


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,
    get_resize_output_image_size,
    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, logging


a : Dict		=				logging.get_logger(__name__)
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				["pixel_values"]






					def __init__(				self ,       snake_case_ = True ,       snake_case_ = None ,       snake_case_ = PILImageResampling.BILINEAR ,       snake_case_ = True ,       snake_case_ = None ,       snake_case_ = True ,       snake_case_ = 1 / 2_5_5 ,       snake_case_ = True ,       snake_case_ = None ,       snake_case_ = None ,       **snake_case_ ,       )   ->			None:







								'''simple docstring'''

								super().__init__(**UpperCamelCase_						)
								__lowercase 					=   size if size is not None else {'shortest_edge': 2_5_6}
								__lowercase 					=   get_size_dict(UpperCamelCase_ ,       default_to_square=UpperCamelCase_						)
								__lowercase 					=   crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
								__lowercase 					=   get_size_dict(UpperCamelCase_						)
								__lowercase 					=   do_resize
								__lowercase 					=   size
								__lowercase 					=   resample
								__lowercase 					=   do_center_crop
								__lowercase 					=   crop_size
								__lowercase 					=   do_rescale
								__lowercase 					=   rescale_factor
								__lowercase 					=   do_normalize
								__lowercase 					=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase 					=   image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = PILImageResampling.BICUBIC ,       snake_case_ = None ,       **snake_case_ ,       )   ->			np.ndarray:







								'''simple docstring'''

								__lowercase 					=   get_size_dict(UpperCamelCase_ ,       default_to_square=UpperCamelCase_						)
								if "shortest_edge" not in size:
											raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'						)
								__lowercase 					=   get_resize_output_image_size(UpperCamelCase_ ,       size=size['''shortest_edge'''] ,       default_to_square=UpperCamelCase_						)
								return resize(UpperCamelCase_ ,       size=UpperCamelCase_ ,       resample=UpperCamelCase_ ,       data_format=UpperCamelCase_ ,       **UpperCamelCase_						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = None ,       **snake_case_ ,       )   ->			np.ndarray:







								'''simple docstring'''

								__lowercase 					=   get_size_dict(UpperCamelCase_						)
								return center_crop(UpperCamelCase_ ,       size=(size['''height'''], size['''width''']) ,       data_format=UpperCamelCase_ ,       **UpperCamelCase_						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = None ,       **snake_case_						)   ->			np.ndarray:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ = None ,       **snake_case_ ,       )   ->			np.ndarray:







								'''simple docstring'''

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






					def A      (				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_ = ChannelDimension.FIRST ,       **snake_case_ ,       )   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   do_resize if do_resize is not None else self.do_resize
								__lowercase 					=   size if size is not None else self.size
								__lowercase 					=   get_size_dict(UpperCamelCase_ ,       default_to_square=UpperCamelCase_						)
								__lowercase 					=   resample if resample is not None else self.resample
								__lowercase 					=   do_center_crop if do_center_crop is not None else self.do_center_crop
								__lowercase 					=   crop_size if crop_size is not None else self.crop_size
								__lowercase 					=   get_size_dict(UpperCamelCase_						)
								__lowercase 					=   do_rescale if do_rescale is not None else self.do_rescale
								__lowercase 					=   rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase 					=   do_normalize if do_normalize is not None else self.do_normalize
								__lowercase 					=   image_mean if image_mean is not None else self.image_mean
								__lowercase 					=   image_std if image_std is not None else self.image_std

								__lowercase 					=   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:
											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.'''						)

								# All transformations expect numpy arrays.
								__lowercase 					=   [to_numpy_array(UpperCamelCase_						) for image in images]

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

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

								if do_rescale:
											__lowercase 					=   [self.rescale(image=UpperCamelCase_ ,       scale=UpperCamelCase_						) for image in images]

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

								__lowercase 					=   [to_channel_dimension_format(UpperCamelCase_ ,       UpperCamelCase_						) for image in images]

								__lowercase 					=   {'pixel_values': images}
								return BatchFeature(data=UpperCamelCase_ ,       tensor_type=UpperCamelCase_						)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a : List[str]		=				{
    '''configuration_table_transformer''': [
        '''TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''TableTransformerConfig''',
        '''TableTransformerOnnxConfig''',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Union[str, Any]		=				[
	    '''TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TableTransformerForObjectDetection''',
	    '''TableTransformerModel''',
	    '''TableTransformerPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_table_transformer import (
	    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TableTransformerConfig,
	    TableTransformerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TableTransformerForObjectDetection,
		    TableTransformerModel,
		    TableTransformerPreTrainedModel,
		)

else:
	import sys

	a : Any		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


