

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 ):







    """simple docstring"""





    lowerCamelCase			=							['image_processor']
    lowerCamelCase			=							'SamImageProcessor'


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





        '''simple docstring'''







        super().__init__(lowercase_		)
        A__          =   self.image_processor
        A__          =   -1_0
        A__          =   self.image_processor.size['longest_edge']


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





        '''simple docstring'''







        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 						snake_case__     (      self :       List[str],lowercase_ :       Any,lowercase_ :       Any,lowercase_ :       List[str]=None,lowercase_ :       Dict=None,lowercase_ :       Optional[int]=None,lowercase_ :       Any="pt",)->    List[str]:





        '''simple docstring'''







        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 						snake_case__     (      self :       Dict,lowercase_ :       Any,lowercase_ :       Tuple		)->    int:





        '''simple docstring'''







        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 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       np.ndarray,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    np.ndarray:





        '''simple docstring'''







        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 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=None,lowercase_ :       str=None,lowercase_ :       Dict=None,)->    Union[str, Any]:





        '''simple docstring'''







        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 						snake_case__     (      self :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.image_processor.model_input_names
        return list(dict.fromkeys(lowercase_		)		)


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





        '''simple docstring'''







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




import os

# Precomputes a list of the 100 first triangular numbers
lowercase_       		=  [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def 						_snake_case(    )							->      int:





    '''simple docstring'''






    A__          =   os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__      )      )
    A__          =   os.path.join(SCREAMING_SNAKE_CASE__    ,			'words.txt'      )

    A__          =   ''
    with open(SCREAMING_SNAKE_CASE__      ) as f:
        A__          =   f.readline()

    A__          =   [word.strip('"'      ) for word in words.strip('\r\n'      ).split(','      )]
    A__          =   [
        word
        for word in [sum(ord(SCREAMING_SNAKE_CASE__      ) - 64 for x in word      ) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     print(solution())

