




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

import black


snake_case_ : 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.
snake_case_ : str    		=       "    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 __snake_case			(			unittest.TestCase		):







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir					,						'''models/bert/'''))
        UpperCAmelCase_          =							self.transformer_dir
        shutil.copy(
            os.path.join(_snake_case					,						'''src/transformers/models/bert/modeling_bert.py''')					,						os.path.join(self.transformer_dir					,						'''models/bert/modeling_bert.py''')					,						)







   def lowerCamelCase						(      self		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							'''src/transformers'''
        shutil.rmtree(self.transformer_dir)







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Any					,						_snake_case		:						Any					,						_snake_case		:						List[Any]					,						_snake_case		:						List[str]=None):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
        self.assertEqual(_snake_case					,						_snake_case)







   def lowerCamelCase						(      self		:						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'''					,						_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'''					,						_snake_case)					,						)

        # Copy consistency with a really long name
        UpperCAmelCase_          =							'''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'''					,						_snake_case					,						_snake_case)					,						)

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







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


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

        UpperCAmelCase_          =							(
            '''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.'''
        )
        UpperCAmelCase_          =							(
            '''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'''
        )
        UpperCAmelCase_          =							(
            '''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'''
        )

        UpperCAmelCase_     ,       UpperCAmelCase_          =							check_copies.convert_to_localized_md(
            _snake_case					,						_snake_case					,						localized_readme['''format_model_list'''])

        self.assertFalse(_snake_case)
        self.assertEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_     ,       UpperCAmelCase_          =							check_copies.convert_to_localized_md(
            _snake_case					,						_snake_case					,						localized_readme['''format_model_list'''])

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

        UpperCAmelCase_          =							(
            '''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.'''
        )
        UpperCAmelCase_          =							(
            '''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'''
        )
        UpperCAmelCase_          =							(
            '''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'''
        )

        UpperCAmelCase_     ,       UpperCAmelCase_          =							check_copies.convert_to_localized_md(
            _snake_case					,						_snake_case					,						localized_readme['''format_model_list'''])

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












import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class __snake_case			(			a ,  unittest.TestCase		):
   UpperCAmelCase__  :    Any          =  PhobertTokenizer
   UpperCAmelCase__  :    List[str]          =  False







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase_          =							['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
        UpperCAmelCase_          =							dict(zip(_snake_case					,						range(len(_snake_case))))
        UpperCAmelCase_          =							['''#version: 0.2''', '''l à</w>''']
        UpperCAmelCase_          =							{'''unk_token''': '''<unk>'''}

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''merges_file'''])

        with open(self.vocab_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             for token in vocab_tokens:
                  fp.write(F"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             fp.write('''\n'''.join(_snake_case))







   def lowerCamelCase						(      self		:						int					,						**_snake_case		:						Any):

        """simple docstring"""


        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname					,						**_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							PhobertTokenizer(self.vocab_file					,						self.merges_file					,						**self.special_tokens_map)
        UpperCAmelCase_          =							'''Tôi là VinAI Research'''
        UpperCAmelCase_          =							'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        UpperCAmelCase_          =							tokenizer.tokenize(_snake_case)
        print(_snake_case)
        self.assertListEqual(_snake_case					,						_snake_case)

        UpperCAmelCase_          =							tokens + [tokenizer.unk_token]

        UpperCAmelCase_          =							[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case)					,						_snake_case)






