


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

import black


UpperCAmelCase      =   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.
UpperCAmelCase      =   """    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 						UpperCamelCase__					(     self ):
       snake_case_	=				tempfile.mkdtemp()
       os.makedirs(os.path.join(self.transformer_dir		,				'''models/bert/''' ) )
       snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				'''src/transformers'''
       shutil.rmtree(self.transformer_dir )



 def 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase		,				_UpperCAmelCase=None ):
       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_19 )
       snake_case_	=				black.format_str(_UpperCAmelCase		,				mode=_UpperCAmelCase )
       snake_case_	=				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 						UpperCamelCase__					(     self ):
       snake_case_	=				check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''' )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )



 def 						UpperCamelCase__					(     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'''		,				_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
       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'''		,				_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 						UpperCamelCase__					(     self ):
       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(
           _UpperCAmelCase		,				_UpperCAmelCase		,				localized_readme['''format_model_list'''] )

       self.assertFalse(_UpperCAmelCase )
       self.assertEqual(_UpperCAmelCase		,				_UpperCAmelCase )

       snake_case_	,   snake_case_	=				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 )

       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(
           _UpperCAmelCase		,				_UpperCAmelCase		,				localized_readme['''format_model_list'''] )

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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase      =   {"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   ["""XGLMTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XGLMForCausalLM""",
       """XGLMModel""",
       """XGLMPreTrainedModel""",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """FlaxXGLMForCausalLM""",
       """FlaxXGLMModel""",
       """FlaxXGLMPreTrainedModel""",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase      =   [
       """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXGLMForCausalLM""",
       """TFXGLMModel""",
       """TFXGLMPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm import XGLMTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_xglm_fast import XGLMTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xglm import (
          TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXGLMForCausalLM,
          TFXGLMModel,
          TFXGLMPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase      =   _LazyModule(__name__, globals()["""__file__"""], _import_structure)