


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       lowercase_					(lowercase__   ):
				snake_case	    =['image_processor']
				snake_case	    ='SamImageProcessor'





				def __init__(				self       ,  lowercase_)  -> Optional[Any]:
											super().__init__(lowercase_)
											a__      =self.image_processor
											a__      =-10
											a__      =self.image_processor.size['longest_edge']





				def __call__(				self       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_ = None       ,  **lowercase_       ,  )  -> BatchEncoding:
											a__      =self.image_processor(
											    lowercase_       ,  return_tensors=lowercase_       ,  **lowercase_       ,  )

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

											if hasattr(lowercase_       ,  'numpy'):  # Checks if Torch or TF tensor
																		a__      =original_sizes.numpy()

											a__    ,     a__    ,     a__      =self._check_and_preprocess_points(
											    input_points=lowercase_       ,  input_labels=lowercase_       ,  input_boxes=lowercase_       ,  )

											a__      =self._normalize_and_convert(
											    lowercase_       ,  lowercase_       ,  input_points=lowercase_       ,  input_labels=lowercase_       ,  input_boxes=lowercase_       ,  return_tensors=lowercase_       ,  )

											return encoding_image_processor





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_="pt"       ,  )  -> Optional[Any]:
											if input_points is not None:
																		if len(lowercase_) != len(lowercase_):
																									a__      =[
																									    self._normalize_coordinates(self.target_size       ,  lowercase_       ,  original_sizes[0]) for point in input_points
																									]
																		else:
																									a__      =[
																									    self._normalize_coordinates(self.target_size       ,  lowercase_       ,  lowercase_)
																									    for point, original_size in zip(lowercase_       ,  lowercase_)
																									]
																		# 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:
																																a__    ,     a__      =self._pad_points_and_labels(lowercase_       ,  lowercase_)

																		a__      =np.array(lowercase_)

											if input_labels is not None:
																		a__      =np.array(lowercase_)

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

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

											return encoding_image_processor





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





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_=False)  -> np.ndarray:
											a__    ,     a__      =original_size
											a__    ,     a__      =self.image_processor._get_preprocess_shape(lowercase_       ,  longest_edge=lowercase_)
											a__      =deepcopy(lowercase_).astype(lowercase_)

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

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

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

											return coords





				def 						__UpperCamelCase    (				self       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=None       ,  )  -> Tuple:
											if input_points is not None:
																		if hasattr(lowercase_       ,  'numpy'):  # Checks for TF or Torch tensor
																									a__      =input_points.numpy().tolist()

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

											if input_labels is not None:
																		if hasattr(lowercase_       ,  'numpy'):
																									a__      =input_labels.numpy().tolist()

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

											if input_boxes is not None:
																		if hasattr(lowercase_       ,  'numpy'):
																									a__      =input_boxes.numpy().tolist()

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

											return input_points, input_labels, input_boxes





				@property
				def 						__UpperCamelCase    (				self)  -> Optional[Any]:
											a__      =self.image_processor.model_input_names
											return list(dict.fromkeys(lowercase_))





				def 						__UpperCamelCase    (				self       ,  *lowercase_       ,  **lowercase_)  -> Any:
											return self.image_processor.post_process_masks(*lowercase_       ,  **lowercase_)





from typing import TYPE_CHECKING

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


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    '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

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

