
"""simple docstring"""




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from ..image_utils import load_image


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

A__ :       Optional[Any]				= logging.get_logger(__name__)


A__ :       Union[str, Any]				= Dict[str, Any]
A__ :       List[Any]				= List[Prediction]


@add_end_docstrings(SCREAMING_SNAKE_CASE__			)
class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       def __init__(  self			,					*A_			,					**A_							)  ->						Union[str, Any]:





         """simple docstring"""




         super().__init__(*A_			,					**A_							)

         if self.framework == "tf":
           raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''							)

         requires_backends(self			,					'''vision'''							)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()							)							)
       def 				lowercase_				(  self			,					**A_							)  ->						int:





         """simple docstring"""




         _lowercase:      List[str]   			=  {}
         if "threshold" in kwargs:
           _lowercase:      List[Any]   			=  kwargs['''threshold''']
         return {}, {}, postprocess_kwargs
       def __call__(  self			,					*A_			,					**A_							)  ->						Union[Predictions, List[Prediction]]:





         """simple docstring"""




         return super().__call__(*A_			,					**A_							)
       def 				lowercase_				(  self			,					A_							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      List[Any]   			=  load_image(A_							)
         _lowercase:      Tuple   			=  torch.IntTensor([[image.height, image.width]]							)
         _lowercase:      Optional[Any]   			=  self.image_processor(images=[image]			,					return_tensors='''pt'''							)
         if self.tokenizer is not None:
           _lowercase:      List[Any]   			=  self.tokenizer(text=inputs['''words''']			,					boxes=inputs['''boxes''']			,					return_tensors='''pt'''							)
         _lowercase:      Any   			=  target_size
         return inputs
       def 				lowercase_				(  self			,					A_							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Any   			=  model_inputs.pop('''target_size'''							)
         _lowercase:      List[str]   			=  self.model(**A_							)
         _lowercase:      str   			=  outputs.__class__({'''target_size''': target_size, **outputs}							)
         if self.tokenizer is not None:
           _lowercase:      Optional[int]   			=  model_inputs['''bbox''']
         return model_outputs
       def 				lowercase_				(  self			,					A_			,					A_=0.9							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  model_outputs['''target_size''']
         if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           _lowercase			,					_lowercase:      Any   			=  target_size[0].tolist()

           def unnormalize(A_							):
             return self._get_bounding_box(
                 torch.Tensor(
                     [
                         (width * bbox[0] / 1000),
                         (height * bbox[1] / 1000),
                         (width * bbox[2] / 1000),
                         (height * bbox[3] / 1000),
                     ]							)							)

           _lowercase			,					_lowercase:      int   			=  model_outputs['''logits'''].squeeze(0							).softmax(dim=-1							).max(dim=-1							)
           _lowercase:      int   			=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           _lowercase:      Optional[Any]   			=  [unnormalize(A_							) for bbox in model_outputs['''bbox'''].squeeze(0							)]
           _lowercase:      Optional[int]   			=  ['''score''', '''label''', '''box''']
           _lowercase:      Tuple   			=  [dict(zip(A_			,					A_							)							) for vals in zip(scores.tolist()			,					A_			,					A_							) if vals[0] > threshold]
         else:
           # This is a regular ForObjectDetectionModel
           _lowercase:      List[str]   			=  self.image_processor.post_process_object_detection(A_			,					A_			,					A_							)
           _lowercase:      Optional[Any]   			=  raw_annotations[0]
           _lowercase:      Tuple   			=  raw_annotation['''scores''']
           _lowercase:      Dict   			=  raw_annotation['''labels''']
           _lowercase:      Any   			=  raw_annotation['''boxes''']

           _lowercase:      Tuple   			=  scores.tolist()
           _lowercase:      str   			=  [self.model.config.idalabel[label.item()] for label in labels]
           _lowercase:      Optional[Any]   			=  [self._get_bounding_box(A_							) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           _lowercase:      Optional[Any]   			=  ['''score''', '''label''', '''box''']
           _lowercase:      Optional[Any]   			=  [
               dict(zip(A_			,					A_							)							)
               for vals in zip(raw_annotation['''scores''']			,					raw_annotation['''labels''']			,					raw_annotation['''boxes''']							)
           ]

         return annotation



       def 				lowercase_				(  self			,					A_							)  ->						Dict[str, int]:





         """simple docstring"""




         if self.framework != "pt":
           raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''							)
         _lowercase			,					_lowercase			,					_lowercase			,					_lowercase:      Dict   			=  box.int().tolist()
         _lowercase:      Optional[Any]   			=  {
             '''xmin''': xmin,
             '''ymin''': ymin,
             '''xmax''': xmax,
             '''ymax''': ymax,
         }
         return bbox


"""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)
