



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			Tuple    ):
				"""simple docstring"""







				__a       =		AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE    )
				__a       =		FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE    )
				__a       =		checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE    )

				__a       =		"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

				if config.model_type == "t5":
								__a       =		"""SelfAttention"""
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								__a       =		"""LocalSelfAttention"""
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__a       =		"""TransientGlobalSelfAttention"""
				else:
								raise ValueError(
								    """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
								    """ attribute with a value from ['local', 'transient-global]."""    )

				# Encoder
				for layer_index in range(config.num_layers    ):
								__a       =		f"layers_{str(_SCREAMING_SNAKE_CASE    )}"

								# Self-Attention
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

								# Layer Normalization
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

								if split_mlp_wi:
												__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
												__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
								else:
												__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

								# Layer Normalization
								__a       =		tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

								# Assigning
								__a       =		flax_model.params["""encoder"""]["""block"""][str(_SCREAMING_SNAKE_CASE    )]["""layer"""]
								__a       =		tax_attention_key
								__a       =		tax_attention_out
								__a       =		tax_attention_query
								__a       =		tax_attention_value

								__a       =		tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__a       =		tax_global_layer_norm

								if split_mlp_wi:
												__a       =		tax_mlp_wi_a
												__a       =		tax_mlp_wi_a
								else:
												__a       =		tax_mlp_wi

								__a       =		tax_mlp_wo
								__a       =		tax_mlp_layer_norm

								__a       =		flax_model_encoder_layer_block

				# Only for layer 0:
				__a       =		tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
				__a       =		tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__a       =		tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
								__a       =		tax_encoder_global_rel_embedding

				# Assigning
				__a       =		tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
				__a       =		tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers    ):
								__a       =		f"layers_{str(_SCREAMING_SNAKE_CASE    )}"

								# Self-Attention
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

								# Layer Normalization
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
								    """scale"""
								]

								# Encoder-Decoder-Attention
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
								__a       =		tax_enc_dec_attention_module["""key"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""out"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""query"""]["""kernel"""]
								__a       =		tax_enc_dec_attention_module["""value"""]["""kernel"""]

								# Layer Normalization
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

								# MLP
								if split_mlp_wi:
												__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
												__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
								else:
												__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

								# Layer Normalization
								__a       =		tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

								# Assigning
								__a       =		flax_model.params["""decoder"""]["""block"""][str(_SCREAMING_SNAKE_CASE    )]["""layer"""]
								__a       =		tax_attention_key
								__a       =		tax_attention_out
								__a       =		tax_attention_query
								__a       =		tax_attention_value

								__a       =		tax_pre_attention_layer_norm

								__a       =		tax_enc_dec_attention_key
								__a       =		tax_enc_dec_attention_out
								__a       =		tax_enc_dec_attention_query
								__a       =		tax_enc_dec_attention_value

								__a       =		tax_cross_layer_norm

								if split_mlp_wi:
												__a       =		tax_mlp_wi_a
												__a       =		tax_mlp_wi_a
								else:
												__a       =		tax_mlp_wi

								__a       =		tax_mlp_wo

								__a       =		txa_mlp_layer_norm

								__a       =		flax_model_decoder_layer_block

				# Decoder Normalization
				__a       =		tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
				__a       =		txa_decoder_norm

				# Only for layer 0:
				__a       =		tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
				__a       =		tax_decoder_rel_embedding

				# Token Embeddings
				__a       =		tax_model["""target"""]["""token_embedder"""]["""embedding"""]
				__a       =		txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								__a       =		tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

				flax_model.save_pretrained(_SCREAMING_SNAKE_CASE    )
				print("""T5X Model was sucessfully converted!"""    )


if __name__ == "__main__":
						lowerCamelCase__			= argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
						)
						parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
						parser.add_argument(
						    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
						)
						lowerCamelCase__			= parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





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    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		Any      ,				__lowercase		:		str	):

											'''simple docstring'''
											super().__init__()
											__a       =		[]
											__a       =		domain







							def 						UpperCamelCase_					(   self		:		Union[str, Any]      ,				__lowercase		:		str      ,				__lowercase		:		list[tuple[str, str | None]]	):

											'''simple docstring'''
											# 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      ,				__lowercase	)
																											self.urls.append(__lowercase	)



def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







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

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str    ):
				"""simple docstring"""







				return parse.urlparse(_SCREAMING_SNAKE_CASE    ).netloc

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			str = "https://github.com"    ):
				"""simple docstring"""







				__a       =		get_domain_name(_SCREAMING_SNAKE_CASE    )

				# Initialize the parser
				__a       =		Parser(_SCREAMING_SNAKE_CASE    )

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

								# 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(_SCREAMING_SNAKE_CASE    )
																# 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(_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__":
						lowerCamelCase__			= emails_from_url("""https://github.com""")
						print(F"""{len(emails)} emails found:""")
						print("""\n""".join(sorted(emails)))
