
from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
		import torch

		from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

lowercase   =	logging.get_logger(__name__)





@add_end_docstrings(UpperCAmelCase							)
class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Tuple ,       **__UpperCamelCase       :  Union[str, Any] ):
												super().__init__(**__UpperCamelCase )
												requires_backends(self ,       """vision""" )
												requires_backends(self ,       """torch""" )

												if self.framework != "pt":
																			raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )

												self.check_model_type(__UpperCamelCase )

					def  lowercase__   (						self       :  Dict ,       **__UpperCamelCase       :  Optional[int] ):
												lowerCamelCase_					=       {}
												lowerCamelCase_					=       {}
												lowerCamelCase_					=       {}
												# preprocess args
												if "points_per_batch" in kwargs:
																			lowerCamelCase_					=       kwargs["""points_per_batch"""]
												if "points_per_crop" in kwargs:
																			lowerCamelCase_					=       kwargs["""points_per_crop"""]
												if "crops_n_layers" in kwargs:
																			lowerCamelCase_					=       kwargs["""crops_n_layers"""]
												if "crop_overlap_ratio" in kwargs:
																			lowerCamelCase_					=       kwargs["""crop_overlap_ratio"""]
												if "crop_n_points_downscale_factor" in kwargs:
																			lowerCamelCase_					=       kwargs["""crop_n_points_downscale_factor"""]
												# postprocess args
												if "pred_iou_thresh" in kwargs:
																			lowerCamelCase_					=       kwargs["""pred_iou_thresh"""]
												if "stability_score_offset" in kwargs:
																			lowerCamelCase_					=       kwargs["""stability_score_offset"""]
												if "mask_threshold" in kwargs:
																			lowerCamelCase_					=       kwargs["""mask_threshold"""]
												if "stability_score_thresh" in kwargs:
																			lowerCamelCase_					=       kwargs["""stability_score_thresh"""]
												if "crops_nms_thresh" in kwargs:
																			lowerCamelCase_					=       kwargs["""crops_nms_thresh"""]
												if "output_rle_mask" in kwargs:
																			lowerCamelCase_					=       kwargs["""output_rle_mask"""]
												if "output_bboxes_mask" in kwargs:
																			lowerCamelCase_					=       kwargs["""output_bboxes_mask"""]
												return preprocess_kwargs, forward_params, postprocess_kwargs

					def __call__(						self       :  str ,       __UpperCamelCase       :  Union[str, Any] ,       *__UpperCamelCase       :  str ,       __UpperCamelCase       :  List[Any]=None ,       __UpperCamelCase       :  List[str]=None ,       **__UpperCamelCase       :  Optional[Any] ):
												return super().__call__(__UpperCamelCase ,       *__UpperCamelCase ,       num_workers=__UpperCamelCase ,       batch_size=__UpperCamelCase ,       **__UpperCamelCase )

					def  lowercase__   (						self       :  List[Any] ,       __UpperCamelCase       :  Tuple ,       __UpperCamelCase       :  Tuple=6_4 ,       __UpperCamelCase       :  int = 0 ,       __UpperCamelCase       :  float = 5_1_2 / 1_5_0_0 ,       __UpperCamelCase       :  Optional[int] = 3_2 ,       __UpperCamelCase       :  Optional[int] = 1 ,       ):
												lowerCamelCase_					=       load_image(__UpperCamelCase )
												lowerCamelCase_					=       self.image_processor.size["""longest_edge"""]
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self.image_processor.generate_crop_boxes(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )
												lowerCamelCase_					=       self.image_processor(images=__UpperCamelCase ,       return_tensors="""pt""" )

												with self.device_placement():
																			if self.framework == "pt":
																										lowerCamelCase_					=       self.get_inference_context()
																										with inference_context():
																																	lowerCamelCase_					=       self._ensure_tensor_on_device(__UpperCamelCase ,       device=self.device )
																																	lowerCamelCase_					=       self.model.get_image_embeddings(model_inputs.pop("""pixel_values""" ) )
																																	lowerCamelCase_					=       image_embeddings

												lowerCamelCase_					=       grid_points.shape[1]
												lowerCamelCase_					=       points_per_batch if points_per_batch is not None else n_points

												if points_per_batch <= 0:
																			raise ValueError(
																			    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
																			    """To return all points at once, set points_per_batch to None""" )

