



"""simple docstring"""
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCAmelCase       :int	=   logging.get_logger(__name__)
_lowerCAmelCase       :List[Any]	=   {"""vocab_file""": """spiece.model"""}

_lowerCAmelCase       :List[str]	=   {
    """vocab_file""": {
        """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
        """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
        """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
        """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
        """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
        """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
        """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
        """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
    }
}

_lowerCAmelCase       :int	=   {
    """albert-base-v1""": 512,
    """albert-large-v1""": 512,
    """albert-xlarge-v1""": 512,
    """albert-xxlarge-v1""": 512,
    """albert-base-v2""": 512,
    """albert-large-v2""": 512,
    """albert-xlarge-v2""": 512,
    """albert-xxlarge-v2""": 512,
}

_lowerCAmelCase       :Dict	=   """▁"""
class 				_UpperCAmelCase				(					a ):





     '''simple docstring'''



     a__   							=VOCAB_FILES_NAMES
     a__   							=PRETRAINED_VOCAB_FILES_MAP
     a__   							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES





     def __init__(   self			,      A			,      A=True			,      A=True			,      A=False			,      A="[CLS]"			,      A="[SEP]"			,      A="<unk>"			,      A="[SEP]"			,      A="<pad>"			,      A="[CLS]"			,      A="[MASK]"			,      A = None			,      **A			,      )				->   None:
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         _UpperCAmelCase				:    int											=       (
             AddedToken(a_			,      lstrip=a_			,      rstrip=a_			,      normalized=a_						)
             if isinstance(a_			,      a_						)
             else mask_token
         )

         _UpperCAmelCase				:    Any											=       {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             do_lower_case=a_			,      remove_space=a_			,      keep_accents=a_			,      bos_token=a_			,      eos_token=a_			,      unk_token=a_			,      sep_token=a_			,      pad_token=a_			,      cls_token=a_			,      mask_token=a_			,      sp_model_kwargs=self.sp_model_kwargs			,      **a_			,      )

         _UpperCAmelCase				:    Dict											=       do_lower_case
         _UpperCAmelCase				:    List[str]											=       remove_space
         _UpperCAmelCase				:    Optional[int]											=       keep_accents
         _UpperCAmelCase				:    Optional[int]											=       vocab_file

         _UpperCAmelCase				:    Tuple											=       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
         self.sp_model.Load(a_						)





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





     def 					__lowerCAmelCase      (   self						)				->   Any:
         _UpperCAmelCase				:    str											=       {self.convert_ids_to_tokens(a_						): i for i in range(self.vocab_size						)}
         vocab.update(self.added_tokens_encoder						)
         return vocab





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





     def __setstate__(   self			,      A						)				->   Optional[int]:
         _UpperCAmelCase				:    int											=       d

         # for backward compatibility
         if not hasattr(self			,      '''sp_model_kwargs'''						):
             _UpperCAmelCase				:    Any											=       {}

         _UpperCAmelCase				:    Dict											=       spm.SentencePieceProcessor(**self.sp_model_kwargs						)
         self.sp_model.Load(self.vocab_file						)





     def 					__lowerCAmelCase      (   self			,      A						)				->   List[str]:
         if self.remove_space:
             _UpperCAmelCase				:    List[Any]											=       " ".join(inputs.strip().split()						)
         else:
             _UpperCAmelCase				:    int											=       inputs
         _UpperCAmelCase				:    Dict											=       outputs.replace('''``'''			,      '''\"'''						).replace('''\'\''''			,      '''\"'''						)

         if not self.keep_accents:
             _UpperCAmelCase				:    Tuple											=       unicodedata.normalize('''NFKD'''			,      a_						)
             _UpperCAmelCase				:    str											=       "".join([c for c in outputs if not unicodedata.combining(a_						)]						)
         if self.do_lower_case:
             _UpperCAmelCase				:    Optional[int]											=       outputs.lower()

