


'''simple docstring'''


import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 	__lowerCAmelCase		:


  '''simple docstring'''







  def        _a							(							self :       Dict	,_a :       Optional[Any]	,_a :       Any	,_a :       int ):

        '''simple docstring'''




        return None







class 	__lowerCAmelCase		:


  '''simple docstring'''







  def        _a							(							self :       int	,_a :       Dict	,_a :       Union[str, Any]	,_a :       Union[str, Any]	,_a :       List[Any] ):

        '''simple docstring'''




        return None







class 	__lowerCAmelCase		(							unittest.TestCase    ):


  '''simple docstring'''







  a_  					=							[
      # (model_name, model_kwargs)
      ("""bert-base-cased""", {}),
      ("""gpt2""", {"""use_cache""": False}),  # We don't support exporting GPT2 past keys anymore
  ]






  @require_tf
  @slow
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowercase_	,"""tf"""	,12	,**lowercase_ )






  @require_torch
  @slow
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              self._test_export(lowercase_	,"""pt"""	,12	,**lowercase_ )






  @require_torch
  @slow
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        from transformers import BertModel

        A_						:  str          =				["""[UNK]""", """[SEP]""", """[CLS]""", """[PAD]""", """[MASK]""", """some""", """other""", """words"""]
        with NamedTemporaryFile(mode="""w+t""" ) as vocab_file:
              vocab_file.write("""\n""".join(lowercase_ ) )
              vocab_file.flush()
              A_						:  str          =				BertTokenizerFast(vocab_file.name )

        with TemporaryDirectory() as bert_save_dir:
              A_						:  Any          =				BertModel(BertConfig(vocab_size=len(lowercase_ ) ) )
              model.save_pretrained(lowercase_ )
              self._test_export(lowercase_	,"""pt"""	,12	,lowercase_ )






  @require_tf
  @slow
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              A_						:  Optional[int]          =				self._test_export(lowercase_	,"""tf"""	,12	,**lowercase_ )
              A_						:  Dict          =				quantize(Path(lowercase_ ) )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowercase_ ).stat().st_size:
                    self.fail("""Quantized model is bigger than initial ONNX model""" )






  @require_torch
  @slow
  def        _a							(							self :       Any ):

        '''simple docstring'''




        for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
              A_						:  Union[str, Any]          =				self._test_export(lowercase_	,"""pt"""	,12	,**lowercase_ )
              A_						:  List[Any]          =				quantize(lowercase_ )

              # Ensure the actual quantized model is not bigger than the original one
              if quantized_path.stat().st_size >= Path(lowercase_ ).stat().st_size:
                    self.fail("""Quantized model is bigger than initial ONNX model""" )






  def        _a							(							self :       Dict	,_a :       List[str]	,_a :       int	,_a :       List[str]	,_a :       List[str]=None	,**_a :       Tuple ):

        '''simple docstring'''




        try:
              # Compute path
              with TemporaryDirectory() as tempdir:
                    A_						:  List[str]          =				Path(lowercase_ ).joinpath("""model.onnx""" )

              # Remove folder if exists
              if path.parent.exists():
                    path.parent.rmdir()

              # Export
              convert(lowercase_	,lowercase_	,lowercase_	,lowercase_	,lowercase_	,**lowercase_ )

              return path
        except Exception as e:
              self.fail(lowercase_ )






  @require_torch
  @require_tokenizers
  @slow
  def        _a							(							self :       int ):

        '''simple docstring'''




        from transformers import BertModel

        A_						:  List[str]          =				BertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
        A_						:  Any          =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
        self._test_infer_dynamic_axis(lowercase_	,lowercase_	,"""pt""" )






  @require_tf
  @require_tokenizers
  @slow
  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        from transformers import TFBertModel

