





"""simple docstring"""







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

import black


a__      :	Dict       				=      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.
a__      :	int       				=      '    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 __magic_name__   (       unittest.TestCase		):
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir   ,  'models/bert/'	)	)
         _lowerCAmelCase	      =					self.transformer_dir
         shutil.copy(
             os.path.join(__magic_name__   ,  'src/transformers/models/bert/modeling_bert.py'	)   ,  os.path.join(self.transformer_dir   ,  'models/bert/modeling_bert.py'	)   ,  )
       def 		_lowerCamelCase   (     self	):





         """simple docstring"""


         _lowerCAmelCase	      =					'src/transformers'
         shutil.rmtree(self.transformer_dir	)
       def 		_lowerCamelCase   (     self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__=None	):





         """simple docstring"""


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





         """simple docstring"""


         _lowerCAmelCase	      =					check_copies.find_code_in_transformers('models.bert.modeling_bert.BertLMPredictionHead'	)
         self.assertEqual(__magic_name__   ,  __magic_name__	)
       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'   ,  __magic_name__   ,  )

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

         # Copy consistency with a really long name
         _lowerCAmelCase	      =					'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'   ,  __magic_name__   ,  __magic_name__	)   ,  )

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





         """simple docstring"""


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

         _lowerCAmelCase	      =					(
             '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.'
         )
         _lowerCAmelCase	      =					(
             '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'
         )
         _lowerCAmelCase	      =					(
             '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'
         )

         _lowerCAmelCase      ,   _lowerCAmelCase	      =					check_copies.convert_to_localized_md(
             __magic_name__   ,  __magic_name__   ,  localized_readme['format_model_list']	)

         self.assertFalse(__magic_name__	)
         self.assertEqual(__magic_name__   ,  __magic_name__	)

         _lowerCAmelCase      ,   _lowerCAmelCase	      =					check_copies.convert_to_localized_md(
             __magic_name__   ,  __magic_name__   ,  localized_readme['format_model_list']	)

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

         _lowerCAmelCase	      =					(
             '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.'
         )
         _lowerCAmelCase	      =					(
             '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'
         )
         _lowerCAmelCase	      =					(
             '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'
         )

         _lowerCAmelCase      ,   _lowerCAmelCase	      =					check_copies.convert_to_localized_md(
             __magic_name__   ,  __magic_name__   ,  localized_readme['format_model_list']	)

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











import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


A__      :     Tuple       =					logging.get_logger(__name__)
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			"""upernet"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       str=512,    lowerCamelCase		:       Optional[Any]=0.02,    lowerCamelCase		:       Optional[Any]=[1, 2, 3, 6],    lowerCamelCase		:       Optional[int]=True,    lowerCamelCase		:       Tuple=0.4,    lowerCamelCase		:       Optional[int]=384,    lowerCamelCase		:       Optional[int]=256,    lowerCamelCase		:       Dict=1,    lowerCamelCase		:       str=False,    lowerCamelCase		:       List[str]=255,    **lowerCamelCase		:       List[Any],    ):

								'''simple docstring'''
								super().__init__(**lowerCamelCase    )

								if backbone_config is None:
												logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''    )
												lowercase__  					=     CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']    )
								elif isinstance(lowerCamelCase,    lowerCamelCase    ):
												lowercase__  					=     backbone_config.get('''model_type'''    )
												lowercase__  					=     CONFIG_MAPPING[backbone_model_type]
												lowercase__  					=     config_class.from_dict(lowerCamelCase    )

								lowercase__  					=     backbone_config
								lowercase__  					=     hidden_size
								lowercase__  					=     initializer_range
								lowercase__  					=     pool_scales
								lowercase__  					=     use_auxiliary_head
								lowercase__  					=     auxiliary_loss_weight
								lowercase__  					=     auxiliary_in_channels
								lowercase__  					=     auxiliary_channels
								lowercase__  					=     auxiliary_num_convs
								lowercase__  					=     auxiliary_concat_input
								lowercase__  					=     loss_ignore_index




				def 							lowercase__  (	self		:       str    ):

								'''simple docstring'''
								lowercase__  					=     copy.deepcopy(self.__dict__    )
								lowercase__  					=     self.backbone_config.to_dict()
								lowercase__  					=     self.__class__.model_type
								return output




