

'''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   :     Dict			):
   __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   :     Tuple			):
   __lowercase							  = '''src/transformers'''
   shutil.rmtree(self.transformer_dir			)






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






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   # 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   :     Optional[Any]			):
   __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__			)




import math


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						if 0 not in (x, y):
												# We use the relation x^y = y*log10(x), where 10 is the base.
												return y * math.logaa(SCREAMING_SNAKE_CASE__       )
						else:
												if x == 0:  # 0 raised to any number is 0
																		return 0
												elif y == 0:
																		return 1  # any number raised to 0 is 1
						raise AssertionError('''This should never happen'''       )


if __name__ == "__main__":  # Main function
					# Read two numbers from input and typecast them to int using map function.
					# Here x is the base and y is the power.
					lowerCAmelCase_       	=	'''Enter the base and the power separated by a comma: '''
					lowerCAmelCase_	,      lowerCAmelCase_       	=	map(int, input(prompt).split(''','''))
					lowerCAmelCase_	,      lowerCAmelCase_       	=	map(int, input(prompt).split(''','''))

					# We find the log of each number, using the function res(), which takes two
					# arguments.
					lowerCAmelCase_       	=	res(xa, ya)
					lowerCAmelCase_       	=	res(xa, ya)

					# We check for the largest number
					if resa > resa:
										print('''Largest number is''', xa, '''^''', ya)
					elif resa > resa:
										print('''Largest number is''', xa, '''^''', ya)
					else:
										print('''Both are equal''')