
"""simple docstring"""







from __future__ import annotations

_SCREAMING_SNAKE_CASE	       =      """Muhammad Umer Farooq"""
_SCREAMING_SNAKE_CASE	       =      """MIT"""
_SCREAMING_SNAKE_CASE	       =      """1.0.0"""
_SCREAMING_SNAKE_CASE	       =      """Muhammad Umer Farooq"""
_SCREAMING_SNAKE_CASE	       =      """contact@muhammadumerfarooq.me"""
_SCREAMING_SNAKE_CASE	       =      """Alpha"""

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

import requests







class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							int							,						snake_case_   :							str    ):
												super().__init__()
												__snake_case     			=       []
												__snake_case     			=       domain





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							str							,						snake_case_   :							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:
																																								__snake_case     			=       parse.urljoin(self.domain							,						snake_case_    )
																																								self.urls.append(snake_case_    )




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


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







							"""simple docstring"""


							return parse.urlparse(SCREAMING_SNAKE_CASE ).netloc
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE = "https://github.com" )  ->     list[str]:







							"""simple docstring"""


							__snake_case     			=       get_domain_name(SCREAMING_SNAKE_CASE )

							# Initialize the parser
							__snake_case     			=       Parser(SCREAMING_SNAKE_CASE )

							try:
														# Open URL
														__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
														__snake_case     			=       set()
														for link in parser.urls:
																					# open URL.
																					# read = requests.get(link)
																					try:
																												__snake_case     			=       requests.get(SCREAMING_SNAKE_CASE )
																												# Get the valid email.
																												__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__":
		_SCREAMING_SNAKE_CASE	       =      emails_from_url("""https://github.com""")
		print(F"""{len(emails)} emails found:""")
		print("""\n""".join(sorted(emails)))


"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_SCREAMING_SNAKE_CASE	       =      {
    """configuration_mobilevit""": ["""MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MobileViTConfig""", """MobileViTOnnxConfig"""],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTFeatureExtractor"""]
		_SCREAMING_SNAKE_CASE	       =      ["""MobileViTImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """MobileViTForImageClassification""",
		    """MobileViTForSemanticSegmentation""",
		    """MobileViTModel""",
		    """MobileViTPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE	       =      [
		    """TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFMobileViTForImageClassification""",
		    """TFMobileViTForSemanticSegmentation""",
		    """TFMobileViTModel""",
		    """TFMobileViTPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_mobilevit import MobileViTFeatureExtractor
				from .image_processing_mobilevit import MobileViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mobilevit import (
				    MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MobileViTForImageClassification,
				    MobileViTForSemanticSegmentation,
				    MobileViTModel,
				    MobileViTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mobilevit import (
				    TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFMobileViTForImageClassification,
				    TFMobileViTForSemanticSegmentation,
				    TFMobileViTModel,
				    TFMobileViTPreTrainedModel,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE	       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
