
'''simple docstring'''







from __future__ import annotations

_snake_case				:   Union[str, Any]     =				"""Muhammad Umer Farooq"""
_snake_case				:   Union[str, Any]     =				"""MIT"""
_snake_case				:   Dict     =				"""1.0.0"""
_snake_case				:   Union[str, Any]     =				"""Muhammad Umer Farooq"""
_snake_case				:   str     =				"""contact@muhammadumerfarooq.me"""
_snake_case				:   Optional[int]     =				"""Alpha"""

import re
from html.parser import HTMLParser
from urllib import parse

import requests


class 			lowerCAmelCase (				__UpperCAmelCase			):




  def __init__(				self					,						UpperCamelCase      ):
        super().__init__()
        _SCREAMING_SNAKE_CASE				= []
        _SCREAMING_SNAKE_CASE				= domain




  def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
        # Only parse the 'anchor' tag.
        if tag == "a":
              # Check the list of defined attributes.
              for name, value in attrs:
                    # If href is defined, and not empty nor # print it.
                    if name == "href" and value != "#" and value != "":
                          # If not already in urls.
                          if value not in self.urls:
                                _SCREAMING_SNAKE_CASE				= parse.urljoin(self.domain					,						UpperCamelCase      )
                                self.urls.append(UpperCamelCase      )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
      return ".".join(get_sub_domain_name(_SCREAMING_SNAKE_CASE  ).split("."  )[-2:]  )



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str  ):
      return parse.urlparse(_SCREAMING_SNAKE_CASE  ).netloc



def 			_a		(	_SCREAMING_SNAKE_CASE		:   str = "https://github.com"  ):
      _SCREAMING_SNAKE_CASE				= get_domain_name(_SCREAMING_SNAKE_CASE  )

      # Initialize the parser
      _SCREAMING_SNAKE_CASE				= Parser(_SCREAMING_SNAKE_CASE  )

      try:
            # Open URL
            _SCREAMING_SNAKE_CASE				= requests.get(_SCREAMING_SNAKE_CASE  )

            # pass the raw HTML to the parser to get links
            parser.feed(r.text  )

            # Get links and loop through
            _SCREAMING_SNAKE_CASE				= set()
            for link in parser.urls:
                  # open URL.
                  # read = requests.get(link)
                  try:
                        _SCREAMING_SNAKE_CASE				= requests.get(_SCREAMING_SNAKE_CASE  )
                        # Get the valid email.
                        _SCREAMING_SNAKE_CASE				= re.findall("[a-zA-Z0-9]+@" + domain	,			read.text  )
                        # If not in list then append it.
                        for email in emails:
                              valid_emails.add(_SCREAMING_SNAKE_CASE  )
                  except ValueError:
                        pass
      except ValueError:
            raise SystemExit(1  )

      # Finally return a sorted list of email addresses with no duplicates.
      return sorted(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
      _snake_case				:   int     =				emails_from_url("""https://github.com""")
      print(F"{len(emails)} emails found:")
      print("""\n""".join(sorted(emails)))

'''simple docstring'''







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			):
		a							:					Optional[int]       =					["""image_processor"""]
		a							:					Tuple       =					"""SamImageProcessor"""




		def __init__(				self					,						UpperCamelCase      ):
								super().__init__(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= self.image_processor
								_SCREAMING_SNAKE_CASE				= -10
								_SCREAMING_SNAKE_CASE				= self.image_processor.size["longest_edge"]




		def __call__(				self					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase=None					,						UpperCamelCase = None					,						**UpperCamelCase					,						):
								_SCREAMING_SNAKE_CASE				= self.image_processor(
								    UpperCamelCase					,						return_tensors=UpperCamelCase					,						**UpperCamelCase					,						)

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

								if hasattr(UpperCamelCase					,						"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=UpperCamelCase					,						input_labels=UpperCamelCase					,						input_boxes=UpperCamelCase					,						)

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

								return encoding_image_processor




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

														_SCREAMING_SNAKE_CASE				= np.array(UpperCamelCase      )

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

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

								if input_boxes is not None:
														if return_tensors == "pt":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# 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(UpperCamelCase      )
																				# boxes batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= 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":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# 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(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= 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":
																				_SCREAMING_SNAKE_CASE				= torch.from_numpy(UpperCamelCase      )
																				# 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(UpperCamelCase      )
																				# point batch size of 1 by default
																				_SCREAMING_SNAKE_CASE				= 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     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase      ):
								_SCREAMING_SNAKE_CASE				= max([point.shape[0] for point in input_points]      )
								_SCREAMING_SNAKE_CASE				= []
								for i, point in enumerate(UpperCamelCase      ):
														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(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= processed_input_points
								return input_points, input_labels




		def     lowercase       (				self					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase					,						UpperCamelCase=False      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= original_size
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= self.image_processor._get_preprocess_shape(UpperCamelCase					,						longest_edge=UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= deepcopy(UpperCamelCase      ).astype(UpperCamelCase      )

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

								if input_labels is not None:
														if hasattr(UpperCamelCase					,						"numpy"      ):
																				_SCREAMING_SNAKE_CASE				= 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."      )
														_SCREAMING_SNAKE_CASE				= [np.array(UpperCamelCase      ) for label in input_labels]
								else:
														_SCREAMING_SNAKE_CASE				= None

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

								return input_points, input_labels, input_boxes




		@property
		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processor.model_input_names
								return list(dict.fromkeys(UpperCamelCase      )      )




		def     lowercase       (				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								return self.image_processor.post_process_masks(*UpperCamelCase					,						**UpperCamelCase      )