchFeature(data=__lowercase				,		tensor_type=__lowercase   )










from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase	:     Optional[Any]   =logging.get_logger(__name__)

_UpperCAmelCase	:     str   ={
    """facebook/vit-mae-base""": """https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json""",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}







class  snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__					:     Optional[Any]    =							"""vit_mae"""




						def __init__( self      ,  __lowercase=7_6_8      ,  __lowercase=1_2      ,  __lowercase=1_2      ,  __lowercase=3_0_7_2      ,  __lowercase="gelu"      ,  __lowercase=0.0      ,  __lowercase=0.0      ,  __lowercase=0.02      ,  __lowercase=1e-12      ,  __lowercase=2_2_4      ,  __lowercase=1_6      ,  __lowercase=3      ,  __lowercase=True      ,  __lowercase=1_6      ,  __lowercase=5_1_2      ,  __lowercase=8      ,  __lowercase=2_0_4_8      ,  __lowercase=0.75      ,  __lowercase=False      ,  **__lowercase      ,  )    ->			str:
										super().__init__(**__lowercase							)

										lowerCAmelCase_       :					Dict			 = hidden_size
										lowerCAmelCase_       :					Any			 = num_hidden_layers
										lowerCAmelCase_       :					Any			 = num_attention_heads
										lowerCAmelCase_       :					int			 = intermediate_size
										lowerCAmelCase_       :					Dict			 = hidden_act
										lowerCAmelCase_       :					int			 = hidden_dropout_prob
										lowerCAmelCase_       :					str			 = attention_probs_dropout_prob
										lowerCAmelCase_       :					List[str]			 = initializer_range
										lowerCAmelCase_       :					Dict			 = layer_norm_eps
										lowerCAmelCase_       :					Union[str, Any]			 = image_size
										lowerCAmelCase_       :					Optional[int]			 = patch_size
										lowerCAmelCase_       :					Tuple			 = num_channels
										lowerCAmelCase_       :					List[str]			 = qkv_bias
										lowerCAmelCase_       :					List[Any]			 = decoder_num_attention_heads
										lowerCAmelCase_       :					int			 = decoder_hidden_size
										lowerCAmelCase_       :					Optional[int]			 = decoder_num_hidden_layers
										lowerCAmelCase_       :					Tuple			 = decoder_intermediate_size
										lowerCAmelCase_       :					Tuple			 = mask_ratio
										lowerCAmelCase_       :					Any			 = norm_pix_loss