



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 center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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


__snake_case    :		List[Any]     =logging.get_logger(__name__)



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""pixel_values"""]



		def __init__(self		,__lowerCamelCase = True		,__lowerCamelCase = None		,__lowerCamelCase = PIL.Image.BICUBIC		,__lowerCamelCase = True		,__lowerCamelCase = None		,__lowerCamelCase = 1 / 2_55		,__lowerCamelCase = True		,__lowerCamelCase = True		,__lowerCamelCase = None		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      None:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					size if size is not None else {'''height''': 2_56, '''width''': 2_56}
						lowerCAmelCase__					:    Dict												=					get_size_dict(__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
						lowerCAmelCase__					:    Any												=					get_size_dict(__lowerCamelCase		,param_name='''crop_size'''			)

						lowerCAmelCase__					:    str												=					do_resize
						lowerCAmelCase__					:    Any												=					size
						lowerCAmelCase__					:    Optional[Any]												=					resample
						lowerCAmelCase__					:    Dict												=					do_center_crop
						lowerCAmelCase__					:    int												=					crop_size
						lowerCAmelCase__					:    List[Any]												=					do_rescale
						lowerCAmelCase__					:    Optional[Any]												=					rescale_factor
						lowerCAmelCase__					:    Union[str, Any]												=					do_normalize
						lowerCAmelCase__					:    Dict												=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__					:    Tuple												=					image_std if image_std is not None else IMAGENET_STANDARD_STD



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = PIL.Image.BICUBIC		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					get_size_dict(__lowerCamelCase			)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
						return resize(
						    __lowerCamelCase		,size=(size['''height'''], size['''width'''])		,resample=__lowerCamelCase		,data_format=__lowerCamelCase		,**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					get_size_dict(__lowerCamelCase			)
						if "height" not in size or "width" not in size:
										raise ValueError(f"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""			)
						return center_crop(__lowerCamelCase		,size=(size['''height'''], size['''width'''])		,data_format=__lowerCamelCase		,**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      Optional[int]:

						"""simple docstring"""






						return rescale(__lowerCamelCase		,scale=__lowerCamelCase		,data_format=__lowerCamelCase		,**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      np.ndarray:

						"""simple docstring"""






						return normalize(__lowerCamelCase		,mean=__lowerCamelCase		,std=__lowerCamelCase		,data_format=__lowerCamelCase		,**__lowerCamelCase			)



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase=None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = None		,__lowerCamelCase = ChannelDimension.FIRST		,**__lowerCamelCase		,)					->      PIL.Image.Image:

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__					:    List[Any]												=					resample if resample is not None else self.resample
						lowerCAmelCase__					:    List[str]												=					do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__					:    Dict												=					do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__					:    Tuple												=					rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__					:    Dict												=					do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__					:    List[str]												=					image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__					:    Optional[Any]												=					image_std if image_std is not None else self.image_std

						lowerCAmelCase__					:    str												=					size if size is not None else self.size
						lowerCAmelCase__					:    Optional[Any]												=					get_size_dict(__lowerCamelCase			)
						lowerCAmelCase__					:    Optional[int]												=					crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__					:    Union[str, Any]												=					get_size_dict(__lowerCamelCase		,param_name='''crop_size'''			)

						lowerCAmelCase__					:    Optional[Any]												=					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_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.
						lowerCAmelCase__					:    List[str]												=					[to_numpy_array(__lowerCamelCase			) for image in images]

						if do_resize:
										lowerCAmelCase__					:    List[str]												=					[self.resize(image=__lowerCamelCase		,size=__lowerCamelCase		,resample=__lowerCamelCase			) for image in images]

						if do_center_crop:
										lowerCAmelCase__					:    str												=					[self.center_crop(image=__lowerCamelCase		,size=__lowerCamelCase			) for image in images]

						if do_rescale:
										lowerCAmelCase__					:    Tuple												=					[self.rescale(image=__lowerCamelCase		,scale=__lowerCamelCase			) for image in images]

						if do_normalize:
										lowerCAmelCase__					:    List[Any]												=					[self.normalize(image=__lowerCamelCase		,mean=__lowerCamelCase		,std=__lowerCamelCase			) for image in images]

						lowerCAmelCase__					:    Optional[int]												=					[to_channel_dimension_format(__lowerCamelCase		,__lowerCamelCase			) for image in images]

						lowerCAmelCase__					:    Optional[Any]												=					{'''pixel_values''': images}
						return BatchFeature(data=__lowerCamelCase		,tensor_type=__lowerCamelCase			)






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


__snake_case    :		Any  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vit_msn"""



		def __init__(self		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=2_24		,__lowerCamelCase=16		,__lowerCamelCase=3		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    str												=					num_hidden_layers
						lowerCAmelCase__					:    List[str]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_act
						lowerCAmelCase__					:    Tuple												=					hidden_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    int												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    str												=					patch_size
						lowerCAmelCase__					:    Optional[int]												=					num_channels
						lowerCAmelCase__					:    int												=					qkv_bias

