



"""simple docstring"""







from __future__ import annotations

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

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

import requests






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			def __init__(      self						,	SCREAMING_SNAKE_CASE__		)      ->	None:
				super().__init__()
				A__											=					[]
				A__											=					domain







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	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:
								A__											=					parse.urljoin(self.domain						,	SCREAMING_SNAKE_CASE__		)
								self.urls.append(SCREAMING_SNAKE_CASE__		)



def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> str:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       str     )	-> str:






	"""simple docstring"""





	return parse.urlparse(UpperCAmelCase_     ).netloc

def       _lowerCamelCase				(							UpperCAmelCase_ :       str = "https://github.com"     )	-> list[str]:






	"""simple docstring"""





	A__											=					get_domain_name(UpperCAmelCase_     )

	# Initialize the parser
	A__											=					Parser(UpperCAmelCase_     )

	try:
		# Open URL
		A__											=					requests.get(UpperCAmelCase_     )

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

		# Get links and loop through
		A__											=					set()
		for link in parser.urls:
			# open URL.
			# read = requests.get(link)
			try:
				A__											=					requests.get(UpperCAmelCase_     )
				# Get the valid email.
				A__											=					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__":
			UpperCamelCase     =							emails_from_url("""https://github.com""")
			print(f'{len(emails)} emails found:')
			print("""\n""".join(sorted(emails)))









"""simple docstring"""







import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase     =							logging.get_logger(__name__)

UpperCamelCase     =							{
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					List[str]							  =     "data2vec-audio"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=32						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__="gelu"						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 512, 512, 512)						,	SCREAMING_SNAKE_CASE__=(5, 2, 2, 2, 2, 2, 2)						,	SCREAMING_SNAKE_CASE__=(10, 3, 3, 3, 3, 2, 2)						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=19						,	SCREAMING_SNAKE_CASE__=5						,	SCREAMING_SNAKE_CASE__=0.0_5						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=10						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__="sum"						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=256						,	SCREAMING_SNAKE_CASE__=(512, 512, 512, 512, 1500)						,	SCREAMING_SNAKE_CASE__=(5, 3, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=(1, 2, 3, 1, 1)						,	SCREAMING_SNAKE_CASE__=512						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	Dict:
				super().__init__(**SCREAMING_SNAKE_CASE__						,	pad_token_id=SCREAMING_SNAKE_CASE__						,	bos_token_id=SCREAMING_SNAKE_CASE__						,	eos_token_id=SCREAMING_SNAKE_CASE__		)
				A__											=					hidden_size
				A__											=					feat_extract_activation
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					conv_bias
				A__											=					num_conv_pos_embeddings
				A__											=					num_conv_pos_embedding_groups
				A__											=					conv_pos_kernel_size
				A__											=					len(self.conv_dim		)
				A__											=					num_hidden_layers
				A__											=					intermediate_size
				A__											=					hidden_act
				A__											=					num_attention_heads
				A__											=					hidden_dropout
				A__											=					attention_dropout
				A__											=					activation_dropout
				A__											=					feat_proj_dropout
				A__											=					final_dropout
				A__											=					layerdrop
				A__											=					layer_norm_eps
				A__											=					initializer_range
				A__											=					vocab_size
				A__											=					use_weighted_layer_sum

				if (
				    (len(self.conv_stride		) != self.num_feat_extract_layers)
				    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
				    or (len(self.conv_dim		) != self.num_feat_extract_layers)
				):
					raise ValueError(
					    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
					    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
					    f""" {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,"""
					    f""" `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

				# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
				A__											=					mask_time_prob
				A__											=					mask_time_length
				A__											=					mask_time_min_masks
				A__											=					mask_feature_prob
				A__											=					mask_feature_length
				A__											=					mask_feature_min_masks

				# ctc loss
				A__											=					ctc_loss_reduction
				A__											=					ctc_zero_infinity

				# adapter
				A__											=					add_adapter
				A__											=					adapter_kernel_size
				A__											=					adapter_stride
				A__											=					num_adapter_layers
				A__											=					output_hidden_size or hidden_size

				# SequenceClassification-specific parameter. Feel free to ignore for other classes.
				A__											=					classifier_proj_size

				# XVector-specific parameters. Feel free to ignore for other classes.
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					list(SCREAMING_SNAKE_CASE__		)
				A__											=					xvector_output_dim







			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return math.prod(self.conv_stride		)




