

"""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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


a		=logging.get_logger(__name__)


if is_vision_available():
					import PIL






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Union[str, Any]										=				['pixel_values']
							def __init__(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      int = True       ,SCREAMING_SNAKE_CASE__		:      Dict = None       ,SCREAMING_SNAKE_CASE__		:      List[Any] = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = True       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = True       ,SCREAMING_SNAKE_CASE__		:      str = 1 / 2_5_5       ,SCREAMING_SNAKE_CASE__		:      Optional[int] = True       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      Tuple = None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = True       ,**SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,):
								super().__init__(**__lowerCAmelCase)
								__lowerCamelCase    :    Tuple 	=			size if size is not None else {'shortest_edge': 2_2_4}
								__lowerCamelCase    :    str 	=			get_size_dict(__lowerCAmelCase       ,default_to_square=__lowerCAmelCase)
								__lowerCamelCase    :    Any 	=			crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowerCamelCase    :    str 	=			get_size_dict(__lowerCAmelCase       ,default_to_square=__lowerCAmelCase       ,param_name='crop_size')

								__lowerCamelCase    :    List[str] 	=			do_resize
								__lowerCamelCase    :    Any 	=			size
								__lowerCamelCase    :    int 	=			resample
								__lowerCamelCase    :    Optional[int] 	=			do_center_crop
								__lowerCamelCase    :    Any 	=			crop_size
								__lowerCamelCase    :    Optional[Any] 	=			do_rescale
								__lowerCamelCase    :    Optional[Any] 	=			rescale_factor
								__lowerCamelCase    :    Optional[Any] 	=			do_normalize
								__lowerCamelCase    :    str 	=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
								__lowerCamelCase    :    Any 	=			image_std if image_std is not None else OPENAI_CLIP_STD
								__lowerCamelCase    :    List[Any] 	=			do_convert_rgb
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      List[Any] = PILImageResampling.BICUBIC       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,**SCREAMING_SNAKE_CASE__		:      int       ,):
								__lowerCamelCase    :    Union[str, Any] 	=			get_size_dict(__lowerCAmelCase       ,default_to_square=__lowerCAmelCase)
								if "shortest_edge" not in size:
									raise ValueError(F"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}")
								__lowerCamelCase    :    Optional[int] 	=			get_resize_output_image_size(__lowerCAmelCase       ,size=size['shortest_edge']       ,default_to_square=__lowerCAmelCase)
								return resize(__lowerCAmelCase       ,size=__lowerCAmelCase       ,resample=__lowerCAmelCase       ,data_format=__lowerCAmelCase       ,**__lowerCAmelCase)
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      List[str]       ,SCREAMING_SNAKE_CASE__		:      Dict = None       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]       ,):
								__lowerCamelCase    :    Tuple 	=			get_size_dict(__lowerCAmelCase)
								if "height" not in size or "width" not in size:
									raise ValueError(F"The `size` parameter must contain the keys (height, width). Got {size.keys()}")
								return center_crop(__lowerCAmelCase       ,size=(size['height'], size['width'])       ,data_format=__lowerCAmelCase       ,**__lowerCAmelCase)
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = None       ,**SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,):
								return rescale(__lowerCAmelCase       ,scale=__lowerCAmelCase       ,data_format=__lowerCAmelCase       ,**__lowerCAmelCase)
							def    lowerCAmelCase					(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      Dict       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,**SCREAMING_SNAKE_CASE__		:      Dict       ,):
								return normalize(__lowerCAmelCase       ,mean=__lowerCAmelCase       ,std=__lowerCAmelCase       ,data_format=__lowerCAmelCase       ,**__lowerCAmelCase)


