





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_							):


  '''simple docstring'''
  a_							:       Dict      					=["""image_processor"""]
  a_							:       Optional[Any]      					="""SamImageProcessor"""

  def __init__(      self      :					Tuple      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    super().__init__(UpperCamelCase							)
    _snake_case   :				Dict														=	self.image_processor
    _snake_case   :				str														=	-10
    _snake_case   :				int														=	self.image_processor.size['longest_edge']

  def __call__(      self      :					int      ,				UpperCamelCase      :					Any=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					Optional[Any]=None      ,				UpperCamelCase      :					List[Any]=None      ,				UpperCamelCase      :					Optional[Union[str, TensorType]] = None      ,				**UpperCamelCase      :					str      ,				):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.image_processor(
        UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				**UpperCamelCase      ,				)

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

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

    _snake_case   :				List[str]														=	self._check_and_preprocess_points(
        input_points=UpperCamelCase      ,				input_labels=UpperCamelCase      ,				input_boxes=UpperCamelCase      ,				)

    _snake_case   :				Optional[Any]														=	self._normalize_and_convert(
        UpperCamelCase      ,				UpperCamelCase      ,				input_points=UpperCamelCase      ,				input_labels=UpperCamelCase      ,				input_boxes=UpperCamelCase      ,				return_tensors=UpperCamelCase      ,				)

    return encoding_image_processor

  def UpperCamelCase_ (      self      :					Union[str, Any]      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					Tuple=None      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					Tuple=None      ,				UpperCamelCase      :					int="pt"      ,				):


    '''simple docstring'''





    if input_points is not None:
      if len(UpperCamelCase							) != len(UpperCamelCase							):
        _snake_case   :				Dict														=	[
            self._normalize_coordinates(self.target_size      ,				UpperCamelCase      ,				original_sizes[0]							) for point in input_points
        ]
      else:
        _snake_case   :				Dict														=	[
            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:
          _snake_case   :				Optional[Any]														=	self._pad_points_and_labels(UpperCamelCase      ,				UpperCamelCase							)

      _snake_case   :				Tuple														=	np.array(UpperCamelCase							)

    if input_labels is not None:
      _snake_case   :				Optional[int]														=	np.array(UpperCamelCase							)

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

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


    '''simple docstring'''





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

  def UpperCamelCase_ (      self      :					Optional[int]      ,				UpperCamelCase      :					int      ,				UpperCamelCase      :					np.ndarray      ,				UpperCamelCase      :					Dict      ,				UpperCamelCase      :					List[str]=False							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	original_size
    _snake_case   :				str														=	self.image_processor._get_preprocess_shape(UpperCamelCase      ,				longest_edge=UpperCamelCase							)
    _snake_case   :				Optional[Any]														=	deepcopy(UpperCamelCase							).astype(UpperCamelCase							)

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

    _snake_case   :				Union[str, Any]														=	coords[..., 0] * (new_w / old_w)
    _snake_case   :				Dict														=	coords[..., 1] * (new_h / old_h)

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

    return coords

  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					int=None      ,				):


    '''simple docstring'''





    if input_points is not None:
      if hasattr(UpperCamelCase      ,				'numpy'							):  # Checks for TF or Torch tensor
        _snake_case   :				Union[str, Any]														=	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.'							)
      _snake_case   :				Tuple														=	[np.array(UpperCamelCase							) for input_point in input_points]
    else:
      _snake_case   :				Optional[Any]														=	None

    if input_labels is not None:
      if hasattr(UpperCamelCase      ,				'numpy'							):
        _snake_case   :				Dict														=	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.'							)
      _snake_case   :				Optional[Any]														=	[np.array(UpperCamelCase							) for label in input_labels]
    else:
      _snake_case   :				Optional[Any]														=	None

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

    return input_points, input_labels, input_boxes

  @property
  def UpperCamelCase_ (      self      :					List[Any]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.image_processor.model_input_names
    return list(dict.fromkeys(UpperCamelCase							)							)




  def UpperCamelCase_ (      self      :					Optional[Any]      ,				*UpperCamelCase      :					Any      ,				**UpperCamelCase      :					Any							):


    '''simple docstring'''





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











from random import randint, random


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	bool = False    ,							lowerCAmelCase:	int = 5    ,							)->		list:
  _snake_case   :				Dict														=	[[-1] * number_of_cells]  # Create a highway without any car
  _snake_case   :				List[str]														=	0
  _snake_case   :				List[str]														=	max(lowerCAmelCase    ,							0				)
  while i < number_of_cells:
    _snake_case   :				Optional[Any]														=	(
        randint(0    ,							lowerCAmelCase				) if random_speed else initial_speed
    )  # Place the cars
    i += (
        randint(1    ,							max_speed * 2				) if random_frequency else frequency
    )  # Arbitrary number, may need tuning
  return highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int				)->		int:
  _snake_case   :				Dict														=	0
  _snake_case   :				Optional[Any]														=	highway_now[car_index + 1 :]
  for cell in range(len(lowerCAmelCase				)				):  # May need a better name for this
    if cells[cell] != -1:  # If the cell is not empty then
      return distance  # we have the distance we wanted
    distance += 1
  # Here if the car is near the end of the highway
  return distance + get_distance(lowerCAmelCase    ,							-1				)


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				List[Any]														=	len(lowerCAmelCase				)
  # Beforce calculations, the highway is empty
  _snake_case   :				List[Any]														=	[-1] * number_of_cells

  for car_index in range(lowerCAmelCase				):
    if highway_now[car_index] != -1:
      # Add 1 to the current speed of the car and cap the speed
      _snake_case   :				int														=	min(highway_now[car_index] + 1    ,							lowerCAmelCase				)
      # Number of empty cell before the next car
      _snake_case   :				Tuple														=	get_distance(lowerCAmelCase    ,							lowerCAmelCase				) - 1
      # We can't have the car causing an accident
      _snake_case   :				Union[str, Any]														=	min(next_highway[car_index]    ,							lowerCAmelCase				)
      if random() < probability:
        # Randomly, a driver will slow down
        _snake_case   :				int														=	max(next_highway[car_index] - 1    ,							0				)
  return next_highway


def     lowerCamelCase_	(      lowerCAmelCase:	list    ,							lowerCAmelCase:	int    ,							lowerCAmelCase:	float    ,							lowerCAmelCase:	int				)->		list:
  _snake_case   :				Dict														=	len(highway[0]				)

  for i in range(lowerCAmelCase				):
    _snake_case   :				Any														=	update(highway[i]    ,							lowerCAmelCase    ,							lowerCAmelCase				)
    _snake_case   :				Tuple														=	[-1] * number_of_cells

    for car_index in range(lowerCAmelCase				):
      _snake_case   :				Union[str, Any]														=	next_speeds_calculated[car_index]
      if speed != -1:
        # Change the position based on the speed (with % to create the loop)
        _snake_case   :				Union[str, Any]														=	(car_index + speed) % number_of_cells
        # Commit the change of position
        _snake_case   :				Tuple														=	speed
    highway.append(lowerCAmelCase				)

  return highway


if __name__ == "__main__":
      import doctest

      doctest.testmod()




