


'''simple docstring'''



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
      from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments



@require_torch
class      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		Union[str, Any]      )						->							List[Any]:
          for model_result in results.values():
               for batch_size, sequence_length in zip(model_result['''bs''']							,			model_result['''ss''']      ):
                    __UpperCAmelCase     :			List[str]  							=      model_result['''result'''][batch_size][sequence_length]
                    self.assertIsNotNone(snake_case      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							int:
          __UpperCAmelCase     :			str  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			Dict  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Union[str, Any]  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Dict:
          __UpperCAmelCase     :			Optional[Any]  							=      '''sgugger/tiny-distilbert-classification'''
          __UpperCAmelCase     :			Tuple  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			only_pretrain_model=snake_case							,			)
          __UpperCAmelCase     :			Any  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Tuple:
          __UpperCAmelCase     :			List[str]  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			Optional[int]  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			torchscript=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Union[str, Any]  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			Any  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     @unittest.skipIf(torch_device == '''cpu'''							,			'''Cant do half precision'''      )
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			str  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			fpaa=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Optional[int]  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			str  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		str      )						->							Any:
          __UpperCAmelCase     :			int  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			Tuple  							=      AutoConfig.from_pretrained(snake_case      )
          # set architectures equal to `None`
          __UpperCAmelCase     :			Tuple  							=      None
          __UpperCAmelCase     :			List[Any]  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			List[Any]  							=      PyTorchBenchmark(snake_case							,			configs=[config]      )
          __UpperCAmelCase     :			Dict  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[Any]:
          __UpperCAmelCase     :			int  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			str  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Dict  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			Any  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result      )
          self.check_results_dict_not_empty(results.memory_train_result      )

     @unittest.skipIf(torch_device == '''cpu'''							,			'''Can\'t do half precision'''      )
     def 			lowerCamelCase__			(    self					:		Any      )						->							Dict:
          __UpperCAmelCase     :			Tuple  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			List[Any]  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			fpaa=snake_case							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Optional[Any]  							=      PyTorchBenchmark(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result      )
          self.check_results_dict_not_empty(results.memory_train_result      )

     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			Any  							=      AutoConfig.from_pretrained(snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			int  							=      PyTorchBenchmark(snake_case							,			configs=[config]      )
          __UpperCAmelCase     :			Union[str, Any]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase     :			Optional[Any]  							=      '''sshleifer/tinier_bart'''
          __UpperCAmelCase     :			Any  							=      AutoConfig.from_pretrained(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Dict  							=      PyTorchBenchmark(snake_case							,			configs=[config]      )
          __UpperCAmelCase     :			Optional[Any]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_inference_result      )
          self.check_results_dict_not_empty(results.memory_inference_result      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			Optional[Any]  							=      '''sshleifer/tiny-gpt2'''
          __UpperCAmelCase     :			str  							=      AutoConfig.from_pretrained(snake_case      )
          __UpperCAmelCase     :			int  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			List[Any]  							=      PyTorchBenchmark(snake_case							,			configs=[config]      )
          __UpperCAmelCase     :			List[str]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result      )
          self.check_results_dict_not_empty(results.memory_train_result      )

     def 			lowerCamelCase__			(    self					:		str      )						->							int:
          __UpperCAmelCase     :			Dict  							=      '''sshleifer/tinier_bart'''
          __UpperCAmelCase     :			Tuple  							=      AutoConfig.from_pretrained(snake_case      )
          __UpperCAmelCase     :			Any  							=      PyTorchBenchmarkArguments(
              models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			multi_process=snake_case							,			)
          __UpperCAmelCase     :			Union[str, Any]  							=      PyTorchBenchmark(snake_case							,			configs=[config]      )
          __UpperCAmelCase     :			List[Any]  							=      benchmark.run()
          self.check_results_dict_not_empty(results.time_train_result      )
          self.check_results_dict_not_empty(results.memory_train_result      )

