
import argparse
import logging
import os
import sys

import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers

import transformers
from transformers import BartForConditionalGeneration, BartTokenizer


logging.basicConfig(
    format='''%(asctime)s | %(levelname)s | %(name)s |  [%(filename)s:%(lineno)d] %(message)s''',
    datefmt='''%Y-%m-%d %H:%M:%S''',
    level=os.environ.get('''LOGLEVEL''', '''INFO''').upper(),
    stream=sys.stdout,
)

lowercase   =	logging.getLogger(__name__)

lowercase   =	{'''facebook/bart-base''': BartForConditionalGeneration}
lowercase   =	{'''facebook/bart-base''': BartTokenizer}



def   __lowerCAmelCase    (				)	->     Dict:
							lowerCamelCase_					=       argparse.ArgumentParser(description="""Export Bart model + Beam Search to ONNX graph."""							)
							parser.add_argument(
							    """--validation_file"""      ,					type=UpperCAmelCase__      ,					default=UpperCAmelCase__      ,					help="""A csv or a json file containing the validation data."""							)
							parser.add_argument(
							    """--max_length"""      ,					type=UpperCAmelCase__      ,					default=5      ,					help="""The maximum total input sequence length after tokenization."""      ,					)
							parser.add_argument(
							    """--num_beams"""      ,					type=UpperCAmelCase__      ,					default=UpperCAmelCase__      ,					help=(
							        """Number of beams to use for evaluation. This argument will be """
							        """passed to ``model.generate``, which is used during ``evaluate`` and ``predict``."""
							    )      ,					)
							parser.add_argument(
							    """--model_name_or_path"""      ,					type=UpperCAmelCase__      ,					help="""Path to pretrained model or model identifier from huggingface.co/models."""      ,					required=UpperCAmelCase__      ,					)
							parser.add_argument(
							    """--config_name"""      ,					type=UpperCAmelCase__      ,					default=UpperCAmelCase__      ,					help="""Pretrained config name or path if not the same as model_name"""      ,					)
							parser.add_argument(
							    """--device"""      ,					type=UpperCAmelCase__      ,					default="""cpu"""      ,					help="""Device where the model will be run"""      ,					)
							parser.add_argument("""--output_file_path"""      ,					type=UpperCAmelCase__      ,					default=UpperCAmelCase__      ,					help="""Where to store the final ONNX file."""							)

							lowerCamelCase_					=       parser.parse_args()

							return args



def   __lowerCAmelCase    (				UpperCAmelCase__					:   Tuple      ,					UpperCAmelCase__					:   Any="cpu"							)	->     Optional[int]:
							lowerCamelCase_					=       model_dict[model_name].from_pretrained(UpperCAmelCase__							).to(UpperCAmelCase__							)
							lowerCamelCase_					=       tokenizer_dict[model_name].from_pretrained(UpperCAmelCase__							)

							if model_name in ["facebook/bart-base"]:
														lowerCamelCase_					=       0
														lowerCamelCase_					=       None
														lowerCamelCase_					=       0

							return huggingface_model, tokenizer



def   __lowerCAmelCase    (				UpperCAmelCase__					:   List[Any]      ,					UpperCAmelCase__					:   Optional[int]      ,					UpperCAmelCase__					:   int      ,					UpperCAmelCase__					:   Any      ,					UpperCAmelCase__					:   str							)	->     Optional[int]:
							model.eval()

							lowerCamelCase_					=       None
							lowerCamelCase_					=       torch.jit.script(BARTBeamSearchGenerator(UpperCAmelCase__							)							)

							with torch.no_grad():
														lowerCamelCase_					=       """My friends are cool but they eat too many carbs."""
														lowerCamelCase_					=       tokenizer([ARTICLE_TO_SUMMARIZE]      ,					max_length=1_0_2_4      ,					return_tensors="""pt"""							).to(model.device							)

														lowerCamelCase_					=       model.generate(
														    inputs["""input_ids"""]      ,					attention_mask=inputs["""attention_mask"""]      ,					num_beams=UpperCAmelCase__      ,					max_length=UpperCAmelCase__      ,					early_stopping=UpperCAmelCase__      ,					decoder_start_token_id=model.config.decoder_start_token_id      ,					)

