






"""simple docstring"""



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

import black


_UpperCamelCase		:  Optional[int]						=		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.
_UpperCamelCase		:  Any						=		'''    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
'''

class        a     (   unittest.TestCase	):







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       'src/transformers'
      shutil.rmtree(self.transformer_dir       )







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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'       )
      self.assertEqual(snake_case_							,		snake_case_       )







    def 			UpperCamelCase_      (			self       ):
      # Base copy consistency
      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'							,		snake_case_							,		)

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

      # Copy consistency with a really long name
      lowercase							    =       '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'							,		snake_case_							,		snake_case_       )							,		)

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







    def 			UpperCamelCase_      (			self       ):
      lowercase							    =       check_copies.LOCALIZED_READMES['README_zh-hans.md']

      lowercase							    =       (
          '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.'
      )
      lowercase							    =       (
          '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'
      )
      lowercase							    =       (
          '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'
      )

      lowercase    ,     lowercase							    =       check_copies.convert_to_localized_md(
          snake_case_							,		snake_case_							,		localized_readme['format_model_list']       )

      self.assertFalse(snake_case_       )
      self.assertEqual(snake_case_							,		snake_case_       )

      lowercase    ,     lowercase							    =       check_copies.convert_to_localized_md(
          snake_case_							,		snake_case_							,		localized_readme['format_model_list']       )

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

      lowercase							    =       (
          '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.'
      )
      lowercase							    =       (
          '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'
      )
      lowercase							    =       (
          '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'
      )

      lowercase    ,     lowercase							    =       check_copies.convert_to_localized_md(
          snake_case_							,		snake_case_							,		localized_readme['format_model_list']       )

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









'''simple docstring'''




def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       list:


     '''simple docstring'''



     if n_term == "":
          return []
     _UpperCAmelCase     							=   []
     for temp in range(int(__lowercase       )       ):
          series.append(f'1/{temp + 1}' if series else "1"       )
     return series


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE     :str       =				input('''Enter the last number (nth term) of the Harmonic Series''')
  print('''Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n''')
  print(harmonic_series(nth_term))




