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					(    _A    ):
						lowercase					 =     ['image_processor']
						lowercase					 =     'SamImageProcessor'

						def __init__(	self				:      Optional[Any]							,							__lowerCamelCase				:      List[str]   )							->		Optional[int]:


									'''simple docstring'''

									super().__init__(__lowerCamelCase   )
									lowercase							=		self.image_processor
									lowercase							=		-10
									lowercase							=		self.image_processor.size['''longest_edge''']

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


									'''simple docstring'''

									lowercase							=		self.image_processor(
									    __lowerCamelCase							,							return_tensors=__lowerCamelCase							,							**__lowerCamelCase							,							)

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

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

									lowercase		,lowercase		,lowercase							=		self._check_and_preprocess_points(
									    input_points=__lowerCamelCase							,							input_labels=__lowerCamelCase							,							input_boxes=__lowerCamelCase							,							)

									lowercase							=		self._normalize_and_convert(
									    __lowerCamelCase							,							__lowerCamelCase							,							input_points=__lowerCamelCase							,							input_labels=__lowerCamelCase							,							input_boxes=__lowerCamelCase							,							return_tensors=__lowerCamelCase							,							)

									return encoding_image_processor

						def        __a		(	self				:      Union[str, Any]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      str=None							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      int="pt"							,							)							->		Optional[int]:


									'''simple docstring'''

									if input_points is not None:
												if len(__lowerCamelCase   ) != len(__lowerCamelCase   ):
															lowercase							=		[
															    self._normalize_coordinates(self.target_size							,							__lowerCamelCase							,							original_sizes[0]   ) for point in input_points
															]
												else:
															lowercase							=		[
															    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:
																		lowercase		,lowercase							=		self._pad_points_and_labels(__lowerCamelCase							,							__lowerCamelCase   )

												lowercase							=		np.array(__lowerCamelCase   )

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

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

									if input_boxes is not None:
												if return_tensors == "pt":
															lowercase							=		torch.from_numpy(__lowerCamelCase   )
															# boxes batch size of 1 by default
															lowercase							=		input_boxes.unsqueeze(1   ) if len(input_boxes.shape   ) != 3 else input_boxes
												elif return_tensors == "tf":
															lowercase							=		tf.convert_to_tensor(__lowerCamelCase   )
															# boxes batch size of 1 by default
															lowercase							=		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":
															lowercase							=		torch.from_numpy(__lowerCamelCase   )
															# point batch size of 1 by default
															lowercase							=		input_points.unsqueeze(1   ) if len(input_points.shape   ) != 4 else input_points
												elif return_tensors == "tf":
															lowercase							=		tf.convert_to_tensor(__lowerCamelCase   )
															# point batch size of 1 by default
															lowercase							=		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":
															lowercase							=		torch.from_numpy(__lowerCamelCase   )
															# point batch size of 1 by default
															lowercase							=		input_labels.unsqueeze(1   ) if len(input_labels.shape   ) != 3 else input_labels
												elif return_tensors == "tf":
															lowercase							=		tf.convert_to_tensor(__lowerCamelCase   )
															# point batch size of 1 by default
															lowercase							=		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        __a		(	self				:      Dict							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Optional[int]   )							->		Optional[int]:


									'''simple docstring'''

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

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      List[Any]							,							__lowerCamelCase				:      str=False   )							->		np.ndarray:


									'''simple docstring'''

									lowercase		,lowercase							=		original_size
									lowercase		,lowercase							=		self.image_processor._get_preprocess_shape(__lowerCamelCase							,							longest_edge=__lowerCamelCase   )
									lowercase							=		deepcopy(__lowerCamelCase   ).astype(__lowerCamelCase   )

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

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

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

									return coords

						def        __a		(	self				:      Optional[Any]							,							__lowerCamelCase				:      Optional[int]=None							,							__lowerCamelCase				:      Union[str, Any]=None							,							__lowerCamelCase				:      Optional[Any]=None							,							)							->		Dict:


