



"""simple docstring"""






import os
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


_lowercase			:			str										=						logging.get_logger(__name__)

_lowercase			:			Optional[Any]										=						"▁"

_lowercase			:			Any										=						{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

_lowercase			:			Union[str, Any]										=						{
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

_lowercase			:			str										=						{"vinai/bartpho-syllable": 1_0_2_4}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							VOCAB_FILES_NAMES
					_a             =							PRETRAINED_VOCAB_FILES_MAP
					_a             =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a             =							['input_ids', 'attention_mask']



					def __init__(	self							:  str,  lowerCamelCase							:  Dict,  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Optional[Any]="<s>",  lowerCamelCase							:  List[Any]="</s>",  lowerCamelCase							:  Optional[int]="</s>",  lowerCamelCase							:  int="<s>",  lowerCamelCase							:  List[Any]="<unk>",  lowerCamelCase							:  Dict="<pad>",  lowerCamelCase							:  Tuple="<mask>",  lowerCamelCase							:  Optional[Dict[str, Any]] = None,  **lowerCamelCase							:  Dict,  )-> None:
								# Mask token behave like a normal word, i.e. include the space before it
								lowerCamelCase__		:		str		     =AddedToken(lowerCamelCase,  lstrip=lowerCamelCase,  rstrip=lowerCamelCase					) if isinstance(lowerCamelCase,  lowerCamelCase					) else mask_token

								lowerCamelCase__		:		int		     ={} if sp_model_kwargs is None else sp_model_kwargs

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

								lowerCamelCase__		:		Optional[Any]		     =vocab_file
								lowerCamelCase__		:		Any		     =monolingual_vocab_file
								lowerCamelCase__		:		str		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.Load(str(lowerCamelCase					)					)

								# Load the reduced vocab

								# Keep order of special tokens for backward compatibility
								lowerCamelCase__		:		Any		     ={}
								lowerCamelCase__		:		Optional[Any]		     =0
								for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
											if str(lowerCamelCase					) not in self.fairseq_tokens_to_ids:
														lowerCamelCase__		:		Optional[int]		     =cnt
														cnt += 1
								with open(lowerCamelCase,  '''r''',  encoding='''utf-8'''					) as f:
											for line in f.readlines():
														lowerCamelCase__		:		List[str]		     =line.strip().split()[0]
														lowerCamelCase__		:		Dict		     =len(self.fairseq_tokens_to_ids					)
								if str(lowerCamelCase					) not in self.fairseq_tokens_to_ids:
											lowerCamelCase__		:		Tuple		     =len(self.fairseq_tokens_to_ids					)

								lowerCamelCase__		:		str		     ={v: k for k, v in self.fairseq_tokens_to_ids.items()}



					def __getstate__(	self							:  List[str]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     =self.__dict__.copy()
								lowerCamelCase__		:		Optional[int]		     =None
								lowerCamelCase__		:		int		     =self.sp_model.serialized_model_proto()
								return state



					def __setstate__(	self							:  List[Any],  lowerCamelCase							:  Tuple					)-> Any:
								lowerCamelCase__		:		Optional[int]		     =d

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

								lowerCamelCase__		:		str		     =spm.SentencePieceProcessor(**self.sp_model_kwargs					)
								self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:

								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowerCamelCase__		:		Optional[Any]		     =[self.cls_token_id]
								lowerCamelCase__		:		Optional[int]		     =[self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep



					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None,  lowerCamelCase							:  bool = 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 [1] + ([0] * len(lowerCamelCase					)) + [1]
								return [1] + ([0] * len(lowerCamelCase					)) + [1, 1] + ([0] * len(lowerCamelCase					)) + [1]



					def 		snake_case	(	self							:  Dict,  lowerCamelCase							:  List[int],  lowerCamelCase							:  Optional[List[int]] = None					)-> List[int]:
								lowerCamelCase__		:		Optional[Any]		     =[self.sep_token_id]
								lowerCamelCase__		:		List[Any]		     =[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 		snake_case	(	self							:  int					)-> Optional[int]:
								return len(self.fairseq_ids_to_tokens					)



					def 		snake_case	(	self							:  Optional[int]					)-> Optional[int]:
								lowerCamelCase__		:		Optional[Any]		     ={self.convert_ids_to_tokens(lowerCamelCase					): i for i in range(self.vocab_size					)}
								vocab.update(self.added_tokens_encoder					)
								return vocab



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  str					)-> List[str]:
								return self.sp_model.encode(lowerCamelCase,  out_type=lowerCamelCase					)



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  Optional[Any]					)-> List[str]:
								if token in self.fairseq_tokens_to_ids:
											return self.fairseq_tokens_to_ids[token]
								else:
											return self.unk_token_id



					def 		snake_case	(	self							:  Any,  lowerCamelCase							:  Optional[int]					)-> Optional[int]:
								return self.fairseq_ids_to_tokens[index]



					def 		snake_case	(	self							:  int,  lowerCamelCase							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		List[str]		     =''''''.join(lowerCamelCase					).replace(lowerCamelCase,  ''' '''					).strip()
								return out_string




					def 		snake_case	(	self							:  Union[str, Any],  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None					)-> Tuple[str]:
								if not os.path.isdir(lowerCamelCase					):
											logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''					)
											return
								lowerCamelCase__		:		Dict		     =os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
								lowerCamelCase__		:		Tuple		     =os.path.join(
								    lowerCamelCase,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
														lowerCamelCase__		:		Any		     =self.sp_model.serialized_model_proto()
														fi.write(lowerCamelCase					)

								if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
								    lowerCamelCase					) and os.path.isfile(self.monolingual_vocab_file					):
											copyfile(self.monolingual_vocab_file,  lowerCamelCase					)
								elif not os.path.isfile(self.monolingual_vocab_file					):
											with open(lowerCamelCase,  '''w''',  encoding='''utf-8'''					) as fp:
														for token in self.fairseq_tokens_to_ids:
																	if token not in self.all_special_tokens:
																				fp.write(F'''{str(lowerCamelCase					)} \n'''					)

								return out_vocab_file, out_monolingual_vocab_file









"""simple docstring"""






# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Union[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     ={
			    '''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]
			lowerCamelCase__		:		Optional[Any]		     ={
			    '''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],
			}
			lowerCamelCase__		:		Any		     =f'''{src_lang}-{tgt_lang}'''

			lowerCamelCase__		:		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=__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
			lowerCamelCase__		:		str		     =os.path.join(__lowerCamelCase							,		'''README.md'''     )
			print(f'''Generating {path}'''     )
			with open(__lowerCamelCase							,		'''w'''							,		encoding='''utf-8'''     ) as f:
						f.write(__lowerCamelCase     )


# make sure we are under the root of the project
_lowercase			:			List[str]										=						Path(__file__).resolve().parent.parent.parent
_lowercase			:			Dict										=						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"]:
		_lowercase			:			int										=						model_cards_dir / "allenai" / model_name
		write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)