							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Any       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any] = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      List[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = None       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      List[str] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[int] = None       ,SCREAMING_SNAKE_CASE__		:      Any = None       ,SCREAMING_SNAKE_CASE__		:      str = None       ,SCREAMING_SNAKE_CASE__		:      Optional[Any] = None       ,SCREAMING_SNAKE_CASE__		:      Optional[int] = ChannelDimension.FIRST       ,**SCREAMING_SNAKE_CASE__		:      Dict       ,):
								__lowerCamelCase    :    Tuple 	=			do_resize if do_resize is not None else self.do_resize
								__lowerCamelCase    :    Dict 	=			size if size is not None else self.size
								__lowerCamelCase    :    Optional[int] 	=			get_size_dict(__lowerCAmelCase       ,param_name='size'       ,default_to_square=__lowerCAmelCase)
								__lowerCamelCase    :    Any 	=			resample if resample is not None else self.resample
								__lowerCamelCase    :    Union[str, Any] 	=			do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowerCamelCase    :    List[str] 	=			crop_size if crop_size is not None else self.crop_size
								__lowerCamelCase    :    Any 	=			get_size_dict(__lowerCAmelCase       ,param_name='crop_size'       ,default_to_square=__lowerCAmelCase)
								__lowerCamelCase    :    Optional[int] 	=			do_rescale if do_rescale is not None else self.do_rescale
								__lowerCamelCase    :    int 	=			rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowerCamelCase    :    Any 	=			do_normalize if do_normalize is not None else self.do_normalize
								__lowerCamelCase    :    Any 	=			image_mean if image_mean is not None else self.image_mean
								__lowerCamelCase    :    List[Any] 	=			image_std if image_std is not None else self.image_std
								__lowerCamelCase    :    Dict 	=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

								__lowerCamelCase    :    Union[str, 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:
									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.')

								# PIL RGBA images are converted to RGB
								if do_convert_rgb:
									__lowerCamelCase    :    Optional[Any] 	=			[convert_to_rgb(__lowerCAmelCase) for image in images]

								# All transformations expect numpy arrays.
								__lowerCamelCase    :    List[Any] 	=			[to_numpy_array(__lowerCAmelCase) for image in images]

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

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

								if do_rescale:
									__lowerCamelCase    :    Union[str, Any] 	=			[self.rescale(image=__lowerCAmelCase       ,scale=__lowerCAmelCase) for image in images]

								if do_normalize:
									__lowerCamelCase    :    int 	=			[self.normalize(image=__lowerCAmelCase       ,mean=__lowerCAmelCase       ,std=__lowerCAmelCase) for image in images]

								__lowerCamelCase    :    List[Any] 	=			[to_channel_dimension_format(__lowerCAmelCase       ,__lowerCAmelCase) for image in images]

								__lowerCamelCase    :    str 	=			{'pixel_values': images}
								return BatchFeature(data=__lowerCAmelCase       ,tensor_type=__lowerCAmelCase)







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


a		=logging.get_logger(__name__)

a		={
    """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 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  str										=				'''vit_msn'''
							def __init__(		self		:      Any       ,SCREAMING_SNAKE_CASE__		:      int=7_6_8       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=1_2       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]=1_2       ,SCREAMING_SNAKE_CASE__		:      Tuple=3_0_7_2       ,SCREAMING_SNAKE_CASE__		:      str="gelu"       ,SCREAMING_SNAKE_CASE__		:      Any=0.0       ,SCREAMING_SNAKE_CASE__		:      str=0.0       ,SCREAMING_SNAKE_CASE__		:      Any=0.02       ,SCREAMING_SNAKE_CASE__		:      int=1E-06       ,SCREAMING_SNAKE_CASE__		:      Optional[int]=2_2_4       ,SCREAMING_SNAKE_CASE__		:      Dict=1_6       ,SCREAMING_SNAKE_CASE__		:      int=3       ,SCREAMING_SNAKE_CASE__		:      List[str]=True       ,**SCREAMING_SNAKE_CASE__		:      str       ,):
								super().__init__(**SCREAMING_SNAKE_CASE__)

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




