


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 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor"]
						UpperCAmelCase             =						"SamImageProcessor"




						def __init__(       self					:      Tuple				,    _a					:      Tuple  )							->     Union[str, Any]:


										"""simple docstring"""





										super().__init__(_a  )
										_SCREAMING_SNAKE_CASE			  =self.image_processor
										_SCREAMING_SNAKE_CASE			  =-10
										_SCREAMING_SNAKE_CASE			  =self.image_processor.size['''longest_edge''']




						def __call__(       self					:      Union[str, Any]				,    _a					:      List[str]=None				,    _a					:      Any=None				,    _a					:      Any=None				,    _a					:      Optional[Any]=None				,    _a					:      Optional[Union[str, TensorType]] = None				,    **_a					:      int				,    )							->     BatchEncoding:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor(
										    _a				,    return_tensors=_a				,    **_a				,    )

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

										if hasattr(_a				,    '''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=_a				,    input_labels=_a				,    input_boxes=_a				,    )

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

										return encoding_image_processor




						def 							__UpperCamelCase				(       self					:      Union[str, Any]				,    _a					:      Dict				,    _a					:      List[str]				,    _a					:      List[str]=None				,    _a					:      Union[str, Any]=None				,    _a					:      Any=None				,    _a					:      List[Any]="pt"				,    )							->     Union[str, Any]:


										"""simple docstring"""





										if input_points is not None:
														if len(_a  ) != len(_a  ):
																		_SCREAMING_SNAKE_CASE			  =[
																		    self._normalize_coordinates(self.target_size				,    _a				,    original_sizes[0]  ) for point in input_points
																		]
														else:
																		_SCREAMING_SNAKE_CASE			  =[
																		    self._normalize_coordinates(self.target_size				,    _a				,    _a  )
																		    for point, original_size in zip(_a				,    _a  )
																		]
														# 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(_a				,    _a  )

														_SCREAMING_SNAKE_CASE			  =np.array(_a  )

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

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

										if input_boxes is not None:
														if return_tensors == "pt":
																		_SCREAMING_SNAKE_CASE			  =torch.from_numpy(_a  )
																		# 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(_a  )
																		# boxes batch size of 1 by default
																		_SCREAMING_SNAKE_CASE			  =tf.expand_dims(_a				,    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(_a  )
																		# 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(_a  )
																		# point batch size of 1 by default
																		_SCREAMING_SNAKE_CASE			  =tf.expand_dims(_a				,    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(_a  )
																		# 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(_a  )
																		# point batch size of 1 by default
																		_SCREAMING_SNAKE_CASE			  =tf.expand_dims(_a				,    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					:      List[Any]				,    _a					:      str				,    _a					:      Tuple  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =max([point.shape[0] for point in input_points]  )
										_SCREAMING_SNAKE_CASE			  =[]
										for i, point in enumerate(_a  ):
														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(_a  )
										_SCREAMING_SNAKE_CASE			  =processed_input_points
										return input_points, input_labels




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    _a					:      int				,    _a					:      np.ndarray				,    _a					:      str				,    _a					:      Union[str, Any]=False  )							->     np.ndarray:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =original_size
										_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =self.image_processor._get_preprocess_shape(_a				,    longest_edge=_a  )
										_SCREAMING_SNAKE_CASE			  =deepcopy(_a  ).astype(_a  )

										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 							__UpperCamelCase				(       self					:      Optional[Any]				,    _a					:      Tuple=None				,    _a					:      Dict=None				,    _a					:      Tuple=None				,    )							->     Any:


										"""simple docstring"""





										if input_points is not None:
														if hasattr(_a				,    '''numpy'''  ):  # Checks for TF or Torch tensor
																		_SCREAMING_SNAKE_CASE			  =input_points.numpy().tolist()

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

										if input_labels is not None:
														if hasattr(_a				,    '''numpy'''  ):
																		_SCREAMING_SNAKE_CASE			  =input_labels.numpy().tolist()

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

										if input_boxes is not None:
														if hasattr(_a				,    '''numpy'''  ):
																		_SCREAMING_SNAKE_CASE			  =input_boxes.numpy().tolist()

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

										return input_points, input_labels, input_boxes




						@property
						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.image_processor.model_input_names
										return list(dict.fromkeys(_a  )  )




						def 							__UpperCamelCase				(       self					:      Optional[int]				,    *_a					:      Dict				,    **_a					:      Dict  )							->     Optional[int]:


										"""simple docstring"""





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



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


snake_case_						:			int													=							logging.get_logger(__name__)

snake_case_						:			Optional[Any]													=							{
    '''SCUT-DLVCLab/lilt-roberta-en-base''': (
        '''https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'''
    ),
}

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						"lilt"




						def __init__(       self					:      int				,    _a					:      Dict=3_0522				,    _a					:      str=768				,    _a					:      Optional[int]=12				,    _a					:      str=12				,    _a					:      List[str]=3072				,    _a					:      Any="gelu"				,    _a					:      int=0.1				,    _a					:      Any=0.1				,    _a					:      Union[str, Any]=512				,    _a					:      str=2				,    _a					:      List[Any]=0.02				,    _a					:      str=1E-12				,    _a					:      str=0				,    _a					:      str="absolute"				,    _a					:      Any=None				,    _a					:      Dict=4				,    _a					:      int=1024				,    **_a					:      List[Any]				,    )							->     Any:


										"""simple docstring"""





										super().__init__(pad_token_id=_a				,    **_a  )

										_SCREAMING_SNAKE_CASE			  =vocab_size
										_SCREAMING_SNAKE_CASE			  =hidden_size
										_SCREAMING_SNAKE_CASE			  =num_hidden_layers
										_SCREAMING_SNAKE_CASE			  =num_attention_heads
										_SCREAMING_SNAKE_CASE			  =hidden_act
										_SCREAMING_SNAKE_CASE			  =intermediate_size
										_SCREAMING_SNAKE_CASE			  =hidden_dropout_prob
										_SCREAMING_SNAKE_CASE			  =attention_probs_dropout_prob
										_SCREAMING_SNAKE_CASE			  =max_position_embeddings
										_SCREAMING_SNAKE_CASE			  =type_vocab_size
										_SCREAMING_SNAKE_CASE			  =initializer_range
										_SCREAMING_SNAKE_CASE			  =layer_norm_eps
										_SCREAMING_SNAKE_CASE			  =position_embedding_type
										_SCREAMING_SNAKE_CASE			  =classifier_dropout
										_SCREAMING_SNAKE_CASE			  =channel_shrink_ratio
										_SCREAMING_SNAKE_CASE			  =max_ad_position_embeddings