


'''simple docstring'''




import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_a   :   Optional[int]												=   False
_a   :   Dict												=   False





def   _a		(lowercase__			:   Namespace						)			->			int:






     """simple docstring"""
     return TrainCommand(lowercase__						)




class 			_lowercase (	__lowercase							):
   @staticmethod
   def a  (						SCREAMING_SNAKE_CASE_       :      ArgumentParser      ) ->							Any:
        __snake_case           =						parser.add_parser('train'       ,   help='CLI tool to train a model on a task.'      )

        train_parser.add_argument(
            '--train_data'       ,   type=SCREAMING_SNAKE_CASE_       ,   required=SCREAMING_SNAKE_CASE_       ,   help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'       ,   )
        train_parser.add_argument(
            '--column_label'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=0       ,   help='Column of the dataset csv file with example labels.'      )
        train_parser.add_argument(
            '--column_text'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=1       ,   help='Column of the dataset csv file with example texts.'      )
        train_parser.add_argument(
            '--column_id'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=2       ,   help='Column of the dataset csv file with example ids.'      )
        train_parser.add_argument(
            '--skip_first_row'       ,   action='store_true'       ,   help='Skip the first row of the csv file (headers).'      )

        train_parser.add_argument('--validation_data'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=''       ,   help='path to validation dataset.'      )
        train_parser.add_argument(
            '--validation_split'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=0.1       ,   help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'       ,   )

        train_parser.add_argument('--output'       ,   type=SCREAMING_SNAKE_CASE_       ,   default='./'       ,   help='path to saved the trained model.'      )

        train_parser.add_argument(
            '--task'       ,   type=SCREAMING_SNAKE_CASE_       ,   default='text_classification'       ,   help='Task to train the model on.'      )
        train_parser.add_argument(
            '--model'       ,   type=SCREAMING_SNAKE_CASE_       ,   default='bert-base-uncased'       ,   help='Model\'s name or path to stored model.'      )
        train_parser.add_argument('--train_batch_size'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=32       ,   help='Batch size for training.'      )
        train_parser.add_argument('--valid_batch_size'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=64       ,   help='Batch size for validation.'      )
        train_parser.add_argument('--learning_rate'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=3e-5       ,   help='Learning rate.'      )
        train_parser.add_argument('--adam_epsilon'       ,   type=SCREAMING_SNAKE_CASE_       ,   default=1e-08       ,   help='Epsilon for Adam optimizer.'      )
        train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_      )
   def __init__(						self       :      str       ,   SCREAMING_SNAKE_CASE_       :      Namespace      ) ->							Optional[int]:
        __snake_case           =						logging.get_logger('transformers-cli/training'      )

        __snake_case           =						'tf' if is_tf_available() else 'torch'

        os.makedirs(args.output       ,   exist_ok=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						args.output

        __snake_case           =						args.column_label
        __snake_case           =						args.column_text
        __snake_case           =						args.column_id

        self.logger.info(f'Loading {args.task} pipeline for {args.model}'      )
        if args.task == "text_classification":
             __snake_case           =						TextClassificationPipeline.from_pretrained(args.model      )
        elif args.task == "token_classification":
             raise NotImplementedError
        elif args.task == "question_answering":
             raise NotImplementedError

        self.logger.info(f'Loading dataset from {args.train_data}'      )
        __snake_case           =						Processor.create_from_csv(
            args.train_data       ,   column_label=args.column_label       ,   column_text=args.column_text       ,   column_id=args.column_id       ,   skip_first_row=args.skip_first_row       ,   )
        __snake_case           =						None
        if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'      )
             __snake_case           =						Processor.create_from_csv(
                 args.validation_data       ,   column_label=args.column_label       ,   column_text=args.column_text       ,   column_id=args.column_id       ,   skip_first_row=args.skip_first_row       ,   )

        __snake_case           =						args.validation_split
        __snake_case           =						args.train_batch_size
        __snake_case           =						args.valid_batch_size
        __snake_case           =						args.learning_rate
        __snake_case           =						args.adam_epsilon
   def a  (						self       :      Optional[int]      ) ->							int:
        if self.framework == "tf":
             return self.run_tf()
        return self.run_torch()
   def a  (						self       :      Tuple      ) ->							List[str]:
        raise NotImplementedError





   def a  (						self       :      Dict      ) ->							Optional[int]:
        self.pipeline.fit(
            self.train_dataset       ,   validation_data=self.valid_dataset       ,   validation_split=self.validation_split       ,   learning_rate=self.learning_rate       ,   adam_epsilon=self.adam_epsilon       ,   train_batch_size=self.train_batch_size       ,   valid_batch_size=self.valid_batch_size       ,   )

        # Save trained pipeline
        self.pipeline.save_pretrained(self.output      )






"""simple docstring"""




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

import os
from pathlib import Path




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  {
      '''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]
  _lowercase:      Dict   			=  {
      '''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'''],
  }
  _lowercase:      Optional[Any]   			=  f'''{src_lang}-{tgt_lang}'''

  _lowercase:      Tuple   			=  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(_UpperCamelCase			,					exist_ok=_UpperCamelCase    )
  _lowercase:      List[str]   			=  os.path.join(_UpperCamelCase			,					'''README.md'''    )
  print(f'''Generating {path}'''    )
  with open(_UpperCamelCase			,					'''w'''			,					encoding='''utf-8'''    ) as f:
    f.write(_UpperCamelCase    )


# make sure we are under the root of the project
A__ :       List[str]				= Path(__file__).resolve().parent.parent.parent
A__ :       Optional[int]				= repo_dir / 'model_cards'

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