text: # Copyright 2020 The HuggingFace Team. All rights reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import os
  import re
  import shutil
  import sys
  import tempfile
  import unittest
  
  import black
  
  
  git_repo_path = 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.
  REFERENCE_CODE = """    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 CopyCheckTester(unittest.TestCase):
      def setUp(self):
          self.transformer_dir = tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir, "models/bert/"))
          check_copies.TRANSFORMER_PATH = self.transformer_dir
          shutil.copy(
              os.path.join(git_repo_path, "src/transformers/models/bert/modeling_bert.py"),
              os.path.join(self.transformer_dir, "models/bert/modeling_bert.py"),
          )
  
      def tearDown(self):
          check_copies.TRANSFORMER_PATH = "src/transformers"
          shutil.rmtree(self.transformer_dir)
  
      def check_copy_consistency(self, comment, class_name, class_code, overwrite_result=None):
          code = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
          if overwrite_result is not None:
              expected = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
          mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119)
          code = black.format_str(code, mode=mode)
          fname = os.path.join(self.transformer_dir, "new_code.py")
          with open(fname, "w", newline="\n") as f:
              f.write(code)
          if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(fname)) == 0)
          else:
              check_copies.is_copy_consistent(f.name, overwrite=True)
              with open(fname, "r") as f:
                  self.assertTrue(f.read(), expected)
  
      def test_find_code_in_transformers(self):
          code = check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead")
          self.assertEqual(code, REFERENCE_CODE)
  
      def test_is_copy_consistent(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",
              REFERENCE_CODE,
          )
  
          # Copy consistency with rename
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel",
              "TestModelLMPredictionHead",
              re.sub("Bert", "TestModel", REFERENCE_CODE),
          )
  
          # Copy consistency with a really long name
          long_class_name = "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", long_class_name, REFERENCE_CODE),
          )
  
          # Copy consistency with overwrite
          self.check_copy_consistency(
              "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel",
              "TestModelLMPredictionHead",
              REFERENCE_CODE,
              overwrite_result=re.sub("Bert", "TestModel", REFERENCE_CODE),
          )
  
      def test_convert_to_localized_md(self):
          localized_readme = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
  
          md_list = (
              "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."
          )
          localized_md_list = (
              "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"
          )
          converted_md_list_sample = (
              "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"
          )
  
          num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
              md_list, localized_md_list, localized_readme["format_model_list"]
          )
  
          self.assertFalse(num_models_equal)
          self.assertEqual(converted_md_list, converted_md_list_sample)
  
          num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
              md_list, converted_md_list, localized_readme["format_model_list"]
          )
  
          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(num_models_equal)
  
          link_changed_md_list = (
              "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."
          )
          link_unchanged_md_list = (
              "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"
          )
          converted_md_list_sample = (
              "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"
          )
  
          num_models_equal, converted_md_list = check_copies.convert_to_localized_md(
              link_changed_md_list, link_unchanged_md_list, localized_readme["format_model_list"]
          )
  
          # Check if the model link is synchronized.
          self.assertEqual(converted_md_list, converted_md_list_sample)
  