'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowercase        =    logging.get_logger(__name__)


__lowercase        =    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all BART models at https://huggingface.co/models?filter=bart
__lowercase        =    {
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json""",
    },
}

__lowercase        =    {
    """facebook/bart-base""": 1_0_2_4,
    """facebook/bart-large""": 1_0_2_4,
    """facebook/bart-large-mnli""": 1_0_2_4,
    """facebook/bart-large-cnn""": 1_0_2_4,
    """facebook/bart-large-xsum""": 1_0_2_4,
    """yjernite/bart_eli5""": 1_0_2_4,
}






class 							a__(   __snake_case      ):


			'''simple docstring'''






			UpperCAmelCase_     :	Optional[int]						=			VOCAB_FILES_NAMES
			UpperCAmelCase_     :	int						=			PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_     :	Optional[int]						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCAmelCase_     :	Union[str, Any]						=			['input_ids', 'attention_mask']
			UpperCAmelCase_     :	Optional[int]						=			BartTokenizer



			def __init__(  self			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase=None			,       __lowerCAmelCase="replace"			,       __lowerCAmelCase="<s>"			,       __lowerCAmelCase="</s>"			,       __lowerCAmelCase="</s>"			,       __lowerCAmelCase="<s>"			,       __lowerCAmelCase="<unk>"			,       __lowerCAmelCase="<pad>"			,       __lowerCAmelCase="<mask>"			,       __lowerCAmelCase=False			,       __lowerCAmelCase=True			,       **__lowerCAmelCase			,       ):




					"""simple docstring"""


					super().__init__(
					    A_			,       A_			,       tokenizer_file=A_			,       errors=A_			,       bos_token=A_			,       eos_token=A_			,       sep_token=A_			,       cls_token=A_			,       unk_token=A_			,       pad_token=A_			,       mask_token=A_			,       add_prefix_space=A_			,       trim_offsets=A_			,       **A_			,       )

					lowerCAmelCase     =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
					if pre_tok_state.get("""add_prefix_space"""			,       A_) != add_prefix_space:
							lowerCAmelCase     =    getattr(A_			,       pre_tok_state.pop("""type"""))
							lowerCAmelCase     =    add_prefix_space
							lowerCAmelCase     =    pre_tok_class(**A_)

					lowerCAmelCase     =    add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					lowerCAmelCase     =    "post_processor"
					lowerCAmelCase     =    getattr(self.backend_tokenizer			,       A_			,       A_)
					if tokenizer_component_instance:
							lowerCAmelCase     =    json.loads(tokenizer_component_instance.__getstate__())

							# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
							if "sep" in state:
									lowerCAmelCase     =    tuple(state["""sep"""])
							if "cls" in state:
									lowerCAmelCase     =    tuple(state["""cls"""])

							lowerCAmelCase     =    False

							if state.get("""add_prefix_space"""			,       A_) != add_prefix_space:
									lowerCAmelCase     =    add_prefix_space
									lowerCAmelCase     =    True

							if state.get("""trim_offsets"""			,       A_) != trim_offsets:
									lowerCAmelCase     =    trim_offsets
									lowerCAmelCase     =    True

							if changes_to_apply:
									lowerCAmelCase     =    getattr(A_			,       state.pop("""type"""))
									lowerCAmelCase     =    component_class(**A_)
									setattr(self.backend_tokenizer			,       A_			,       A_)



			@property
			def 				a_			(  self):




					"""simple docstring"""


					if self._mask_token is None:
							if self.verbose:
									logger.error("""Using mask_token, but it is not set yet.""")
							return None
					return str(self._mask_token)



			@mask_token.setter
			def 				a_			(  self			,       __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    AddedToken(A_			,       lstrip=A_			,       rstrip=A_) if isinstance(A_			,       A_) else value
					lowerCAmelCase     =    value



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    kwargs.get("""is_split_into_words"""			,       A_)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    """to use it with pretokenized inputs.""")

					return super()._batch_encode_plus(*A_			,       **A_)



			def 				a_			(  self			,       *__lowerCAmelCase			,       **__lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase     =    kwargs.get("""is_split_into_words"""			,       A_)

					if is_split_into_words and not self.add_prefix_space:
							raise ValueError(
							    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
							    """to use it with pretokenized inputs.""")

					return super()._encode_plus(*A_			,       **A_)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase     =    self._tokenizer.model.save(A_			,       name=A_)
					return tuple(A_)



			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase=None):




					"""simple docstring"""


					lowerCAmelCase     =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
					if token_ids_a is None:
							return output

					return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







			def 				a_			(  self			,       __lowerCAmelCase			,       __lowerCAmelCase = None):




					"""simple docstring"""


					lowerCAmelCase     =    [self.sep_token_id]
					lowerCAmelCase     =    [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]











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

import os
from pathlib import Path



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase	)		->					str:
 UpperCamelCase      :   Union[str, Any]			      =  {
     "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      :   List[str]			      =  {
     "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      :   str			      =  F"""{src_lang}-{tgt_lang}"""

 UpperCamelCase      :   Dict			      =  F"""
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"
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

- 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)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR's WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

"""
 os.makedirs(_lowerCAmelCase				,  exist_ok=_lowerCAmelCase	)
 UpperCamelCase      :   Union[str, Any]			      =  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
__lowerCamelCase			:	str					    =	Path(__file__).resolve().parent.parent.parent
__lowerCamelCase			:	Optional[Any]					    =	repo_dir / """model_cards"""

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