
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case				:   Optional[Any]     =				{
    """configuration_swiftformer""": [
        """SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SwiftFormerConfig""",
        """SwiftFormerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_snake_case				:   Dict     =				[
						    """SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwiftFormerForImageClassification""",
						    """SwiftFormerModel""",
						    """SwiftFormerPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swiftformer import (
						    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    SwiftFormerConfig,
						    SwiftFormerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swiftformer import (
												    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwiftFormerForImageClassification,
												    SwiftFormerModel,
												    SwiftFormerPreTrainedModel,
												)

else:
						import sys

						_snake_case				:   List[Any]     =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

'''simple docstring'''







from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
						import torch

if is_tf_available():
						import tensorflow as tf


class 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Optional[int]       =					["""image_processor"""]
		a							:					Tuple       =					"""SamImageProcessor"""




		def __init__(				self					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.image_processor
								_SCREAMING_SNAKE_CASE				= -10
								_SCREAMING_SNAKE_CASE				= self.image_processor.size["longest_edge"]




		def __call__(				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.image_processor(
								    UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)

								# pop arguments that are not used in the foward but used nevertheless
								_SCREAMING_SNAKE_CASE				= encoding_image_processor["original_sizes"]

								if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks if Torch or TF tensor
														_SCREAMING_SNAKE_CASE				= original_sizes.numpy()

								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._check_and_preprocess_points(
								    input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= self._normalize_and_convert(
								    UpperCamelCase					,						UpperCamelCase					,						input_points=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						return_tensors=UpperCamelCase					,						)

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase="pt"					,						):
								if input_points is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]      ) for point in input_points
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase      )
																				    for point, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														# check that all arrays have the same shape
														if not all(point.shape == input_points[0].shape for point in input_points      ):
																				if input_labels is not None:
																										_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self._pad_points_and_labels(UpperCamelCase					,						UpperCamelCase      )

														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_labels is not None:
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if len(UpperCamelCase      ) != len(UpperCamelCase      ):
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						original_sizes[0]					,						is_bounding_box=UpperCamelCase      )
																				    for box in input_boxes
																				]
														else:
																				_SCREAMING_SNAKE_CASE				= [
																				    self._normalize_coordinates(self.target_size					,						UpperCamelCase					,						UpperCamelCase					,						is_bounding_box=UpperCamelCase      )
																				    for box, original_size in zip(UpperCamelCase					,						UpperCamelCase      )
																				]
														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

								if input_boxes is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_boxes.unsqueeze(1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_boxes.shape      ) != 3 else input_boxes
														encoding_image_processor.update({"input_boxes": input_boxes}      )
								if input_points is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_points.unsqueeze(1      ) if len(input_points.shape      ) != 4 else input_points
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_points.shape      ) != 4 else input_points
														encoding_image_processor.update({"input_points": input_points}      )
								if input_labels is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= input_labels.unsqueeze(1      ) if len(input_labels.shape      ) != 3 else input_labels
														elif return_tensors == "tf":
																				_SCREAMING_SNAKE_CASE				= tf.convert_to_tensor(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= tf.expand_dims(UpperCamelCase					,						1      ) if len(input_labels.shape      ) != 3 else input_labels
														encoding_image_processor.update({"input_labels": input_labels}      )

								return encoding_image_processor




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= max([point.shape[0] for point in input_points]      )
								_SCREAMING_SNAKE_CASE				= []
								for i, point in enumerate(UpperCamelCase      ):
														if point.shape[0] != expected_nb_points:
																				_SCREAMING_SNAKE_CASE				= np.concatenate(
																				    [point, np.zeros((expected_nb_points - point.shape[0], 2)      ) + self.point_pad_value]					,						axis=0      )
																				_SCREAMING_SNAKE_CASE				= np.append(input_labels[i]					,						[self.point_pad_value]      )
														processed_input_points.append(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processed_input_points
								return input_points, input_labels




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= original_size
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.image_processor._get_preprocess_shape(UpperCamelCase					,						longest_edge=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= deepcopy(UpperCamelCase      ).astype(UpperCamelCase      )

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						2					,						2      )

								_SCREAMING_SNAKE_CASE				= coords[..., 0] * (new_w / old_w)
								_SCREAMING_SNAKE_CASE				= coords[..., 1] * (new_h / old_h)

								if is_bounding_box:
														_SCREAMING_SNAKE_CASE				= coords.reshape(-1					,						4      )

								return coords




		def     lowercase       (				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						):
								if input_points is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):  # Checks for TF or Torch tensor
																				_SCREAMING_SNAKE_CASE				= input_points.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_points[0]					,						UpperCamelCase      ):
																				raise ValueError("Input points must be a list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for input_point in input_points]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_labels is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_labels.numpy().tolist()

														if not isinstance(UpperCamelCase					,						UpperCamelCase      ) or not isinstance(input_labels[0]					,						UpperCamelCase      ):
																				raise ValueError("Input labels must be a list of list integers."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for label in input_labels]
								else:
														_SCREAMING_SNAKE_CASE				= None

								if input_boxes is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= input_boxes.numpy().tolist()

														if (
														    not isinstance(UpperCamelCase					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0]					,						UpperCamelCase      )
														    or not isinstance(input_boxes[0][0]					,						UpperCamelCase      )
														):
																				raise ValueError("Input boxes must be a list of list of list of floating points."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ).astype(np.floataa      ) for box in input_boxes]
								else:
														_SCREAMING_SNAKE_CASE				= None

								return input_points, input_labels, input_boxes




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCamelCase      )      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.image_processor.post_process_masks(*UpperCamelCase					,						**UpperCamelCase      )