'''simple docstring'''

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

import black


__a:			str	       =       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:			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       UpperCAmelCase  (						unittest.TestCase ):





       '''simple docstring'''



       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   List[str]						= tempfile.mkdtemp()
        os.makedirs(os.path.join(self.transformer_dir     ,       '''models/bert/'''			)			)
        lowercase__    :   Tuple						= self.transformer_dir
        shutil.copy(
            os.path.join(__lowerCAmelCase     ,       '''src/transformers/models/bert/modeling_bert.py'''			)     ,       os.path.join(self.transformer_dir     ,       '''models/bert/modeling_bert.py'''			)     ,       )

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Dict						= '''src/transformers'''
        shutil.rmtree(self.transformer_dir			)

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

       def _lowerCAmelCase(						self			)  ->  Optional[int]:
        lowercase__    :   Tuple						= check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead'''			)
        self.assertEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

       def _lowerCAmelCase(						self			)  ->  List[str]:
        # 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'''     ,       __lowerCAmelCase     ,       )

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

        # Copy consistency with a really long name
        lowercase__    :   Any						= '''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'''     ,       __lowerCAmelCase     ,       __lowerCAmelCase			)     ,       )

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


       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   Tuple						= check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

        lowercase__    :   Tuple						= (
            '''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.'''
        )
        lowercase__    :   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'''
        )
        lowercase__    :   str						= (
            '''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'''
        )

        lowercase__			,							lowercase__    :   Dict						= check_copies.convert_to_localized_md(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       localized_readme['''format_model_list''']			)

        self.assertFalse(__lowerCAmelCase			)
        self.assertEqual(__lowerCAmelCase     ,       __lowerCAmelCase			)

        lowercase__			,							lowercase__    :   Any						= check_copies.convert_to_localized_md(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       localized_readme['''format_model_list''']			)

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

        lowercase__    :   Dict						= (
            '''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.'''
        )
        lowercase__    :   Optional[int]						= (
            '''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'''
        )
        lowercase__    :   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'''
        )

        lowercase__			,							lowercase__    :   Optional[int]						= check_copies.convert_to_localized_md(
            __lowerCAmelCase     ,       __lowerCAmelCase     ,       localized_readme['''format_model_list''']			)

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


'''simple docstring'''

import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a:			List[str]	       =       logging.get_logger(__name__)

__a:			int	       =       """▁"""

__a:			Optional[int]	       =       {"""vocab_file""": """prophetnet.tokenizer"""}

__a:			Optional[int]	       =       {
    """vocab_file""": {
        """microsoft/xprophetnet-large-wiki100-cased""": (
            """https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"""
        ),
    }
}

__a:			List[str]	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": {"""do_lower_case""": False},
}

__a:			Tuple	       =       {
    """microsoft/xprophetnet-large-wiki100-cased""": 5_12,
}
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= collections.OrderedDict()
 with open(UpperCAmelCase		,					'''r'''		,					encoding='''utf-8'''					) as reader:
  lowercase__    :   List[Any]						= reader.readlines()
 for index, token in enumerate(UpperCAmelCase					):
  lowercase__    :   List[Any]						= token.rstrip('''\n'''					)
  lowercase__    :   Tuple						= index
 return vocab





class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE 		=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE 		=   ["input_ids", "attention_mask"]

       def __init__(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[SEP]"     ,       __lowerCAmelCase="[UNK]"     ,       __lowerCAmelCase="[PAD]"     ,       __lowerCAmelCase="[CLS]"     ,       __lowerCAmelCase="[MASK]"     ,       __lowerCAmelCase = None     ,       **__lowerCAmelCase     ,       )  ->  None:
        lowercase__    :   Dict						= {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=__lowerCAmelCase     ,       eos_token=__lowerCAmelCase     ,       sep_token=__lowerCAmelCase     ,       unk_token=__lowerCAmelCase     ,       pad_token=__lowerCAmelCase     ,       cls_token=__lowerCAmelCase     ,       mask_token=__lowerCAmelCase     ,       sp_model_kwargs=self.sp_model_kwargs     ,       **__lowerCAmelCase     ,       )

        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        lowercase__    :   str						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(str(__lowerCAmelCase			)			)
        lowercase__    :   Optional[int]						= vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # put special tokens and [unused] tokens into the vocab
        lowercase__    :   str						= {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

        for i in range(10			):
         lowercase__    :   Tuple						= F"""[unused{i}]"""
         lowercase__    :   List[Any]						= 5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase__    :   Optional[Any]						= 12
        lowercase__    :   Tuple						= {v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
         self.unique_no_split_tokens.append(__lowerCAmelCase			)

       def __getstate__(						self			)  ->  Union[str, Any]:
        lowercase__    :   Dict						= self.__dict__.copy()
        lowercase__    :   Optional[Any]						= None
        return state

       def __setstate__(						self     ,       __lowerCAmelCase			)  ->  Dict:
        lowercase__    :   Any						= d
        try:
         import sentencepiece as spm
        except ImportError:
         logger.warning(
             '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
             ''' pip install sentencepiece'''			)
         raise

        # for backward compatibility
        if not hasattr(self     ,       '''sp_model_kwargs'''			):
         lowercase__    :   List[Any]						= {}

        lowercase__    :   int						= spm.SentencePieceProcessor(**self.sp_model_kwargs			)
        self.sp_model.Load(self.vocab_file			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None     ,       __lowerCAmelCase = False			)  ->  List[int]:

        if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=__lowerCAmelCase     ,       token_ids_a=__lowerCAmelCase     ,       already_has_special_tokens=__lowerCAmelCase			)

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

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:
        lowercase__    :   List[str]						= [self.sep_token_id]

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

       @property
       def _lowerCAmelCase(						self			)  ->  List[Any]:
        return len(self.sp_model			) + self.fairseq_offset

       def _lowerCAmelCase(						self			)  ->  List[str]:
        lowercase__    :   str						= {self.convert_ids_to_tokens(__lowerCAmelCase			): i for i in range(self.vocab_size			)}
        vocab.update(self.added_tokens_encoder			)
        return vocab

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        return self.sp_model.encode(__lowerCAmelCase     ,       out_type=__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
        lowercase__    :   Tuple						= self.sp_model.PieceToId(__lowerCAmelCase			)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  str:
        if index in self.fairseq_ids_to_tokens:
         return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Union[str, Any]:
        lowercase__    :   Optional[Any]						= ''''''.join(__lowerCAmelCase			).replace(__lowerCAmelCase     ,       ''' '''			).strip()
        return out_string

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  Tuple[str]:
        if not os.path.isdir(__lowerCAmelCase			):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
         return
        lowercase__    :   str						= os.path.join(
            __lowerCAmelCase     ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

        if os.path.abspath(self.vocab_file			) != os.path.abspath(__lowerCAmelCase			) and os.path.isfile(self.vocab_file			):
         copyfile(self.vocab_file     ,       __lowerCAmelCase			)
        elif not os.path.isfile(self.vocab_file			):
         with open(__lowerCAmelCase     ,       '''wb'''			) as fi:
          lowercase__    :   List[Any]						= self.sp_model.serialized_model_proto()
          fi.write(__lowerCAmelCase			)

        return (out_vocab_file,)


       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase = None			)  ->  List[int]:

        if token_ids_a is None:
         return token_ids_a + [self.sep_token_id]
        lowercase__    :   str						= [self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep

