
from __future__ import annotations

lowercase  :					List[Any]						    =							"""Muhammad Umer Farooq"""
lowercase  :					Tuple						    =							"""MIT"""
lowercase  :					List[str]						    =							"""1.0.0"""
lowercase  :					Any						    =							"""Muhammad Umer Farooq"""
lowercase  :					Optional[Any]						    =							"""contact@muhammadumerfarooq.me"""
lowercase  :					Optional[Any]						    =							"""Alpha"""

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

import requests

class __snake_case		(      UpperCAmelCase_     ):
    def __init__(  self		,snake_case      ):



           '''simple docstring'''







           super().__init__()
           lowercase							:     list[str]       	=      []
           lowercase							:     List[Any]       	=      domain
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case      ):



           '''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:
                                       lowercase							:     str       	=      parse.urljoin(self.domain		,lowerCAmelCase__      )
                                       self.urls.append(lowerCAmelCase__      )







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:
       return ".".join(get_sub_domain_name(_UpperCAmelCase				).split("""."""				)[-2:]				)







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return parse.urlparse(_UpperCAmelCase				).netloc







def 						_snake_case(  SCREAMING_SNAKE_CASE__ = "https://github.com"				) ->    Optional[int]:
       lowercase							:     Optional[int]       	=      get_domain_name(_UpperCAmelCase				)

       # Initialize the parser
       lowercase							:     Any       	=      Parser(_UpperCAmelCase				)

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

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

              # Get links and loop through
              lowercase							:     Dict       	=      set()
              for link in parser.urls:
                     # open URL.
                     # read = requests.get(link)
                     try:
                            lowercase							:     Optional[Any]       	=      requests.get(_UpperCAmelCase				)
                            # Get the valid email.
                            lowercase							:     Dict       	=      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  :					List[Any]						    =							emails_from_url("""https://github.com""")
  print(F'''{len(emails)} emails found:''')
  print("""\n""".join(sorted(emails)))



import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


lowerCAmelCase							:  Union[str, Any]   	=						{
    """text_branch""": """text_model""",
    """audio_branch""": """audio_model.audio_encoder""",
    """attn""": """attention.self""",
    """self.proj""": """output.dense""",
    """attention.self_mask""": """attn_mask""",
    """mlp.fc1""": """intermediate.dense""",
    """mlp.fc2""": """output.dense""",
    """norm1""": """layernorm_before""",
    """norm2""": """layernorm_after""",
    """bn0""": """batch_norm""",
}

lowerCAmelCase							:  int   	=						AutoFeatureExtractor.from_pretrained("""laion/clap-htsat-unfused""", truncation="""rand_trunc""")







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       create_model(
						    "HTSAT-tiny"				,	"roberta"				,	_UpperCAmelCase				,	precision="fp32"				,	device="cuda:0" if torch.cuda.is_available() else "cpu"				,	enable_fusion=_UpperCAmelCase				,	fusion_type="aff_2d" if enable_fusion else None				,	)
						return model, model_cfg







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {}

						SCREAMING_SNAKE_CASE_: Tuple  		=       R".*sequential.(\d+).*"
						SCREAMING_SNAKE_CASE_: Dict  		=       R".*_projection.(\d+).*"

						for key, value in state_dict.items():
												# check if any key needs to be modified
												for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
																		if key_to_modify in key:
																								SCREAMING_SNAKE_CASE_: Any  		=       key.replace(_UpperCAmelCase				,	_UpperCAmelCase    )

												if re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		# replace sequential layers with list
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"sequential.{sequential_layer}."				,	f"layers.{int(_UpperCAmelCase    )//3}.linear."    )
												elif re.match(_UpperCAmelCase				,	_UpperCAmelCase    ):
																		SCREAMING_SNAKE_CASE_: Any  		=       int(re.match(_UpperCAmelCase				,	_UpperCAmelCase    ).group(1    )    )

																		# Because in CLAP they use `nn.Sequential`...
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       1 if projecton_layer == 0 else 2

																		SCREAMING_SNAKE_CASE_: Dict  		=       key.replace(f"_projection.{projecton_layer}."				,	f"_projection.linear{transformers_projection_layer}."    )

												if "audio" and "qkv" in key:
																		# split qkv into query key and value
																		SCREAMING_SNAKE_CASE_: Tuple  		=       value
																		SCREAMING_SNAKE_CASE_: List[str]  		=       mixed_qkv.size(0    ) // 3

																		SCREAMING_SNAKE_CASE_: Any  		=       mixed_qkv[:qkv_dim]
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       mixed_qkv[qkv_dim : qkv_dim * 2]
																		SCREAMING_SNAKE_CASE_: Optional[Any]  		=       mixed_qkv[qkv_dim * 2 :]

																		SCREAMING_SNAKE_CASE_: str  		=       query_layer
																		SCREAMING_SNAKE_CASE_: int  		=       key_layer
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       value_layer
												else:
																		SCREAMING_SNAKE_CASE_: int  		=       value

						return model_state_dict







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=False    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       init_clap(_UpperCAmelCase				,	enable_fusion=_UpperCAmelCase    )

						clap_model.eval()
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       clap_model.state_dict()
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       rename_state_dict(_UpperCAmelCase    )

						SCREAMING_SNAKE_CASE_: List[str]  		=       ClapConfig()
						SCREAMING_SNAKE_CASE_: Tuple  		=       enable_fusion
						SCREAMING_SNAKE_CASE_: Tuple  		=       ClapModel(_UpperCAmelCase    )

						# ignore the spectrogram embedding layer
						model.load_state_dict(_UpperCAmelCase				,	strict=_UpperCAmelCase    )

						model.save_pretrained(_UpperCAmelCase    )
						transformers_config.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  Tuple   	=						argparse.ArgumentParser()
						parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
						parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
						parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
						parser.add_argument("""--enable_fusion""", action="""store_true""", help="""Whether to enable fusion or not""")
						lowerCAmelCase							:  int   	=						parser.parse_args()

						convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)






