






"""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, 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


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





class      UpperCamelCase_       (				a_     ):
  _A				:    Dict =		['pixel_values']



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



         """simple docstring"""





         super().__init__(**snake_case__ )
         UpperCAmelCase					=	size if size is not None else {"""height""": 3_46, """width""": 3_46}
         UpperCAmelCase					=	get_size_dict(snake_case__ )
         UpperCAmelCase					=	crop_size if crop_size is not None else {"""height""": 2_89, """width""": 2_89}
         UpperCAmelCase					=	get_size_dict(snake_case__       ,						param_name="""crop_size""" )

         UpperCAmelCase					=	do_resize
         UpperCAmelCase					=	size
         UpperCAmelCase					=	resample
         UpperCAmelCase					=	do_center_crop
         UpperCAmelCase					=	crop_size
         UpperCAmelCase					=	do_rescale
         UpperCAmelCase					=	rescale_factor
         UpperCAmelCase					=	rescale_offset
         UpperCAmelCase					=	do_normalize
         UpperCAmelCase					=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
         UpperCAmelCase					=	image_std if image_std is not None else IMAGENET_STANDARD_STD
         UpperCAmelCase					=	include_top



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = PIL.Image.NEAREST       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(
             snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						resample=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





         UpperCAmelCase					=	get_size_dict(snake_case__ )
         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(snake_case__       ,						size=(size["""height"""], size["""width"""])       ,						data_format=snake_case__       ,						**snake_case__ )



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__ = True       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	Optional[Any]:



         """simple docstring"""





         if offset:
                UpperCAmelCase					=	(image - 127.5) * scale
                if data_format is not None:
                       UpperCAmelCase					=	to_channel_dimension_format(snake_case__       ,						snake_case__ )
                UpperCAmelCase					=	rescaled_image.astype(np.floataa )
         else:
                UpperCAmelCase					=	rescale(snake_case__       ,						scale=snake_case__       ,						data_format=snake_case__       ,						**snake_case__ )
         return rescaled_image



  def 						UpperCamelCase_ (					self       ,						snake_case__       ,						snake_case__       ,						snake_case__       ,						snake_case__ = None       ,						**snake_case__       ,						)	->	np.ndarray:



         """simple docstring"""





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



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



         """simple docstring"""





         UpperCAmelCase					=	do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase					=	resample if resample is not None else self.resample
         UpperCAmelCase					=	do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase					=	do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase					=	rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase					=	rescale_offset if rescale_offset is not None else self.rescale_offset
         UpperCAmelCase					=	do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase					=	image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase					=	image_std if image_std is not None else self.image_std
         UpperCAmelCase					=	include_top if include_top is not None else self.include_top

         UpperCAmelCase					=	size if size is not None else self.size
         UpperCAmelCase					=	get_size_dict(snake_case__ )
         UpperCAmelCase					=	crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase					=	get_size_dict(snake_case__       ,						param_name="""crop_size""" )

         UpperCAmelCase					=	make_list_of_images(snake_case__ )

         if not valid_images(snake_case__ ):
                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.
         UpperCAmelCase					=	[to_numpy_array(snake_case__ ) for image in images]

         if do_resize:
                UpperCAmelCase					=	[self.resize(image=snake_case__       ,						size=snake_case__       ,						resample=snake_case__ ) for image in images]

         if do_center_crop:
                UpperCAmelCase					=	[self.center_crop(image=snake_case__       ,						size=snake_case__ ) for image in images]

         if do_rescale:
                UpperCAmelCase					=	[self.rescale(image=snake_case__       ,						scale=snake_case__       ,						offset=snake_case__ ) for image in images]

         if do_normalize:
                UpperCAmelCase					=	[self.normalize(image=snake_case__       ,						mean=snake_case__       ,						std=snake_case__ ) for image in images]

         if include_top:
                UpperCAmelCase					=	[self.normalize(image=snake_case__       ,						mean=[0, 0, 0]       ,						std=snake_case__ ) for image in images]

         UpperCAmelCase					=	[to_channel_dimension_format(snake_case__       ,						snake_case__ ) for image in images]

         UpperCAmelCase					=	{"""pixel_values""": images}
         return BatchFeature(data=snake_case__       ,						tensor_type=snake_case__ )



"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__A     :    int    =		{'configuration_bit': ['BIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BitConfig', 'BitOnnxConfig']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    Optional[Any]    =		[
	    'BIT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BitForImageClassification',
	    'BitModel',
	    'BitPreTrainedModel',
	    'BitBackbone',
	]


try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A     :    List[Any]    =		['BitImageProcessor']


if TYPE_CHECKING:
	from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_bit import (
		    BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BitBackbone,
		    BitForImageClassification,
		    BitModel,
		    BitPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_bit import BitImageProcessor

else:
	import sys

	__A     :    Union[str, Any]    =		_LazyModule(__name__, globals()['__file__'], _import_structure)





