

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A_			:					Union[str, Any]    			=     logging.get_logger(__name__)



class        _a		(_SCREAMING_SNAKE_CASE							):


					'''simple docstring'''






					UpperCAmelCase__:  Any    				=			['''pixel_values''']






					def __init__(					self       ,			A__ = True       ,			A__ = 32       ,			A__=PILImageResampling.BILINEAR       ,			A__ = True       ,			**A__       ,			):
						A__				:       Union[str, Any]	  =						do_resize
						A__				:       Tuple	  =						do_rescale
						A__				:       Tuple	  =						size_divisor
						A__				:       Any	  =						resample
						super().__init__(**_lowercase     )






					def     __A     (					self       ,			A__       ,			A__       ,			A__       ,			A__ = None       ,			**A__     ):
						A__  ,  A__				:       List[str]	  =						get_image_size(_lowercase     )
						# Rounds the height and width down to the closest multiple of size_divisor
						A__				:       Dict	  =						height // size_divisor * size_divisor
						A__				:       Union[str, Any]	  =						width // size_divisor * size_divisor
						A__				:       Any	  =						resize(_lowercase       ,			(new_h, new_w)       ,			resample=_lowercase       ,			data_format=_lowercase       ,			**_lowercase     )
						return image






					def     __A     (					self       ,			A__       ,			A__       ,			A__ = None       ,			**A__     ):
						return rescale(image=_lowercase       ,			scale=_lowercase       ,			data_format=_lowercase       ,			**_lowercase     )






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = None       ,			A__=None       ,			A__ = None       ,			A__ = None       ,			A__ = ChannelDimension.FIRST       ,			**A__       ,			):
						A__				:       Tuple	  =						do_resize if do_resize is not None else self.do_resize
						A__				:       Union[str, Any]	  =						do_rescale if do_rescale is not None else self.do_rescale
						A__				:       Tuple	  =						size_divisor if size_divisor is not None else self.size_divisor
						A__				:       Union[str, Any]	  =						resample if resample is not None else self.resample

						if do_resize and size_divisor is None:
							raise ValueError("""size_divisor is required for resizing"""     )

						A__				:       Optional[int]	  =						make_list_of_images(_lowercase     )

						if not valid_images(_lowercase     ):
							raise ValueError("""Invalid image(s)"""     )

						# All transformations expect numpy arrays.
						A__				:       List[Any]	  =						[to_numpy_array(_lowercase     ) for img in images]

						if do_resize:
							A__				:       str	  =						[self.resize(_lowercase       ,			size_divisor=_lowercase       ,			resample=_lowercase     ) for image in images]

						if do_rescale:
							A__				:       List[str]	  =						[self.rescale(_lowercase       ,			scale=1 / 255     ) for image in images]

						A__				:       Optional[int]	  =						[to_channel_dimension_format(_lowercase       ,			_lowercase     ) for image in images]

						A__				:       Optional[Any]	  =						{"""pixel_values""": images}
						return BatchFeature(data=_lowercase       ,			tensor_type=_lowercase     )





'''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__)
