






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 convert_to_rgb, 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


if is_vision_available():
		import PIL


__UpperCAmelCase								=						logging.get_logger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Dict       						=  ['''pixel_values''']



		def __init__(							self				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = PILImageResampling.BICUBIC				,		_UpperCamelCase = True				,		_UpperCamelCase = 1 / 2_5_5				,		_UpperCamelCase = True				,		_UpperCamelCase = None				,		_UpperCamelCase = None				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							None:
							super().__init__(**_UpperCamelCase   )
							UpperCAmelCase_    :      str            =						size if size is not None else {'height': 3_8_4, 'width': 3_8_4}
							UpperCAmelCase_    :      Optional[Any]            =						get_size_dict(_UpperCamelCase				,		default_to_square=_UpperCamelCase   )

							UpperCAmelCase_    :      Dict            =						do_resize
							UpperCAmelCase_    :      Optional[int]            =						size
							UpperCAmelCase_    :      int            =						resample
							UpperCAmelCase_    :      Any            =						do_rescale
							UpperCAmelCase_    :      Dict            =						rescale_factor
							UpperCAmelCase_    :      Union[str, Any]            =						do_normalize
							UpperCAmelCase_    :      int            =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							UpperCAmelCase_    :      int            =						image_std if image_std is not None else OPENAI_CLIP_STD
							UpperCAmelCase_    :      Dict            =						do_convert_rgb



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = PILImageResampling.BICUBIC				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							np.ndarray:
							UpperCAmelCase_    :      List[str]            =						get_size_dict(_UpperCamelCase				,		default_to_square=_UpperCamelCase   )
							if "height" not in size or "width" not in size:
												raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"   )
							UpperCAmelCase_    :      Optional[int]            =						(size['height'], size['width'])
							return resize(_UpperCamelCase				,		size=_UpperCamelCase				,		resample=_UpperCamelCase				,		data_format=_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = None				,		**_UpperCamelCase				,		)	->							Dict:
							return rescale(_UpperCamelCase				,		scale=_UpperCamelCase				,		data_format=_UpperCamelCase				,		**_UpperCamelCase   )



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



		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 = ChannelDimension.FIRST				,		**_UpperCamelCase				,		)	->							PIL.Image.Image:
							UpperCAmelCase_    :      Tuple            =						do_resize if do_resize is not None else self.do_resize
							UpperCAmelCase_    :      List[Any]            =						resample if resample is not None else self.resample
							UpperCAmelCase_    :      str            =						do_rescale if do_rescale is not None else self.do_rescale
							UpperCAmelCase_    :      Optional[Any]            =						rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCAmelCase_    :      Any            =						do_normalize if do_normalize is not None else self.do_normalize
							UpperCAmelCase_    :      str            =						image_mean if image_mean is not None else self.image_mean
							UpperCAmelCase_    :      List[str]            =						image_std if image_std is not None else self.image_std
							UpperCAmelCase_    :      Optional[Any]            =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCAmelCase_    :      List[Any]            =						size if size is not None else self.size
							UpperCAmelCase_    :      int            =						get_size_dict(_UpperCamelCase				,		default_to_square=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						make_list_of_images(_UpperCamelCase   )

							if not valid_images(_UpperCamelCase   ):
												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_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:
												UpperCAmelCase_    :      Dict            =						[convert_to_rgb(_UpperCamelCase   ) for image in images]

							# All transformations expect numpy arrays.
							UpperCAmelCase_    :      List[str]            =						[to_numpy_array(_UpperCamelCase   ) for image in images]

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

							if do_rescale:
												UpperCAmelCase_    :      Optional[int]            =						[self.rescale(image=_UpperCamelCase				,		scale=_UpperCamelCase   ) for image in images]

							if do_normalize:
												UpperCAmelCase_    :      Union[str, Any]            =						[self.normalize(image=_UpperCamelCase				,		mean=_UpperCamelCase				,		std=_UpperCamelCase   ) for image in images]

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

							UpperCAmelCase_    :      Any            =						BatchFeature(data={'pixel_values': images}				,		tensor_type=_UpperCamelCase   )

							return encoded_outputs














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase								=						{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase								=						[
		    'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'ViTMSNModel',
		    'ViTMSNForImageClassification',
		    'ViTMSNPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

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






