

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, is_vision_available, logging


if is_vision_available():
					import PIL


_UpperCAmelCase						:  Any									=			logging.get_logger(__name__)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''pixel_values''']




				def __init__(   self ,  snake_case = True ,  snake_case = None ,  snake_case = None ,  snake_case = PILImageResampling.BILINEAR ,  snake_case = True ,  snake_case = 1 / 255 ,  snake_case = True ,  snake_case = None ,  snake_case = None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											snake_case_     							=       size if size is not None else {'shortest_edge': 384}
											snake_case_     							=       get_size_dict(snake_case ,  default_to_square=snake_case			)

											snake_case_     							=       do_resize
											snake_case_     							=       size
											# Default value set here for backwards compatibility where the value in config is None
											snake_case_     							=       crop_pct if crop_pct is not None else 224 / 256
											snake_case_     							=       resample
											snake_case_     							=       do_rescale
											snake_case_     							=       rescale_factor
											snake_case_     							=       do_normalize
											snake_case_     							=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											snake_case_     							=       image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = PILImageResampling.BICUBIC ,  snake_case = None ,  **snake_case ,  ):
											snake_case_     							=       get_size_dict(snake_case ,  default_to_square=snake_case			)
											if "shortest_edge" not in size:
																		raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''			)
											snake_case_     							=       size['shortest_edge']

											if shortest_edge < 384:
																		# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																		snake_case_     							=       int(shortest_edge / crop_pct			)
																		snake_case_     							=       get_resize_output_image_size(snake_case ,  size=snake_case ,  default_to_square=snake_case			)
																		snake_case_     							=       resize(image=snake_case ,  size=snake_case ,  resample=snake_case ,  data_format=snake_case ,  **snake_case			)
																		# then crop to (shortest_edge, shortest_edge)
																		return center_crop(image=snake_case ,  size=(shortest_edge, shortest_edge) ,  data_format=snake_case ,  **snake_case			)
											else:
																		# warping (no cropping) when evaluated at 384 or larger
																		return resize(
																		    snake_case ,  size=(shortest_edge, shortest_edge) ,  resample=snake_case ,  data_format=snake_case ,  **snake_case			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case = None ,  **snake_case ,  ):
											return rescale(snake_case ,  scale=snake_case ,  data_format=snake_case ,  **snake_case			)




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case ,  snake_case = None ,  **snake_case ,  ):
											return normalize(snake_case ,  mean=snake_case ,  std=snake_case ,  data_format=snake_case ,  **snake_case			)




				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 = ChannelDimension.FIRST ,  **snake_case ,  ):
											snake_case_     							=       do_resize if do_resize is not None else self.do_resize
											snake_case_     							=       crop_pct if crop_pct is not None else self.crop_pct
											snake_case_     							=       resample if resample is not None else self.resample
											snake_case_     							=       do_rescale if do_rescale is not None else self.do_rescale
											snake_case_     							=       rescale_factor if rescale_factor is not None else self.rescale_factor
											snake_case_     							=       do_normalize if do_normalize is not None else self.do_normalize
											snake_case_     							=       image_mean if image_mean is not None else self.image_mean
											snake_case_     							=       image_std if image_std is not None else self.image_std

											snake_case_     							=       size if size is not None else self.size
											snake_case_     							=       get_size_dict(snake_case ,  default_to_square=snake_case			)

											snake_case_     							=       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_resize and size["shortest_edge"] < 384 and crop_pct is None:
																		raise ValueError('crop_pct must be specified if size < 384.'			)

											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.
											snake_case_     							=       [to_numpy_array(snake_case			) for image in images]

											if do_resize:
																		snake_case_     							=       [self.resize(image=snake_case ,  size=snake_case ,  crop_pct=snake_case ,  resample=snake_case			) for image in images]

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

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

											snake_case_     							=       [to_channel_dimension_format(snake_case ,  snake_case			) for image in images]

											snake_case_     							=       {'pixel_values': images}
											return BatchFeature(data=snake_case ,  tensor_type=snake_case			)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:  int									=			{
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase						:  str									=			[
					    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LiltForQuestionAnswering""",
					    """LiltForSequenceClassification""",
					    """LiltForTokenClassification""",
					    """LiltModel""",
					    """LiltPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lilt import (
										    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LiltForQuestionAnswering,
										    LiltForSequenceClassification,
										    LiltForTokenClassification,
										    LiltModel,
										    LiltPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase						:  Dict									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




