





import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

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


__lowercase  :   Optional[Any]						      =logging.get_logger(__name__)

__lowercase  :   Dict						      ={
    """vocab_file""": """vocab.txt""",
    """merges_file""": """bpe.codes""",
}

__lowercase  :   Tuple						      ={
    """vocab_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt""",
    },
    """merges_file""": {
        """vinai/phobert-base""": """https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes""",
        """vinai/phobert-large""": """https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes""",
    },
}

__lowercase  :   Any						      ={
    """vinai/phobert-base""": 256,
    """vinai/phobert-large""": 256,
}




def     a__ (					lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											=set()
   UpperCAmelCase_											=word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)  )
      UpperCAmelCase_											=char

   UpperCAmelCase_											=set(lowercase__  )
   return pairs





class      A       (	__lowercase						):
      _snake_case							     =VOCAB_FILES_NAMES
      _snake_case							     =PRETRAINED_VOCAB_FILES_MAP
      _snake_case							     =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(	self:						Optional[int]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						int="<s>"		,  _lowerCAmelCase:						Any="</s>"		,  _lowerCAmelCase:						Tuple="</s>"		,  _lowerCAmelCase:						Any="<s>"		,  _lowerCAmelCase:						Optional[Any]="<unk>"		,  _lowerCAmelCase:						Union[str, Any]="<pad>"		,  _lowerCAmelCase:						Dict="<mask>"		,  **_lowerCAmelCase:						Union[str, Any]		,  )						->    List[Any]:
         '''simple docstring'''

         super().__init__(
             bos_token=_lowerCAmelCase		,  eos_token=_lowerCAmelCase		,  unk_token=_lowerCAmelCase		,  sep_token=_lowerCAmelCase		,  cls_token=_lowerCAmelCase		,  pad_token=_lowerCAmelCase		,  mask_token=_lowerCAmelCase		,  **_lowerCAmelCase		,  )

         UpperCAmelCase_											=vocab_file
         UpperCAmelCase_											=merges_file

         UpperCAmelCase_											={}
         UpperCAmelCase_											=0
         UpperCAmelCase_											=1
         UpperCAmelCase_											=2
         UpperCAmelCase_											=3

         self.add_from_file(_lowerCAmelCase       )

         UpperCAmelCase_											={v: k for k, v in self.encoder.items()}

         with open(_lowerCAmelCase		,  encoding="utf-8"       ) as merges_handle:
            UpperCAmelCase_											=merges_handle.read().split("\n"       )[:-1]
         UpperCAmelCase_											=[tuple(merge.split()[:-1]       ) for merge in merges]
         UpperCAmelCase_											=dict(zip(_lowerCAmelCase		,  range(len(_lowerCAmelCase       )       )       )       )
         UpperCAmelCase_											={}



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						str		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None		,  _lowerCAmelCase:						bool = False       )						->    List[int]:
         '''simple docstring'''

         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 [1] + ([0] * len(_lowerCAmelCase       )) + [1]
         return [1] + ([0] * len(_lowerCAmelCase       )) + [1, 1] + ([0] * len(_lowerCAmelCase       )) + [1]



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						List[int]		,  _lowerCAmelCase:						Optional[List[int]] = None       )						->    List[int]:
         '''simple docstring'''

         UpperCAmelCase_											=[self.sep_token_id]
         UpperCAmelCase_											=[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 + sep + token_ids_a + sep       ) * [0]



      @property
      def 	lowerCAmelCase__    (	self:						Any       )						->    List[Any]:
         '''simple docstring'''

         return len(self.encoder       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         return dict(self.encoder		,  **self.added_tokens_encoder       )



      def 	lowerCAmelCase__    (	self:						List[str]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         if token in self.cache:
            return self.cache[token]
         UpperCAmelCase_											=tuple(_lowerCAmelCase       )
         UpperCAmelCase_											=tuple(list(word[:-1]       ) + [word[-1] + "</w>"]       )
         UpperCAmelCase_											=get_pairs(_lowerCAmelCase       )

         if not pairs:
            return token

         while True:
            UpperCAmelCase_											=min(_lowerCAmelCase		,  key=lambda _lowerCAmelCase       : self.bpe_ranks.get(_lowerCAmelCase		,  float("inf"       )       )       )
            if bigram not in self.bpe_ranks:
               break
            UpperCAmelCase_						,       UpperCAmelCase_											=bigram
            UpperCAmelCase_											=[]
            UpperCAmelCase_											=0
            while i < len(_lowerCAmelCase       ):
               try:
                  UpperCAmelCase_											=word.index(_lowerCAmelCase		,  _lowerCAmelCase       )
               except ValueError:
                  new_word.extend(word[i:]       )
                  break
               else:
                  new_word.extend(word[i:j]       )
                  UpperCAmelCase_											=j

