






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


A_						:						Any						      =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A_						:						List[str]						      =					'    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n'




class 							_lowerCAmelCase(			unittest.TestCase   ):





       """simple docstring"""







       def      _a       (   self				):
         UpperCamelCase_:   str        =							tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir    ,     'models/bert/'				)				)
         UpperCamelCase_:   int        =							self.transformer_dir
         shutil.copy(
             os.path.join(a__    ,     'src/transformers/models/bert/modeling_bert.py'				)    ,     os.path.join(self.transformer_dir    ,     'models/bert/modeling_bert.py'				)    ,     )





       def      _a       (   self				):
         UpperCamelCase_:   int        =							"src/transformers"
         shutil.rmtree(self.transformer_dir				)





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase=None				):
         UpperCamelCase_:   Any        =							comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
         if overwrite_result is not None:
           UpperCamelCase_:   Any        =							comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
         UpperCamelCase_:   Union[str, Any]        =							black.Mode(target_versions={black.TargetVersion.PYaa}    ,     line_length=1_1_9				)
         UpperCamelCase_:   Optional[int]        =							black.format_str(a__    ,     mode=a__				)
         UpperCamelCase_:   Tuple        =							os.path.join(self.transformer_dir    ,     'new_code.py'				)
         with open(a__    ,     'w'    ,     newline='\n'				) as f:
           f.write(a__				)
         if overwrite_result is None:
           self.assertTrue(len(check_copies.is_copy_consistent(a__				)				) == 0				)
         else:
           check_copies.is_copy_consistent(f.name    ,     overwrite=a__				)
           with open(a__    ,     'r'				) as f:
             self.assertTrue(f.read()    ,     a__				)





       def      _a       (   self				):
         UpperCamelCase_:   Dict        =							check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'				)
         self.assertEqual(a__    ,     a__				)





       def      _a       (   self				):
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'    ,     'BertLMPredictionHead'    ,     REFERENCE_CODE + '\n'    ,     )

         # With no empty line at the end
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'    ,     'BertLMPredictionHead'    ,     a__    ,     )

         # Copy consistency with rename
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'    ,     'TestModelLMPredictionHead'    ,     re.sub('Bert'    ,     'TestModel'    ,     a__				)    ,     )

         # Copy consistency with a really long name
         UpperCamelCase_:   int        =							"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
         self.check_copy_consistency(
             f'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''    ,     f'''{long_class_name}LMPredictionHead'''    ,     re.sub('Bert'    ,     a__    ,     a__				)    ,     )

         # Copy consistency with overwrite
         self.check_copy_consistency(
             '# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'    ,     'TestModelLMPredictionHead'    ,     a__    ,     overwrite_result=re.sub('Bert'    ,     'TestModel'    ,     a__				)    ,     )





       def      _a       (   self				):
         UpperCamelCase_:   str        =							check_copies.LOCALIZED_READMES["README_zh-hans.md"]

         UpperCamelCase_:   str        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
             " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
             " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
             " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
             " Luong, Quoc V. Le, Christopher D. Manning."
         )
         UpperCamelCase_:   List[str]        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         UpperCamelCase_:   Tuple        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
             " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
             " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
             " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
             " method has been applied to compress GPT2 into"
             " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
             " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
             " Multilingual BERT into"
             " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
             " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
             " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
             " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
             " Christopher D. Manning 发布。\n"
         )

         UpperCamelCase_:   List[Any]        =							check_copies.convert_to_localized_md(
             a__    ,     a__    ,     localized_readme['format_model_list']				)

         self.assertFalse(a__				)
         self.assertEqual(a__    ,     a__				)

         UpperCamelCase_:   int        =							check_copies.convert_to_localized_md(
             a__    ,     a__    ,     localized_readme['format_model_list']				)

         # Check whether the number of models is equal to README.md after conversion.
         self.assertTrue(a__				)

         UpperCamelCase_:   List[Any]        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
             " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
             " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
             " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
         )
         UpperCamelCase_:   Union[str, Any]        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
             " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )
         UpperCamelCase_:   Optional[int]        =							(
             "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
             " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
             " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
             " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
         )

         UpperCamelCase_:   str        =							check_copies.convert_to_localized_md(
             a__    ,     a__    ,     localized_readme['format_model_list']				)

         # Check if the model link is synchronized.
         self.assertEqual(a__    ,     a__				)

'''simple docstring'''

import sys
import webbrowser

import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent

if __name__ == "__main__":
     print("""Googling.....""")
     snake_case									=    """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
     snake_case									=    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(1_00_00):
               out_file.write(data)
     snake_case									=    BeautifulSoup(res.text, """html.parser""")
     snake_case									=    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")}')





