


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

import black


SCREAMING_SNAKE_CASE						: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.
SCREAMING_SNAKE_CASE						:Optional[int]			=  '''    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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       tempfile.mkdtemp()
     os.makedirs(os.path.join(self.transformer_dir     ,					"models/bert/"	)	)
     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[Any]	) ->			str:




     """simple docstring"""
     snake_case_            =       "src/transformers"
     shutil.rmtree(self.transformer_dir	)



  def        lowerCAmelCase__	(  self							:							List[Any]     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							int=None	) ->			int:




     """simple docstring"""
     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=1_1_9	)
     snake_case_            =       black.format_str(_lowerCAmelCase     ,					mode=_lowerCAmelCase	)
     snake_case_            =       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        lowerCAmelCase__	(  self							:							Any	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			int:




     """simple docstring"""
     # 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"     ,					_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
     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"     ,					_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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			int:




     """simple docstring"""
     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_     ,     snake_case_            =       check_copies.convert_to_localized_md(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					localized_readme["format_model_list"]	)

     self.assertFalse(_lowerCAmelCase	)
     self.assertEqual(_lowerCAmelCase     ,					_lowerCAmelCase	)

     snake_case_     ,     snake_case_            =       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	)

     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_     ,     snake_case_            =       check_copies.convert_to_localized_md(
         _lowerCAmelCase     ,					_lowerCAmelCase     ,					localized_readme["format_model_list"]	)

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








from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizer
  _SCREAMING_SNAKE_CASE		     =							DistilBertTokenizerFast
  _SCREAMING_SNAKE_CASE		     =							True



  @slow
  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       DistilBertTokenizer.from_pretrained("distilbert-base-uncased"	)

     snake_case_            =       tokenizer.encode("sequence builders"     ,					add_special_tokens=_lowerCAmelCase	)
     snake_case_            =       tokenizer.encode("multi-sequence build"     ,					add_special_tokens=_lowerCAmelCase	)

     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase	)
     snake_case_            =       tokenizer.build_inputs_with_special_tokens(_lowerCAmelCase     ,					_lowerCAmelCase	)

     assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
     assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
         tokenizer.sep_token_id
     ]