               if word[i] == first and i < len(_lowerCAmelCase       ) - 1 and word[i + 1] == second:
                  new_word.append(first + second       )
                  i += 2
               else:
                  new_word.append(word[i]       )
                  i += 1
            UpperCAmelCase_											=tuple(_lowerCAmelCase       )
            UpperCAmelCase_											=new_word
            if len(_lowerCAmelCase       ) == 1:
               break
            else:
               UpperCAmelCase_											=get_pairs(_lowerCAmelCase       )
         UpperCAmelCase_											="@@ ".join(_lowerCAmelCase       )
         UpperCAmelCase_											=word[:-4]
         UpperCAmelCase_											=word
         return word



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Tuple       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=[]

         UpperCAmelCase_											=re.findall(r"\S+\n?"		,  _lowerCAmelCase       )

         for token in words:
            split_tokens.extend(list(self.bpe(_lowerCAmelCase       ).split(" "       )       )       )
         return split_tokens



      def 	lowerCAmelCase__    (	self:						Union[str, Any]		,  _lowerCAmelCase:						Dict       )						->    Dict:
         '''simple docstring'''

         return self.encoder.get(_lowerCAmelCase		,  self.encoder.get(self.unk_token       )       )



      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         return self.decoder.get(_lowerCAmelCase		,  self.unk_token       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						List[Any]       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=" ".join(_lowerCAmelCase       ).replace("@@ "		,  ""       ).strip()
         return out_string



      def 	lowerCAmelCase__    (	self:						Tuple		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Optional[str] = None       )						->    Tuple[str]:
         '''simple docstring'''

         if not os.path.isdir(_lowerCAmelCase       ):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'       )
            return
         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )
         UpperCAmelCase_											=os.path.join(
             _lowerCAmelCase		,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]       )

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

         if os.path.abspath(self.merges_file       ) != os.path.abspath(_lowerCAmelCase       ):
            copyfile(self.merges_file		,  _lowerCAmelCase       )

         return out_vocab_file, out_merge_file






      def 	lowerCAmelCase__    (	self:						Optional[int]		,  _lowerCAmelCase:						Optional[int]       )						->    int:
         '''simple docstring'''

         if isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
            try:
               with open(_lowerCAmelCase		,  "r"		,  encoding="utf-8"       ) as fd:
                  self.add_from_file(_lowerCAmelCase       )
            except FileNotFoundError as fnfe:
               raise fnfe
            except UnicodeError:
               raise Exception(F'Incorrect encoding detected in {f}, please rebuild the dataset'       )
            return

         UpperCAmelCase_											=f.readlines()
         for lineTmp in lines:
            UpperCAmelCase_											=lineTmp.strip()
            UpperCAmelCase_											=line.rfind(" "       )
            if idx == -1:
               raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"       )
            UpperCAmelCase_											=line[:idx]
            UpperCAmelCase_											=len(self.encoder       )











# Usage:
# ./gen-card-facebook-wmt19.py

import os
from pathlib import Path




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   UpperCAmelCase_											={
       "en": "Machine learning is great, isn't it?",
       "ru": "Машинное обучение - это здорово, не так ли?",
       "de": "Maschinelles Lernen ist großartig, oder?",
   }

   # BLUE scores as follows:
   # "pair": [fairseq, transformers]
   UpperCAmelCase_											={
       "ru-en": ["[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)", "39.20"],
       "en-ru": ["[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)", "33.47"],
       "en-de": ["[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)", "42.83"],
       "de-en": ["[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)", "41.35"],
   }
   UpperCAmelCase_											=F'{src_lang}-{tgt_lang}'

   UpperCAmelCase_											=F'\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "facebook/wmt19-{src_lang}-{tgt_lang}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation --num_beams $NUM_BEAMS\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n'
   os.makedirs(lowercase__  ,    exist_ok=lowercase__  )
   UpperCAmelCase_											=os.path.join(lowercase__  ,    "README.md"  )
   print(F'Generating {path}'  )
   with open(lowercase__  ,    "w"  ,    encoding="utf-8"  ) as f:
      f.write(lowercase__  )


# make sure we are under the root of the project
__lowercase  :   int						      =Path(__file__).resolve().parent.parent.parent
__lowercase  :   Any						      =repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
     __lowercase      ,			__lowercase      ,			__lowercase  :   Union[str, Any]						      =model_name.split("""-""")
     __lowercase  :   Optional[int]						      =model_cards_dir / """facebook""" / model_name
     write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)