														torch.onnx.export(
														    UpperCAmelCase__      ,					(
														        inputs["""input_ids"""],
														        inputs["""attention_mask"""],
														        num_beams,
														        max_length,
														        model.config.decoder_start_token_id,
														    )      ,					UpperCAmelCase__      ,					opset_version=1_4      ,					input_names=["""input_ids""", """attention_mask""", """num_beams""", """max_length""", """decoder_start_token_id"""]      ,					output_names=["""output_ids"""]      ,					dynamic_axes={
														        """input_ids""": {0: """batch""", 1: """seq"""},
														        """output_ids""": {0: """batch""", 1: """seq_out"""},
														    }      ,					example_outputs=UpperCAmelCase__      ,					)

														logger.info("""Model exported to {}""".format(UpperCAmelCase__							)							)

														lowerCamelCase_					=       remove_dup_initializers(os.path.abspath(UpperCAmelCase__							)							)

														logger.info("""Deduplicated and optimized model written to {}""".format(UpperCAmelCase__							)							)

														lowerCamelCase_					=       onnxruntime.InferenceSession(UpperCAmelCase__							)
														lowerCamelCase_					=       ort_sess.run(
														    UpperCAmelCase__      ,					{
														        """input_ids""": inputs["""input_ids"""].cpu().numpy(),
														        """attention_mask""": inputs["""attention_mask"""].cpu().numpy(),
														        """num_beams""": np.array(UpperCAmelCase__							),
														        """max_length""": np.array(UpperCAmelCase__							),
														        """decoder_start_token_id""": np.array(model.config.decoder_start_token_id							),
														    }      ,					)

														np.testing.assert_allclose(summary_ids.cpu().numpy()      ,					ort_out[0]      ,					rtol=1e-3      ,					atol=1e-3							)

														logger.info("""Model outputs from torch and ONNX Runtime are similar."""							)
														logger.info("""Success."""							)



def   __lowerCAmelCase    (				)	->     int:
							lowerCamelCase_					=       parse_args()
							lowerCamelCase_					=       5
							lowerCamelCase_					=       4

							# Make one log on every process with the configuration for debugging.
							logging.basicConfig(
							    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,					datefmt="""%m/%d/%Y %H:%M:%S"""      ,					level=logging.INFO      ,					)

							logger.setLevel(logging.INFO							)
							transformers.utils.logging.set_verbosity_error()

							lowerCamelCase_					=       torch.device(args.device							)

							lowerCamelCase_	, lowerCamelCase_					=       load_model_tokenizer(args.model_name_or_path      ,					UpperCAmelCase__							)

							if model.config.decoder_start_token_id is None:
														raise ValueError("""Make sure that `config.decoder_start_token_id` is correctly defined"""							)

							model.to(UpperCAmelCase__							)

							if args.max_length:
														lowerCamelCase_					=       args.max_length

							if args.num_beams:
														lowerCamelCase_					=       args.num_beams

							if args.output_file_path:
														lowerCamelCase_					=       args.output_file_path
							else:
														lowerCamelCase_					=       """BART.onnx"""

							logger.info("""Exporting model to ONNX"""							)
							export_and_validate_model(UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__      ,					UpperCAmelCase__							)


if __name__ == "__main__":
		main()







from __future__ import annotations

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

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

import requests





class    __A(					UpperCAmelCase							):

					def __init__(						self       :  Tuple ,       __UpperCamelCase       :  str ):
												super().__init__()
												lowerCamelCase_					=       []
												lowerCamelCase_					=       domain



					def  lowercase__   (						self       :  Any ,       __UpperCamelCase       :  str ,       __UpperCamelCase       :  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:
																																								lowerCamelCase_					=       parse.urljoin(self.domain ,       __UpperCamelCase )
																																								self.urls.append(__UpperCamelCase )



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



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str							)	->     str:
							return parse.urlparse(UpperCAmelCase__							).netloc



def   __lowerCAmelCase    (				UpperCAmelCase__					:   str = "https://github.com"							)	->     list[str]:
							lowerCamelCase_					=       get_domain_name(UpperCAmelCase__							)

							# Initialize the parser
							lowerCamelCase_					=       Parser(UpperCAmelCase__							)

							try:
														# Open URL
														lowerCamelCase_					=       requests.get(UpperCAmelCase__							)

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

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





