

'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case     =  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     =  '    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    a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Any			     =   tempfile.mkdtemp()
      os.makedirs(os.path.join(self.transformer_dir			,				"models/bert/"  )  )
      _lowercase			:      List[str]			     =   self.transformer_dir
      shutil.copy(
          os.path.join(_UpperCamelCase			,				"src/transformers/models/bert/modeling_bert.py"  )			,				os.path.join(self.transformer_dir			,				"models/bert/modeling_bert.py"  )			,				)


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   "src/transformers"
      shutil.rmtree(self.transformer_dir  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase			,				_UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase			:      Any			     =   comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
      if overwrite_result is not None:
         _lowercase			:      Optional[int]			     =   comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
      _lowercase			:      Any			     =   black.Mode(target_versions={black.TargetVersion.PYaa}			,				line_length=119  )
      _lowercase			:      Union[str, Any]			     =   black.format_str(_UpperCamelCase			,				mode=_UpperCamelCase  )
      _lowercase			:      str			     =   os.path.join(self.transformer_dir			,				"new_code.py"  )
      with open(_UpperCamelCase			,				"w"			,				newline="\n"  ) as f:
         f.write(_UpperCamelCase  )
      if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase  )  ) == 0  )
      else:
         check_copies.is_copy_consistent(f.name			,				overwrite=_UpperCamelCase  )
         with open(_UpperCamelCase			,				"r"  ) as f:
            self.assertTrue(f.read()			,				_UpperCamelCase  )


   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      List[Any]			     =   check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"  )
      self.assertEqual(_UpperCamelCase			,				_UpperCamelCase  )


   def 						_lowerCamelCase   ( 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"			,				_UpperCamelCase			,				)

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

      # Copy consistency with a really long name
      _lowercase			:      List[str]			     =   "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"			,				_UpperCamelCase			,				_UpperCamelCase  )			,				)

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






   def 						_lowerCamelCase   ( self  ):





      """simple docstring"""





      _lowercase			:      Any			     =   check_copies.LOCALIZED_READMES["README_zh-hans.md"]

      _lowercase			:      List[str]			     =   (
          "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			:      str			     =   (
          "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			:      Dict			     =   (
          "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			:      Optional[int]			     =   check_copies.convert_to_localized_md(
          _UpperCamelCase			,				_UpperCamelCase			,				localized_readme["format_model_list"]  )

      self.assertFalse(_UpperCamelCase  )
      self.assertEqual(_UpperCamelCase			,				_UpperCamelCase  )

      _lowercase     ,      _lowercase			:      Optional[int]			     =   check_copies.convert_to_localized_md(
          _UpperCamelCase			,				_UpperCamelCase			,				localized_readme["format_model_list"]  )

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

      _lowercase			:      List[str]			     =   (
          "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			:      int			     =   (
          "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			:      Any			     =   (
          "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			:      Optional[Any]			     =   check_copies.convert_to_localized_md(
          _UpperCamelCase			,				_UpperCamelCase			,				localized_readme["format_model_list"]  )

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









import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE			,			SCREAMING_SNAKE_CASE							):




  @register_to_config
  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   )  -> Tuple:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)

        _lowercase	       =nn.Embedding(lowerCAmelCase							,   lowerCAmelCase					)
        _lowercase	       =False

        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)

        _lowercase	       =TaConfig(
            vocab_size=lowerCAmelCase							,   d_model=lowerCAmelCase							,   num_heads=lowerCAmelCase							,   d_kv=lowerCAmelCase							,   d_ff=lowerCAmelCase							,   dropout_rate=lowerCAmelCase							,   feed_forward_proj=lowerCAmelCase							,   is_decoder=lowerCAmelCase							,   is_encoder_decoder=lowerCAmelCase							,   )

        _lowercase	       =nn.ModuleList()
        for lyr_num in range(lowerCAmelCase					):
              _lowercase	       =TaBlock(lowerCAmelCase					)
              self.encoders.append(lowerCAmelCase					)

        _lowercase	       =TaLayerNorm(lowerCAmelCase					)
        _lowercase	       =nn.Dropout(p=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






        _lowercase	       =self.token_embedder(lowerCAmelCase					)

        _lowercase	       =encoder_input_tokens.shape[1]
        _lowercase	       =torch.arange(lowerCAmelCase							,   device=encoder_input_tokens.device					)
        x += self.position_encoding(lowerCAmelCase					)

        _lowercase	       =self.dropout_pre(lowerCAmelCase					)

        # inverted the attention mask
        _lowercase	       =encoder_input_tokens.size()
        _lowercase	       =self.get_extended_attention_mask(lowerCAmelCase							,   lowerCAmelCase					)

        for lyr in self.encoders:
              _lowercase	       =lyr(lowerCAmelCase							,   lowerCAmelCase					)[0]
        _lowercase	       =self.layer_norm(lowerCAmelCase					)

        return self.dropout_post(lowerCAmelCase					), encoder_inputs_mask


