




'''simple docstring'''





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

import black


UpperCamelCase_						:      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_						:      List[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     _lowercase     (			self   )							->      str:
          _snake_case							=				tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir ,"models/bert/"   )   )
          _snake_case							=				self.transformer_dir
          shutil.copy(
              os.path.join(_SCREAMING_SNAKE_CASE ,"src/transformers/models/bert/modeling_bert.py"   ) ,os.path.join(self.transformer_dir ,"models/bert/modeling_bert.py"   ) ,)






     def     _lowercase     (			self   )							->      Any:
          _snake_case							=				'src/transformers'
          shutil.rmtree(self.transformer_dir   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None   )							->      str:
          _snake_case							=				comment + f"""\nclass {class_name}(nn.Module):\n""" + class_code
          if overwrite_result is not None:
               _snake_case							=				comment + f"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
          _snake_case							=				black.Mode(target_versions={black.TargetVersion.PYaa} ,line_length=119   )
          _snake_case							=				black.format_str(_SCREAMING_SNAKE_CASE ,mode=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				os.path.join(self.transformer_dir ,"new_code.py"   )
          with open(_SCREAMING_SNAKE_CASE ,"w" ,newline="\n"   ) as f:
               f.write(_SCREAMING_SNAKE_CASE   )
          if overwrite_result is None:
               self.assertTrue(len(check_copies.is_copy_consistent(_SCREAMING_SNAKE_CASE   )   ) == 0   )
          else:
               check_copies.is_copy_consistent(f.name ,overwrite=_SCREAMING_SNAKE_CASE   )
               with open(_SCREAMING_SNAKE_CASE ,"r"   ) as f:
                    self.assertTrue(f.read() ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Tuple:
          # 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" ,_SCREAMING_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" ,_SCREAMING_SNAKE_CASE   ) ,)

          # Copy consistency with a really long name
          _snake_case							=				'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" ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) ,)

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






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				check_copies.LOCALIZED_READMES['README_zh-hans.md']

          _snake_case							=				(
              '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.'
          )
          _snake_case							=				(
              '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'
          )
          _snake_case							=				(
              '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'
          )

          _snake_case							=				check_copies.convert_to_localized_md(
              _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,localized_readme["format_model_list"]   )

          self.assertFalse(_SCREAMING_SNAKE_CASE   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				check_copies.convert_to_localized_md(
              _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,localized_readme["format_model_list"]   )

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

          _snake_case							=				(
              '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.'
          )
          _snake_case							=				(
              '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'
          )
          _snake_case							=				(
              '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'
          )

          _snake_case							=				check_copies.convert_to_localized_md(
              _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,localized_readme["format_model_list"]   )

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








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			None:

     """simple docstring"""




     _snake_case							=				generate_pascal_triangle(_UpperCamelCase   )
     for row_idx in range(_UpperCamelCase   ):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1   ):
               print(end=" "   )
          # Print row values
          for col_idx in range(row_idx + 1   ):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx] ,						end=" "   )
               else:
                    print(triangle[row_idx][col_idx] ,						end=""   )
          print()





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[]
     for current_row_idx in range(_UpperCamelCase   ):
          _snake_case							=				populate_current_row(_UpperCamelCase ,						_UpperCamelCase   )
          triangle.append(_UpperCamelCase   )
     return triangle





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   int   )						->			list[int]:

     """simple docstring"""




     _snake_case							=				[-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     _snake_case							, _snake_case							=				1, 1
     for current_col_idx in range(1 ,						_UpperCamelCase   ):
          calculate_current_element(
              _UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase   )
     return current_row





def      __a     (       _UpperCamelCase:   list[list[int]] ,						_UpperCamelCase:   list[int] ,						_UpperCamelCase:   int ,						_UpperCamelCase:   int ,						)						->			None:

     """simple docstring"""




     _snake_case							=				triangle[current_row_idx - 1][current_col_idx - 1]
     _snake_case							=				triangle[current_row_idx - 1][current_col_idx]
     _snake_case							=				above_to_left_elt + above_to_right_elt





def      __a     (       _UpperCamelCase:   int   )						->			list[list[int]]:

     """simple docstring"""




     if not isinstance(_UpperCamelCase ,						_UpperCamelCase   ):
          raise TypeError("The input value of 'num_rows' should be 'int'"   )

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              "The input value of 'num_rows' should be greater than or equal to 0"   )

     _snake_case							=				[[1]]

     for row_index in range(1 ,						_UpperCamelCase   ):
          _snake_case							=				[0] + result[-1] + [0]
          _snake_case							=				row_index + 1
          # Calculate the number of distinct elements in a row
          _snake_case							=				sum(divmod(_UpperCamelCase ,						2   )   )
          _snake_case							=				[
              temp_row[i - 1] + temp_row[i] for i in range(1 ,						distinct_elements + 1   )
          ]
          _snake_case							=				row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          _snake_case							=				row_first_half + row_second_half
          result.append(_UpperCamelCase   )

     return result





def      __a     (       )						->			None:

     """simple docstring"""




     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(_UpperCamelCase:   Callable ,						_UpperCamelCase:   int   ) -> None:
          _snake_case							=				F"""{func.__name__}({value})"""
          _snake_case							=				timeit(F"""__main__.{call}""" ,						setup="import __main__"   )
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F"""{call:38} -- {timing:.4f} seconds"""   )

     for value in range(15   ):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(_UpperCamelCase ,						_UpperCamelCase   )
          print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()


