



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

import black


SCREAMING_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.
SCREAMING_SNAKE_CASE__       =      '''    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     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    tempfile.mkdtemp()
								os.makedirs(os.path.join(self.transformer_dir				,					'models/bert/'					)					)
								A__       =    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     _a							(     self					:       Union[str, Any]					):






								"""simple docstring"""


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



					def     _a							(     self					:       List[str]				,					_snake_case					:       Tuple				,					_snake_case					:       int				,					_snake_case					:       Tuple				,					_snake_case					:       int=None					):






								"""simple docstring"""


								A__       =    comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
								if overwrite_result is not None:
											A__       =    comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
								A__       =    black.Mode(target_versions={black.TargetVersion.PYaa}				,					line_length=1_19					)
								A__       =    black.format_str(_snake_case				,					mode=_snake_case					)
								A__       =    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     _a							(     self					:       str					):






								"""simple docstring"""


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



					def     _a							(     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'				,					_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
								A__       =    '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     _a							(     self					:       List[str]					):






								"""simple docstring"""


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

								A__       =    (
								    '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__       =    (
								    '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__       =    (
								    '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__       =    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					)

								A__     , A__       =    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					)

								A__       =    (
								    '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__       =    (
								    '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__       =    (
								    '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__       =    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
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




