
'''simple docstring'''


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

import black


snake_case_ :  List[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.
snake_case_ :  Tuple     =					'    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 						lowercase__	(  unittest.TestCase   ):






   '''simple docstring'''







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir ,   '''models/bert/'''   )   )
      UpperCamelCase       =  self.transformer_dir
      shutil.copy(
          os.path.join(lowerCamelCase__ ,   '''src/transformers/models/bert/modeling_bert.py'''   ) ,   os.path.join(self.transformer_dir ,   '''models/bert/modeling_bert.py'''   ) ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  '''src/transformers'''
      shutil.rmtree(self.transformer_dir   )







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''   )
      self.assertEqual(lowerCamelCase__ ,   lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      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''' ,   lowerCamelCase__ ,   )

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

      # Copy consistency with a really long name
      UpperCamelCase       =  '''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''' ,   lowerCamelCase__ ,   lowerCamelCase__   ) ,   )

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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

      UpperCamelCase       =  (
          '''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       =  (
          '''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       =  (
          '''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     , UpperCamelCase       =  check_copies.convert_to_localized_md(
          lowerCamelCase__ ,   lowerCamelCase__ ,   localized_readme['''format_model_list''']   )

      self.assertFalse(lowerCamelCase__   )
      self.assertEqual(lowerCamelCase__ ,   lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  check_copies.convert_to_localized_md(
          lowerCamelCase__ ,   lowerCamelCase__ ,   localized_readme['''format_model_list''']   )

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

      UpperCamelCase       =  (
          '''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       =  (
          '''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       =  (
          '''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     , UpperCamelCase       =  check_copies.convert_to_localized_md(
          lowerCamelCase__ ,   lowerCamelCase__ ,   localized_readme['''format_model_list''']   )

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







'''simple docstring'''


def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  []
   for data in source_data:
      for i, el in enumerate(_UpperCAmelCase):
         if len(_UpperCAmelCase) < i + 1:
            data_lists.append([])
         data_lists[i].append(float(_UpperCAmelCase))
   return data_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  []
   for dlist, weight in zip(_UpperCAmelCase,      _UpperCAmelCase):
      UpperCamelCase       =  min(_UpperCAmelCase)
      UpperCamelCase       =  max(_UpperCAmelCase)

      UpperCamelCase       =  []
      # for weight 0 score is 1 - actual score
      if weight == 0:
         for item in dlist:
            try:
               score.append(1 - ((item - mind) / (maxd - mind)))
            except ZeroDivisionError:
               score.append(1)

      elif weight == 1:
         for item in dlist:
            try:
               score.append((item - mind) / (maxd - mind))
            except ZeroDivisionError:
               score.append(0)

        # weight not 0 or 1
      else:
         UpperCamelCase       =  f'Invalid weight of {weight:f} provided'
         raise ValueError(_UpperCAmelCase)

      score_lists.append(_UpperCAmelCase)

   return score_lists



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]]):
   UpperCamelCase       =  [0 for i in range(len(score_lists[0]))]

   for slist in score_lists:
      for j, ele in enumerate(_UpperCAmelCase):
         UpperCamelCase       =  final_scores[j] + ele

   return final_scores



def  __snake_case     (    _UpperCAmelCase					:       list[list[float]],      _UpperCAmelCase					:       list[int]):
   UpperCamelCase       =  get_data(_UpperCAmelCase)
   UpperCamelCase       =  calculate_each_score(_UpperCAmelCase,      _UpperCAmelCase)
   UpperCamelCase       =  generate_final_scores(_UpperCAmelCase)

   # append scores to source data
   for i, ele in enumerate(_UpperCAmelCase):
      source_data[i].append(_UpperCAmelCase)

   return source_data