         return outputs





     def 					__lowerCAmelCase      (   self			,      A						)				->   List[str]:
         _UpperCAmelCase				:    Union[str, Any]											=       self.preprocess_text(a_						)
         _UpperCAmelCase				:    Any											=       self.sp_model.encode(a_			,      out_type=a_						)
         _UpperCAmelCase				:    int											=       []
         for piece in pieces:
             if len(a_						) > 1 and piece[-1] == str(''','''						) and piece[-2].isdigit():
                 _UpperCAmelCase				:    Dict											=       self.sp_model.EncodeAsPieces(piece[:-1].replace(a_			,      ''''''						)						)
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]						) == 1:
                         _UpperCAmelCase				:    int											=       cur_pieces[1:]
                     else:
                         _UpperCAmelCase				:    Optional[int]											=       cur_pieces[0][1:]
                 cur_pieces.append(piece[-1]						)
                 new_pieces.extend(a_						)
             else:
                 new_pieces.append(a_						)

         return new_pieces





     def 					__lowerCAmelCase      (   self			,      A						)				->   Optional[Any]:
         return self.sp_model.PieceToId(a_						)





     def 					__lowerCAmelCase      (   self			,      A						)				->   Optional[int]:
         return self.sp_model.IdToPiece(a_						)





     def 					__lowerCAmelCase      (   self			,      A						)				->   Union[str, Any]:
         _UpperCAmelCase				:    Any											=       []
         _UpperCAmelCase				:    Dict											=       ""
         _UpperCAmelCase				:    Optional[int]											=       False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(a_						) + token
                 _UpperCAmelCase				:    Optional[Any]											=       True
                 _UpperCAmelCase				:    Any											=       []
             else:
                 current_sub_tokens.append(a_						)
                 _UpperCAmelCase				:    int											=       False
         out_string += self.sp_model.decode(a_						)
         return out_string.strip()





     def 					__lowerCAmelCase      (   self			,      A			,      A = None						)				->   List[int]:
         _UpperCAmelCase				:    List[str]											=       [self.sep_token_id]
         _UpperCAmelCase				:    Union[str, Any]											=       [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 					__lowerCAmelCase      (   self			,      A			,      A = None			,      A = False						)				->   List[int]:

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

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





     def 					__lowerCAmelCase      (   self			,      A			,      A = None						)				->   List[int]:
         _UpperCAmelCase				:    Optional[int]											=       [self.sep_token_id]
         _UpperCAmelCase				:    Dict											=       [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 					__lowerCAmelCase      (   self			,      A			,      A = None						)				->   Tuple[str]:
         if not os.path.isdir(a_						):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
             return
         _UpperCAmelCase				:    List[str]											=       os.path.join(
             a_			,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

         return (out_vocab_file,)






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "en": "Machine learning is great, isn't it?",
	    "ru": "Машинное обучение - это здорово, не так ли?",
	    "de": "Maschinelles Lernen ist großartig, nicht wahr?",
	}

	# BLUE scores as follows:
	# "pair": [fairseq, transformers]
	lowercase						:		Optional[int]   					=		{
	    "wmt16-en-de-dist-12-1": [28.3, 27.52],
	    "wmt16-en-de-dist-6-1": [27.4, 27.11],
	    "wmt16-en-de-12-1": [26.9, 25.75],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		F'''
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.

For more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "allenai/{model_name}"
tokenizer = FSMTTokenizer.from_pretrained(mname)
model = FSMTForConditionalGeneration.from_pretrained(mname)

input = "{texts[src_lang]}"
input_ids = tokenizer.encode(input, return_tensors="pt")
outputs = model.generate(input_ids)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded) # {texts[tgt_lang]}

```

#### Limitations and bias


## Training data

Pretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).

## Eval results

Here are the BLEU scores:

model   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don\'t use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

The score was calculated using this code:

```bash
git clone https://github.com/huggingface/transformers
cd transformers
export PAIR={pair}
export DATA_DIR=data/$PAIR
export SAVE_DIR=data/$PAIR
export BS=8
export NUM_BEAMS=5
mkdir -p $DATA_DIR
sacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target
echo $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py allenai/{model_name} $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
```

## Data Sources

- [training, etc.](http://www.statmt.org/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### BibTeX entry and citation info

```
@misc{{kasai2020deep,
    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},
    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},
    year={{2020}},
    eprint={{2006.10369}},
    archivePrefix={{arXiv}},
    primaryClass={{cs.CL}}
}}
```

'''
	model_card_dir.mkdir(parents=A							,exist_ok=A							)
	lowercase						:		str   					=		os.path.join(A							,"README.md"							)
	print(F'''Generating {path}'''							)
	with open(A							,"w"							,encoding="utf-8"							) as f:
		f.write(A							)


# make sure we are under the root of the project
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				repo_dir / """model_cards"""

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


