



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__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =["""image_processor"""]
		snake_case_     ="""SamImageProcessor"""



		def __init__(self		,__lowerCamelCase			)					->      Tuple:

						"""simple docstring"""






						super().__init__(__lowerCamelCase			)
						lowerCAmelCase__					:    int												=					self.image_processor
						lowerCAmelCase__					:    Tuple												=					-10
						lowerCAmelCase__					:    str												=					self.image_processor.size['''longest_edge''']



		def __call__(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase = None		,**__lowerCamelCase		,)					->      BatchEncoding:

						"""simple docstring"""






						lowerCAmelCase__					:    List[str]												=					self.image_processor(
						    __lowerCamelCase		,return_tensors=__lowerCamelCase		,**__lowerCamelCase		,)

						# pop arguments that are not used in the foward but used nevertheless
						lowerCAmelCase__					:    Union[str, Any]												=					encoding_image_processor['''original_sizes''']

						if hasattr(__lowerCamelCase		,'''numpy'''			):  # Checks if Torch or TF tensor
										lowerCAmelCase__					:    str												=					original_sizes.numpy()

						lowerCAmelCase__						,		lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[int]												=					self._check_and_preprocess_points(
						    input_points=__lowerCamelCase		,input_labels=__lowerCamelCase		,input_boxes=__lowerCamelCase		,)

						lowerCAmelCase__					:    Optional[int]												=					self._normalize_and_convert(
						    __lowerCamelCase		,__lowerCamelCase		,input_points=__lowerCamelCase		,input_labels=__lowerCamelCase		,input_boxes=__lowerCamelCase		,return_tensors=__lowerCamelCase		,)

						return encoding_image_processor



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase="pt"		,)					->      List[Any]:

						"""simple docstring"""






						if input_points is not None:
										if len(__lowerCamelCase			) != len(__lowerCamelCase			):
														lowerCAmelCase__					:    Tuple												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,original_sizes[0]			) for point in input_points
														]
										else:
														lowerCAmelCase__					:    Dict												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,__lowerCamelCase			)
														    for point, original_size in zip(__lowerCamelCase		,__lowerCamelCase			)
														]
										# 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:
																		lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					self._pad_points_and_labels(__lowerCamelCase		,__lowerCamelCase			)

										lowerCAmelCase__					:    str												=					np.array(__lowerCamelCase			)

						if input_labels is not None:
										lowerCAmelCase__					:    str												=					np.array(__lowerCamelCase			)

						if input_boxes is not None:
										if len(__lowerCamelCase			) != len(__lowerCamelCase			):
														lowerCAmelCase__					:    Union[str, Any]												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,original_sizes[0]		,is_bounding_box=__lowerCamelCase			)
														    for box in input_boxes
														]
										else:
														lowerCAmelCase__					:    List[str]												=					[
														    self._normalize_coordinates(self.target_size		,__lowerCamelCase		,__lowerCamelCase		,is_bounding_box=__lowerCamelCase			)
														    for box, original_size in zip(__lowerCamelCase		,__lowerCamelCase			)
														]
										lowerCAmelCase__					:    int												=					np.array(__lowerCamelCase			)

						if input_boxes is not None:
										if return_tensors == "pt":
														lowerCAmelCase__					:    List[str]												=					torch.from_numpy(__lowerCamelCase			)
														# boxes batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					input_boxes.unsqueeze(1			) if len(input_boxes.shape			) != 3 else input_boxes
										elif return_tensors == "tf":
														lowerCAmelCase__					:    Union[str, Any]												=					tf.convert_to_tensor(__lowerCamelCase			)
														# boxes batch size of 1 by default
														lowerCAmelCase__					:    int												=					tf.expand_dims(__lowerCamelCase		,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":
														lowerCAmelCase__					:    List[str]												=					torch.from_numpy(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					input_points.unsqueeze(1			) if len(input_points.shape			) != 4 else input_points
										elif return_tensors == "tf":
														lowerCAmelCase__					:    List[str]												=					tf.convert_to_tensor(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Tuple												=					tf.expand_dims(__lowerCamelCase		,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":
														lowerCAmelCase__					:    Any												=					torch.from_numpy(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Dict												=					input_labels.unsqueeze(1			) if len(input_labels.shape			) != 3 else input_labels
										elif return_tensors == "tf":
														lowerCAmelCase__					:    Dict												=					tf.convert_to_tensor(__lowerCamelCase			)
														# point batch size of 1 by default
														lowerCAmelCase__					:    Any												=					tf.expand_dims(__lowerCamelCase		,1			) if len(input_labels.shape			) != 3 else input_labels
										encoding_image_processor.update({'''input_labels''': input_labels}			)