									'''simple docstring'''

									if input_points is not None:
												if hasattr(__lowerCamelCase							,							'''numpy'''   ):  # Checks for TF or Torch tensor
															lowercase							=		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.'''   )
												lowercase							=		[np.array(__lowerCamelCase   ) for input_point in input_points]
									else:
												lowercase							=		None

									if input_labels is not None:
												if hasattr(__lowerCamelCase							,							'''numpy'''   ):
															lowercase							=		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.'''   )
												lowercase							=		[np.array(__lowerCamelCase   ) for label in input_labels]
									else:
												lowercase							=		None

									if input_boxes is not None:
												if hasattr(__lowerCamelCase							,							'''numpy'''   ):
															lowercase							=		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.'''   )
												lowercase							=		[np.array(__lowerCamelCase   ).astype(np.floataa   ) for box in input_boxes]
									else:
												lowercase							=		None

									return input_points, input_labels, input_boxes

						@property
						def        __a		(	self				:      Tuple   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.image_processor.model_input_names
									return list(dict.fromkeys(__lowerCamelCase   )   )



						def        __a		(	self				:      Union[str, Any]							,							*__lowerCamelCase				:      List[Any]							,							**__lowerCamelCase				:      Optional[int]   )							->		List[str]:


									'''simple docstring'''

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






from __future__ import annotations

from collections import deque






class      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      list[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									self.adlist.append(
									    {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}   )

									for keyword in keywords:
												self.add_keyword(__lowerCamelCase   )
									self.set_fail_transitions()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str   )							->		int | None:


									'''simple docstring'''

									for state in self.adlist[current_state]["next_states"]:
												if char == self.adlist[state]["value"]:
															return state
									return None

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str   )							->		None:


									'''simple docstring'''

									lowercase							=		0
									for character in keyword:
												lowercase							=		self.find_next_state(__lowerCamelCase							,							__lowerCamelCase   )
												if next_state is None:
															self.adlist.append(
															    {
															        '''value''': character,
															        '''next_states''': [],
															        '''fail_state''': 0,
															        '''output''': [],
															    }   )
															self.adlist[current_state]["next_states"].append(len(self.adlist   ) - 1   )
															lowercase							=		len(self.adlist   ) - 1
												else:
															lowercase							=		next_state
									self.adlist[current_state]["output"].append(__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		deque()
									for node in self.adlist[0]["next_states"]:
												q.append(__lowerCamelCase   )
												lowercase							=		0
									while q:
												lowercase							=		q.popleft()
												for child in self.adlist[r]["next_states"]:
															q.append(__lowerCamelCase   )
															lowercase							=		self.adlist[r]['''fail_state''']
															while (
															    self.find_next_state(__lowerCamelCase							,							self.adlist[child]['''value''']   ) is None
															    and state != 0
															):
																		lowercase							=		self.adlist[state]['''fail_state''']
															lowercase							=		self.find_next_state(
															    __lowerCamelCase							,							self.adlist[child]['''value''']   )
															if self.adlist[child]["fail_state"] is None:
																		lowercase							=		0
															lowercase							=		(
															    self.adlist[child]['''output''']
															    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
															)



						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str   )							->		dict[str, list[int]]:


									'''simple docstring'''

									lowercase							=		{}  # returns a dict with keywords and list of its occurrences
									lowercase							=		0
									for i in range(len(__lowerCamelCase   )   ):
												while (
												    self.find_next_state(__lowerCamelCase							,							string[i]   ) is None
												    and current_state != 0
												):
															lowercase							=		self.adlist[current_state]['''fail_state''']
												lowercase							=		self.find_next_state(__lowerCamelCase							,							string[i]   )
												if next_state is None:
															lowercase							=		0
												else:
															lowercase							=		next_state
															for key in self.adlist[current_state]["output"]:
																		if key not in result:
																					lowercase							=		[]
																		result[key].append(i - len(__lowerCamelCase   ) + 1   )
									return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()





