






from typing import Dict, List, Optional, Tuple

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


_a				     =  logging.get_logger(__name__)





class __lowerCamelCase			(				snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=				["input_ids", "attention_mask"]

	def __init__(							self	,   UpperCAmelCase="[PAD]"	,   UpperCAmelCase="[BOS]"	,   UpperCAmelCase="[EOS]"	,   UpperCAmelCase="[MASK]"	,   UpperCAmelCase="[CLS]"	,   UpperCAmelCase="[SEP]"	,   UpperCAmelCase=2048	,   **UpperCAmelCase	,   ):
							"""simple docstring"""







							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else pad_token
							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else bos_token
							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else eos_token
							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else mask_token
							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else cls_token
							_UpperCAmelCase     =      AddedToken(UpperCAmelCase	,   lstrip=UpperCAmelCase	,   rstrip=UpperCAmelCase   ) if isinstance(UpperCAmelCase	,   UpperCAmelCase   ) else sep_token

							super().__init__(
							    pad_token=UpperCAmelCase	,   bos_token=UpperCAmelCase	,   eos_token=UpperCAmelCase	,   mask_token=UpperCAmelCase	,   cls_token=UpperCAmelCase	,   sep_token=UpperCAmelCase	,   model_max_length=UpperCAmelCase	,   **UpperCAmelCase	,   )

							_UpperCAmelCase     =      2**8  # utf is 8 bits

							# define special tokens dict
							_UpperCAmelCase     =      {
							    self.pad_token: 0,
							    self.bos_token: 1,
							    self.eos_token: 2,
							    self.mask_token: 3,
							    self.cls_token: 4,
							    self.sep_token: 5,
							}
							_UpperCAmelCase     =      len(self.special_tokens_encoder   )
							_UpperCAmelCase     =      {v: k for k, v in self.special_tokens_encoder.items()}

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      self.special_tokens_encoder.copy()
							vocab.update(self.added_tokens_encoder   )
							for i in range(self._utf_vocab_size   ):
													_UpperCAmelCase     =      chr(UpperCAmelCase   )
													_UpperCAmelCase     =      i + len(self.special_tokens_encoder   )
							return vocab

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._utf_vocab_size + self._num_special_tokens

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None	,   UpperCAmelCase = False   ):
							"""simple docstring"""







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

							# normal case: some special tokens
							if token_ids_a is None:
													return [1] + [0] * len(UpperCAmelCase   ) + [1]
							return [1] + ([0] * len(UpperCAmelCase   )) + [1] + ([0] * len(UpperCAmelCase   )) + [1]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							else:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      [chr(UpperCAmelCase   ) for i in text.encode('utf-8'   )]
							return tokens

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							if token in self.special_tokens_encoder:
													_UpperCAmelCase     =      self.special_tokens_encoder[token]
							elif token in self.added_tokens_encoder:
													_UpperCAmelCase     =      self.added_tokens_encoder[token]
							elif len(UpperCAmelCase   ) != 1:
													_UpperCAmelCase     =      self.unk_token_id
							else:
													_UpperCAmelCase     =      ord(UpperCAmelCase   ) + self._num_special_tokens
							return token_id

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							if index in self.special_tokens_decoder:
													_UpperCAmelCase     =      self.special_tokens_decoder[index]
							elif index in self.added_tokens_decoder:
													_UpperCAmelCase     =      self.added_tokens_decoder[index]
							else:
													_UpperCAmelCase     =      chr(index - self._num_special_tokens   )
							return token

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      b''
							for token in tokens:
													if token in self.special_tokens_decoder:
																			_UpperCAmelCase     =      self.special_tokens_decoder[token].encode('utf-8'   )
													elif token in self.added_tokens_decoder:
																			_UpperCAmelCase     =      self.special_tokens_decoder[token].encode('utf-8'   )
													elif token in self.special_tokens_encoder:
																			_UpperCAmelCase     =      token.encode('utf-8'   )
													elif token in self.added_tokens_encoder:
																			_UpperCAmelCase     =      token.encode('utf-8'   )
													else:
																			_UpperCAmelCase     =      bytes([ord(UpperCAmelCase   )]   )
													bstring += tok_string
							_UpperCAmelCase     =      bstring.decode('utf-8'	,   errors='replace'   )
							return string

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None   ):
							"""simple docstring"""







							return ()








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

import os
from pathlib import Path


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[Any]:





						"""simple docstring"""



						_UpperCAmelCase     =      {
						    '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]
						_UpperCAmelCase     =      {
						    'wmt19-de-en-6-6-base': [0, 38.37],
						    'wmt19-de-en-6-6-big': [0, 39.90],
						}
						_UpperCAmelCase     =      F"""{src_lang}-{tgt_lang}"""

						_UpperCAmelCase     =      F"""
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


## 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   |  transformers
-------|---------
{model_name}  |  {scores[model_name][1]}

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 wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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     )
						_UpperCAmelCase     =      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
_a				     =  Path(__file__).resolve().parent.parent.parent
_a				     =  repo_dir / '''model_cards'''

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		_a				     =  model_cards_dir / '''allenai''' / model_name
		write_model_card(model_card_dir, src_lang='''de''', tgt_lang='''en''', model_name=model_name)