						return encoding_image_processor



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					max([point.shape[0] for point in input_points]			)
						lowerCAmelCase__					:    Any												=					[]
						for i, point in enumerate(__lowerCamelCase			):
										if point.shape[0] != expected_nb_points:
														lowerCAmelCase__					:    int												=					np.concatenate(
														    [point, np.zeros((expected_nb_points - point.shape[0], 2)			) + self.point_pad_value]		,axis=0			)
														lowerCAmelCase__					:    Optional[Any]												=					np.append(input_labels[i]		,[self.point_pad_value]			)
										processed_input_points.append(__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					processed_input_points
						return input_points, input_labels



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase=False			)					->      np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					original_size
						lowerCAmelCase__						,		lowerCAmelCase__					:    str												=					self.image_processor._get_preprocess_shape(__lowerCamelCase		,longest_edge=__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					deepcopy(__lowerCamelCase			).astype(__lowerCamelCase			)

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

						lowerCAmelCase__					:    Optional[Any]												=					coords[..., 0] * (new_w / old_w)
						lowerCAmelCase__					:    Optional[int]												=					coords[..., 1] * (new_h / old_h)

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

						return coords



		def 		lowerCAmelCase__				(self		,__lowerCamelCase=None		,__lowerCamelCase=None		,__lowerCamelCase=None		,)					->      List[str]:

						"""simple docstring"""






						if input_points is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):  # Checks for TF or Torch tensor
														lowerCAmelCase__					:    Union[str, Any]												=					input_points.numpy().tolist()

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

						if input_labels is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):
														lowerCAmelCase__					:    Optional[Any]												=					input_labels.numpy().tolist()

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

						if input_boxes is not None:
										if hasattr(__lowerCamelCase		,'''numpy'''			):
														lowerCAmelCase__					:    int												=					input_boxes.numpy().tolist()

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

						return input_points, input_labels, input_boxes



		@property
		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					self.image_processor.model_input_names
						return list(dict.fromkeys(__lowerCamelCase			)			)



		def 		lowerCAmelCase__				(self		,*__lowerCamelCase		,**__lowerCamelCase			)					->      List[Any]:

						"""simple docstring"""






						return self.image_processor.post_process_masks(*__lowerCamelCase		,**__lowerCamelCase			)






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case    :		Any  =logging.get_logger(__name__)

__snake_case    :		Tuple  ={
    'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""vit_msn"""



		def __init__(self		,__lowerCamelCase=7_68		,__lowerCamelCase=12		,__lowerCamelCase=12		,__lowerCamelCase=30_72		,__lowerCamelCase="gelu"		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-06		,__lowerCamelCase=2_24		,__lowerCamelCase=16		,__lowerCamelCase=3		,__lowerCamelCase=True		,**__lowerCamelCase		,)					->      Any:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					hidden_size
						lowerCAmelCase__					:    str												=					num_hidden_layers
						lowerCAmelCase__					:    List[str]												=					num_attention_heads
						lowerCAmelCase__					:    Optional[int]												=					intermediate_size
						lowerCAmelCase__					:    List[Any]												=					hidden_act
						lowerCAmelCase__					:    Tuple												=					hidden_dropout_prob
						lowerCAmelCase__					:    List[Any]												=					attention_probs_dropout_prob
						lowerCAmelCase__					:    int												=					initializer_range
						lowerCAmelCase__					:    Union[str, Any]												=					layer_norm_eps
						lowerCAmelCase__					:    List[str]												=					image_size
						lowerCAmelCase__					:    str												=					patch_size
						lowerCAmelCase__					:    Optional[int]												=					num_channels
						lowerCAmelCase__					:    int												=					qkv_bias