												for i in range(0 ,       __UpperCamelCase ,       __UpperCamelCase ):
																			lowerCamelCase_					=       grid_points[:, i : i + points_per_batch, :, :]
																			lowerCamelCase_					=       input_labels[:, i : i + points_per_batch]
																			lowerCamelCase_					=       i == n_points - points_per_batch
																			yield {
																			    "input_points": batched_points,
																			    "input_labels": labels,
																			    "input_boxes": crop_boxes,
																			    "is_last": is_last,
																			    **model_inputs,
																			}

					def  lowercase__   (						self       :  str ,       __UpperCamelCase       :  Optional[Any] ,       __UpperCamelCase       :  Optional[int]=0.88 ,       __UpperCamelCase       :  List[str]=0.95 ,       __UpperCamelCase       :  str=0 ,       __UpperCamelCase       :  Any=1 ,       ):
												lowerCamelCase_					=       model_inputs.pop("""input_boxes""" )
												lowerCamelCase_					=       model_inputs.pop("""is_last""" )
												lowerCamelCase_					=       model_inputs.pop("""original_sizes""" ).tolist()
												lowerCamelCase_					=       model_inputs.pop("""reshaped_input_sizes""" ).tolist()

												lowerCamelCase_					=       self.model(**__UpperCamelCase )

												# post processing happens here in order to avoid CPU GPU copies of ALL the masks
												lowerCamelCase_					=       model_outputs["""pred_masks"""]
												lowerCamelCase_					=       self.image_processor.post_process_masks(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       binarize=__UpperCamelCase )
												lowerCamelCase_					=       model_outputs["""iou_scores"""]
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self.image_processor.filter_masks(
												    masks[0] ,       iou_scores[0] ,       original_sizes[0] ,       input_boxes[0] ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       )
												return {
												    "masks": masks,
												    "is_last": is_last,
												    "boxes": boxes,
												    "iou_scores": iou_scores,
												}



					def  lowercase__   (						self       :  Optional[Any] ,       __UpperCamelCase       :  Union[str, Any] ,       __UpperCamelCase       :  Union[str, Any]=False ,       __UpperCamelCase       :  Optional[int]=False ,       __UpperCamelCase       :  str=0.7 ,       ):
												lowerCamelCase_					=       []
												lowerCamelCase_					=       []
												lowerCamelCase_					=       []
												for model_output in model_outputs:
																			all_scores.append(model_output.pop("""iou_scores""" ) )
																			all_masks.extend(model_output.pop("""masks""" ) )
																			all_boxes.append(model_output.pop("""boxes""" ) )

												lowerCamelCase_					=       torch.cat(__UpperCamelCase )
												lowerCamelCase_					=       torch.cat(__UpperCamelCase )
												lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_	, lowerCamelCase_					=       self.image_processor.post_process_for_mask_generation(
												    __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase ,       __UpperCamelCase )

												lowerCamelCase_					=       defaultdict(__UpperCamelCase )
												for output in model_outputs:
																			for k, v in output.items():
																										extra[k].append(__UpperCamelCase )

												lowerCamelCase_					=       {}
												if output_rle_mask:
																			lowerCamelCase_					=       rle_mask

												if output_bboxes_mask:
																			lowerCamelCase_					=       bounding_boxes

												return {"masks": output_masks, "scores": iou_scores, **optional, **extra}







from __future__ import annotations

lowercase   =	'''Muhammad Umer Farooq'''
lowercase   =	'''MIT'''
lowercase   =	'''1.0.0'''
lowercase   =	'''Muhammad Umer Farooq'''
lowercase   =	'''contact@muhammadumerfarooq.me'''
lowercase   =	'''Alpha'''

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

import requests





class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  str ):
												super().__init__()
												lowerCamelCase_					=       []
												lowerCamelCase_					=       domain



					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  list[tuple[str, str | None]] ):
												# 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:
																																								lowerCamelCase_					=       parse.urljoin(self.domain ,       __UpperCamelCase )
																																								self.urls.append(__UpperCamelCase )



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							return ".".join(get_sub_domain_name(UpperCAmelCase__							).split("""."""							)[-2:]							)



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							return parse.urlparse(UpperCAmelCase__							).netloc



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "https://github.com"							)	->     list[str]:
							lowerCamelCase_					=       get_domain_name(UpperCAmelCase__							)

							# Initialize the parser
							lowerCamelCase_					=       Parser(UpperCAmelCase__							)

							try:
														# Open URL
														lowerCamelCase_					=       requests.get(UpperCAmelCase__							)

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

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

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


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