        A_						:  Any          =				TFBertModel(BertConfig.from_pretrained("""lysandre/tiny-bert-random""" ) )
        A_						:  str          =				BertTokenizerFast.from_pretrained("""lysandre/tiny-bert-random""" )
        self._test_infer_dynamic_axis(lowercase_	,lowercase_	,"""tf""" )






  def        _a							(							self :       Any	,_a :       Tuple	,_a :       Union[str, Any]	,_a :       Optional[int] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				FeatureExtractionPipeline(lowercase_	,lowercase_ )

        A_						:  List[Any]          =				["""input_ids""", """token_type_ids""", """attention_mask""", """output_0""", """output_1"""]
        A_						:  Any          =				infer_shapes(lowercase_	,lowercase_ )

        # Assert all variables are present
        self.assertEqual(len(lowercase_ )	,len(lowercase_ ) )
        self.assertTrue(all(var_name in shapes for var_name in variable_names ) )
        self.assertSequenceEqual(variable_names[:3]	,lowercase_ )
        self.assertSequenceEqual(variable_names[3:]	,lowercase_ )

        # Assert inputs are {0: batch, 1: sequence}
        for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
              self.assertDictEqual(shapes[var_name]	,{0: """batch""", 1: """sequence"""} )

        # Assert outputs are {0: batch, 1: sequence} and {0: batch}
        self.assertDictEqual(shapes["""output_0"""]	,{0: """batch""", 1: """sequence"""} )
        self.assertDictEqual(shapes["""output_1"""]	,{0: """batch"""} )






  def        _a							(							self :       Tuple ):

        '''simple docstring'''




        A_						:  int          =				["""input_ids""", """attention_mask""", """token_type_ids"""]
        A_						:  List[Any]          =				{"""input_ids""": [1, 2, 3, 4], """attention_mask""": [0, 0, 0, 0], """token_type_ids""": [1, 1, 1, 1]}
        A_						:  Optional[Any]          =				ensure_valid_input(FuncContiguousArgs()	,lowercase_	,lowercase_ )

        # Should have exactly the same number of args (all are valid)
        self.assertEqual(len(lowercase_ )	,3 )

        # Should have exactly the same input names
        self.assertEqual(set(lowercase_ )	,set(lowercase_ ) )

        # Parameter should be reordered according to their respective place in the function:
        # (input_ids, token_type_ids, attention_mask)
        self.assertEqual(lowercase_	,(tokens["""input_ids"""], tokens["""token_type_ids"""], tokens["""attention_mask"""]) )

        # Generated args are interleaved with another args (for instance parameter "past" in GPT2)
        A_						:  List[Any]          =				ensure_valid_input(FuncNonContiguousArgs()	,lowercase_	,lowercase_ )

        # Should have exactly the one arg (all before the one not provided "some_other_args")
        self.assertEqual(len(lowercase_ )	,1 )
        self.assertEqual(len(lowercase_ )	,1 )

        # Should have only "input_ids"
        self.assertEqual(inputs_args[0]	,tokens["""input_ids"""] )
        self.assertEqual(ordered_input_names[0]	,"""input_ids""" )






  def        _a							(							self :       Any ):

        '''simple docstring'''




        A_						:  Tuple          =				generate_identified_filename(Path("""/home/something/my_fake_model.onnx""" )	,"""-test""" )
        self.assertEqual("""/home/something/my_fake_model-test.onnx"""	,generated.as_posix() )




'''simple docstring'''


import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print('Googling.....')
     __magic_name__			=      'https://www.google.com/search?q=' + ' '.join(sys.argv[1:])
     __magic_name__			=      requests.get(url, headers={'UserAgent': UserAgent().random})
     # res.raise_for_status()
     with open('project1a.html', 'wb') as out_file:  # only for knowing the class
          for data in res.iter_content(10_000):
               out_file.write(data)
     __magic_name__			=      BeautifulSoup(res.text, 'html.parser')
     __magic_name__			=      list(soup.select('.eZt8xd'))[:5]

     print(len(links))
     for link in links:
          if link.text == "Maps":
               webbrowser.open(link.get('href'))
          else:
               webbrowser.open(f"""https://google.com{link.get('href')}""")
