





'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
      import jax.numpy as jnp

if is_tf_available():
      import tensorflow as tf

if is_torch_available():
      import torch


class 					a_     (				unittest.TestCase       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			{
            """task_specific_params""": {
                """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
                """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
                """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
            }
        }
        _SCREAMING_SNAKE_CASE									=			{
            """task_specific_params.summarization.length_penalty""": 1.0,
            """task_specific_params.summarization.max_length""": 128,
            """task_specific_params.summarization.min_length""": 12,
            """task_specific_params.summarization.num_beams""": 4,
            """task_specific_params.summarization_cnn.length_penalty""": 2.0,
            """task_specific_params.summarization_cnn.max_length""": 142,
            """task_specific_params.summarization_cnn.min_length""": 56,
            """task_specific_params.summarization_cnn.num_beams""": 4,
            """task_specific_params.summarization_xsum.length_penalty""": 1.0,
            """task_specific_params.summarization_xsum.max_length""": 62,
            """task_specific_params.summarization_xsum.min_length""": 11,
            """task_specific_params.summarization_xsum.num_beams""": 6,
        }

        self.assertEqual(flatten_dict(A       ) ,		A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(transpose(A       ) ,		x.transpose()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		x.transpose((1, 2, 0)       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		transpose(A ,		axes=(1, 2, 0)       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		transpose(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		transpose(A ,		axes=(1, 2, 0)       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(transpose(A       ) ,		np.asarray(transpose(A       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(transpose(A ,		axes=(1, 2, 0)       ) ,		np.asarray(transpose(A ,		axes=(1, 2, 0)       )       )       )       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.reshape(A ,		(4, 3)       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		np.reshape(A ,		(12, 5)       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		reshape(A ,		(12, 5)       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		reshape(A ,		(4, 3)       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		reshape(A ,		(12, 5)       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(reshape(A ,		(4, 3)       ) ,		np.asarray(reshape(A ,		(4, 3)       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(reshape(A ,		(12, 5)       ) ,		np.asarray(reshape(A ,		(12, 5)       )       )       )       )


    def 			snake_case_(		self       )		->   Any:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.squeeze(A       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		np.squeeze(A ,		axis=2       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		squeeze(A ,		axis=2       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		squeeze(A       ).numpy()       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		squeeze(A ,		axis=2       ).numpy()       )       )


    @require_flax
    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(squeeze(A       ) ,		np.asarray(squeeze(A       )       )       )       )

        _SCREAMING_SNAKE_CASE									=			np.random.randn(1 ,		4 ,		1 ,		5       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(squeeze(A ,		axis=2       ) ,		np.asarray(squeeze(A ,		axis=2       )       )       )       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.expand_dims(A ,		axis=1       )       )       )


    @require_torch
    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			torch.tensor(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )


    @require_tf
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			tf.constant(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		expand_dims(A ,		axis=1       ).numpy()       )       )





    @require_flax
    def 			snake_case_(		self       )		->   Dict:
        _SCREAMING_SNAKE_CASE									=			np.random.randn(3 ,		4       )
        _SCREAMING_SNAKE_CASE									=			jnp.array(A       )
        self.assertTrue(np.allclose(expand_dims(A ,		axis=1       ) ,		np.asarray(expand_dims(A ,		axis=1       )       )       )       )







'''simple docstring'''


from __future__ import annotations

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

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

import requests






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def __init__(						self     :   Any		,     lowerCamelCase__     :   str       )      ->None:



					'''simple docstring'''







					super().__init__()
					_UpperCAmelCase						:				list[str]	    =					[]
					_UpperCAmelCase						:				List[str]	    =					domain






			def 							lowerCAmelCase__			(						self     :   Optional[Any]		,     lowerCamelCase__     :   str		,     lowerCamelCase__     :   list[tuple[str, str | None]]       )      ->None:



					'''simple docstring'''







					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:
													_UpperCAmelCase						:				Dict	    =					parse.urljoin(self.domain		,     lowerCamelCase__       )
													self.urls.append(lowerCamelCase__       )






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






def        __lowerCAmelCase       (__lowerCAmelCase      ):
		return parse.urlparse(__lowerCAmelCase      ).netloc






def        __lowerCAmelCase       (__lowerCAmelCase = "https://github.com"      ):
		_UpperCAmelCase						:				List[str]	    =					get_domain_name(__lowerCAmelCase      )

		# Initialize the parser
		_UpperCAmelCase						:				int	    =					Parser(__lowerCAmelCase      )

		try:
				# Open URL
				_UpperCAmelCase						:				Any	    =					requests.get(__lowerCAmelCase      )

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

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

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


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



