
'''simple docstring'''




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

import black


lowercase			:				Tuple    		=			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.
lowercase			:				Tuple    		=			'    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	) ->				Tuple:






         """simple docstring"""






         A       :   List[Any]					=						tempfile.mkdtemp()
         os.makedirs(os.path.join(self.transformer_dir						,			'''models/bert/'''	)	)
         A       :   Any					=						self.transformer_dir
         shutil.copy(
             os.path.join(SCREAMING_SNAKE_CASE						,			'''src/transformers/models/bert/modeling_bert.py'''	)						,			os.path.join(self.transformer_dir						,			'''models/bert/modeling_bert.py'''	)						,			)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Optional[Any]					=						'''src/transformers'''
         shutil.rmtree(self.transformer_dir	)





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE=None	) ->				Dict:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[int]					=						check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''	)
         self.assertEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """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'''						,			SCREAMING_SNAKE_CASE						,			)

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

         # Copy consistency with a really long name
         A       :   Tuple					=						'''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'''						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)						,			)

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





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         A       :   Optional[Any]					=						check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

         A       :   Any					=						(
             '''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.'''
         )
         A       :   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。\n'''
         )
         A       :   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。\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'''
         )

         A,  A       :   str					=						check_copies.convert_to_localized_md(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

         self.assertFalse(SCREAMING_SNAKE_CASE	)
         self.assertEqual(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         A,  A       :   List[Any]					=						check_copies.convert_to_localized_md(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

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

         A       :   int					=						(
             '''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.'''
         )
         A       :   Union[str, Any]					=						(
             '''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'''
         )
         A       :   List[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'''
         )

         A,  A       :   Optional[int]					=						check_copies.convert_to_localized_md(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			localized_readme['''format_model_list''']	)

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






'''simple docstring'''




import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class  A				(							unittest.TestCase						):





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   int					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   List[str]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             with self.subTest(SCREAMING_SNAKE_CASE	):
                 A       :   Any					=						AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

                 A       :   Any					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE	)
                 self.assertIsNotNone(SCREAMING_SNAKE_CASE	)
                 self.assertIsInstance(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         for model_name in ["bert-base-cased", "bert-large-uncased"]:
             A       :   Optional[int]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   List[str]					=						FlaxBertModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Optional[Any]					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





     @slow
     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         for model_name in ["roberta-base", "roberta-large"]:
             A       :   List[str]					=						AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   Union[str, Any]					=						FlaxRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE	)
             A       :   int					=						tokenizer('''Do you support jax jitted function?'''						,			return_tensors=TensorType.JAX	)

             @jax.jit
             def eval(**SCREAMING_SNAKE_CASE	):
                 return model(**SCREAMING_SNAKE_CASE	)

             eval(**SCREAMING_SNAKE_CASE	).block_until_ready()





     def 				__lowerCAmelCase							(							self	) ->				List[str]:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''bert-base is not a local folder and is not a valid model identifier'''	):
             A       :   List[Any]					=						FlaxAutoModel.from_pretrained('''bert-base'''	)





     def 				__lowerCAmelCase							(							self	) ->				Tuple:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''	):
             A       :   Optional[int]					=						FlaxAutoModel.from_pretrained(SCREAMING_SNAKE_CASE						,			revision='''aaaaaa'''	)





     def 				__lowerCAmelCase							(							self	) ->				str:






         """simple docstring"""






         with self.assertRaisesRegex(
             SCREAMING_SNAKE_CASE						,			'''hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'''						,			):
             A       :   List[str]					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''	)





     def 				__lowerCAmelCase							(							self	) ->				Optional[Any]:






         """simple docstring"""






         with self.assertRaisesRegex(SCREAMING_SNAKE_CASE						,			'''Use `from_pt=True` to load this model'''	):
             A       :   Any					=						FlaxAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''	)