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          __UpperCAmelCase     :			str  							=      '''sshleifer/tiny-gpt2'''
          with tempfile.TemporaryDirectory() as tmp_dir:
               __UpperCAmelCase     :			Any  							=      PyTorchBenchmarkArguments(
                   models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			save_to_csv=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			inference_time_csv_file=os.path.join(snake_case							,			'''inf_time.csv'''      )							,			train_memory_csv_file=os.path.join(snake_case							,			'''train_mem.csv'''      )							,			inference_memory_csv_file=os.path.join(snake_case							,			'''inf_mem.csv'''      )							,			train_time_csv_file=os.path.join(snake_case							,			'''train_time.csv'''      )							,			env_info_csv_file=os.path.join(snake_case							,			'''env.csv'''      )							,			multi_process=snake_case							,			)
               __UpperCAmelCase     :			str  							=      PyTorchBenchmark(snake_case      )
               benchmark.run()
               self.assertTrue(Path(os.path.join(snake_case							,			'''inf_time.csv'''      )      ).exists()      )
               self.assertTrue(Path(os.path.join(snake_case							,			'''train_time.csv'''      )      ).exists()      )
               self.assertTrue(Path(os.path.join(snake_case							,			'''inf_mem.csv'''      )      ).exists()      )
               self.assertTrue(Path(os.path.join(snake_case							,			'''train_mem.csv'''      )      ).exists()      )
               self.assertTrue(Path(os.path.join(snake_case							,			'''env.csv'''      )      ).exists()      )






     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							List[str]:
          __UpperCAmelCase     :			str  							=      '''sshleifer/tiny-gpt2'''

          def _check_summary_is_not_empty(snake_case					:		List[str]      ):
               self.assertTrue(hasattr(snake_case							,			'''sequential'''      )      )
               self.assertTrue(hasattr(snake_case							,			'''cumulative'''      )      )
               self.assertTrue(hasattr(snake_case							,			'''current'''      )      )
               self.assertTrue(hasattr(snake_case							,			'''total'''      )      )

          with tempfile.TemporaryDirectory() as tmp_dir:
               __UpperCAmelCase     :			str  							=      PyTorchBenchmarkArguments(
                   models=[MODEL_ID]							,			training=snake_case							,			inference=snake_case							,			sequence_lengths=[8]							,			batch_sizes=[1]							,			log_filename=os.path.join(snake_case							,			'''log.txt'''      )							,			log_print=snake_case							,			trace_memory_line_by_line=snake_case							,			multi_process=snake_case							,			)
               __UpperCAmelCase     :			Optional[Any]  							=      PyTorchBenchmark(snake_case      )
               __UpperCAmelCase     :			Dict  							=      benchmark.run()
               _check_summary_is_not_empty(result.inference_summary      )
               _check_summary_is_not_empty(result.train_summary      )
               self.assertTrue(Path(os.path.join(snake_case							,			'''log.txt'''      )      ).exists()      )



'''simple docstring'''



from __future__ import annotations

__UpperCAmelCase      :Tuple			    =       "Muhammad Umer Farooq"
__UpperCAmelCase      :Tuple			    =       "MIT"
__UpperCAmelCase      :Union[str, Any]			    =       "1.0.0"
__UpperCAmelCase      :Optional[int]			    =       "Muhammad Umer Farooq"
__UpperCAmelCase      :Optional[Any]			    =       "contact@muhammadumerfarooq.me"
__UpperCAmelCase      :Any			    =       "Alpha"

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

import requests



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Tuple							,			snake_case					:		str      )						->							None:
          super().__init__()
          __UpperCAmelCase     :			list[str]  							=      []
          __UpperCAmelCase     :			Optional[int]  							=      domain






     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		str							,			snake_case					:		list[tuple[str, str | None]]      )						->							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:
                              __UpperCAmelCase     :			Optional[Any]  							=      parse.urljoin(self.domain							,			snake_case      )
                              self.urls.append(snake_case      )




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






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




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






     return parse.urlparse(_lowercase  ).netloc




def      _a     (						_lowercase :     str = "https://github.com"  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      get_domain_name(_lowercase  )

     # Initialize the parser
     __UpperCAmelCase     :			Dict  							=      Parser(_lowercase  )

     try:
          # Open URL
          __UpperCAmelCase     :			Dict  							=      requests.get(_lowercase  )

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

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

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


if __name__ == "__main__":
      __UpperCAmelCase      :List[str]			    =       emails_from_url("https://github.com")
      print(f"""{len(emails)} emails found:""")
      print("\n".join(sorted(emails)))