

'''simple docstring'''






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

import black


lowerCAmelCase__      		=			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.
lowerCAmelCase__      		=			'''    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     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				tempfile.mkdtemp()
   os.makedirs(os.path.join(self.transformer_dir      ,'''models/bert/'''    )    )
   __lowercase													=				self.transformer_dir
   shutil.copy(
       os.path.join(lowercase__      ,'''src/transformers/models/bert/modeling_bert.py'''    )      ,os.path.join(self.transformer_dir      ,'''models/bert/modeling_bert.py'''    )      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''src/transformers'''
   shutil.rmtree(self.transformer_dir    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Any      ,lowercase__     :  Any      ,lowercase__     :  Optional[int]      ,lowercase__     :  List[Any]=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(lowercase__      ,mode=lowercase__    )
   __lowercase													=				os.path.join(self.transformer_dir      ,'''new_code.py'''    )
   with open(lowercase__      ,'''w'''      ,newline='''\n'''    ) as f:
    f.write(lowercase__    )
   if overwrite_result is None:
    self.assertTrue(len(check_copies.is_copy_consistent(lowercase__    )    ) == 0    )
   else:
    check_copies.is_copy_consistent(f.name      ,overwrite=lowercase__    )
    with open(lowercase__      ,'''r'''    ) as f:
     self.assertTrue(f.read()      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   __lowercase													=				check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''    )
   self.assertEqual(lowercase__      ,lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   # 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'''      ,lowercase__      ,)

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

   # 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'''      ,lowercase__      ,lowercase__    )      ,)

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __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(
       lowercase__      ,lowercase__      ,localized_readme['''format_model_list''']    )

   self.assertFalse(lowercase__    )
   self.assertEqual(lowercase__      ,lowercase__    )

   __lowercase						,			__lowercase													=				check_copies.convert_to_localized_md(
       lowercase__      ,lowercase__      ,localized_readme['''format_model_list''']    )

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

   __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(
       lowercase__      ,lowercase__      ,localized_readme['''format_model_list''']    )

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




'''simple docstring'''






from __future__ import annotations

from typing import Any






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  int      ,lowercase__     :  int      ,lowercase__     :  float = 0    ):
   __lowercase						,			__lowercase													=				row, column
   __lowercase													=				[[default_value for c in range(lowercase__    )] for r in range(lowercase__    )]






  def __str__(   self     :  List[str]    ):
   __lowercase													=				F"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   __lowercase													=				0
   for row_vector in self.array:
    for obj in row_vector:
     __lowercase													=				max(lowercase__      ,len(str(lowercase__    )    )    )
   __lowercase													=				F"%{max_element_length}s"

   # Make string and return
   def single_line(lowercase__     :  list[float]    ) -> str:
    nonlocal string_format_identifier
    __lowercase													=				'''['''
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(lowercase__    ) for row_vector in self.array    )
   return s






  def __repr__(   self     :  List[str]    ):
   return str(self    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  tuple[int, int]    ):
   if not (isinstance(lowercase__      ,(list, tuple)    ) and len(lowercase__    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True






  def __getitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]    ):
   assert self.validate_indicies(lowercase__    )
   return self.array[loc[0]][loc[1]]






  def __setitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]      ,lowercase__     :  float    ):
   assert self.validate_indicies(lowercase__    )
   __lowercase													=				value






  def __add__(   self     :  List[Any]      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    )
   assert self.row == another.row and self.column == another.column

   # Add
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c] + another[r, c]
   return result






  def __neg__(   self     :  List[str]    ):
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				-self[r, c]
   return result






  def __sub__(   self     :  str      ,lowercase__     :  Matrix    ):
   return self + (-another)






  def __mul__(   self     :  Dict      ,lowercase__     :  int | float | Matrix    ):

   if isinstance(lowercase__      ,(int, float)    ):  # Scalar multiplication
    __lowercase													=				Matrix(self.row      ,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      __lowercase													=				self[r, c] * another
    return result
   elif isinstance(lowercase__      ,lowercase__    ):  # Matrix multiplication
    assert self.column == another.row
    __lowercase													=				Matrix(self.row      ,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    __lowercase													=				F"Unsupported type given for another ({type(lowercase__    )})"
    raise TypeError(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				Matrix(self.column      ,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c]
   return result






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Matrix      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    ) and isinstance(lowercase__      ,lowercase__    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   __lowercase													=				v.transpose()
   __lowercase													=				(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


 def 	_A  (					):






  """simple docstring"""



  __lowercase													=				Matrix(3  ,       3  ,       0   )
  for i in range(3   ):
   __lowercase													=				1
  print(F"a^(-1) is {ainv}"   )
  # u, v
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				1, 2, -3
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				4, -2, 5
  print(F"u is {u}"   )
  print(F"v is {v}"   )
  print(F"uv^T is {u * v.transpose()}"   )
  # Sherman Morrison
  print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(A__  ,       A__   )}"   )


 def 	_A  (					):






  """simple docstring"""



  import doctest

  doctest.testmod()






 testa()

