






'''simple docstring'''






import os
from pathlib import Path




def        _SCREAMING_SNAKE_CASE  (				A  :       Union[str, Any]  ,		A  :       Any  ,		A  :       Tuple  )    -> Any:
   __snake_case				:      Dict     							= {
       '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]
   __snake_case				:      int     							= {
       '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'],
   }
   __snake_case				:      int     							= F"""{src_lang}-{tgt_lang}"""

   __snake_case				:      List[str]     							= 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(A  ,		exist_ok=A  )
   __snake_case				:      Union[str, Any]     							= os.path.join(A  ,		'README.md'  )
   print(F"""Generating {path}"""  )
   with open(A  ,		'w'  ,		encoding='utf-8'  ) as f:
      f.write(A  )


# 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-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
      __A	,   __A	,   __A    	=   model_name.split('''-''')
      __A    	=   model_cards_dir / '''facebook''' / model_name
      write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)







'''simple docstring'''






import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A    	=   logging.get_logger()




def        _SCREAMING_SNAKE_CASE  (				A  :       int  ,		A  :       str  ,		A  :       LevitConfig  ,		A  :       Path  ,		A  :       bool = True  )    -> Dict:







   """simple docstring"""
   print(F"""Converting {name}..."""  )

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            __snake_case				:      Optional[int]     							= timm.create_model('levit_128s'  ,		pretrained=A  )
         else:
            __snake_case				:      Tuple     							= timm.create_model('levit_128'  ,		pretrained=A  )
      if hidden_sizes == 1_92:
         __snake_case				:      int     							= timm.create_model('levit_192'  ,		pretrained=A  )
      if hidden_sizes == 2_56:
         __snake_case				:      List[Any]     							= timm.create_model('levit_256'  ,		pretrained=A  )
      if hidden_sizes == 3_84:
         __snake_case				:      int     							= timm.create_model('levit_384'  ,		pretrained=A  )

      from_model.eval()
      __snake_case				:      str     							= LevitForImageClassificationWithTeacher(A  ).eval()
      __snake_case				:      int     							= OrderedDict()

      __snake_case				:      Optional[Any]     							= from_model.state_dict()
      __snake_case				:      Tuple     							= list(from_model.state_dict().keys()  )
      __snake_case				:      List[str]     							= list(our_model.state_dict().keys()  )
      print(len(A  )  ,		len(A  )  )
      for i in range(len(A  )  ):
         __snake_case				:      Optional[int]     							= weights[og_keys[i]]
      our_model.load_state_dict(A  )

      __snake_case				:      Tuple     							= torch.randn((2, 3, 2_24, 2_24)  )
      __snake_case				:      Union[str, Any]     							= from_model(A  )
      __snake_case				:      List[str]     							= our_model(A  ).logits

   assert torch.allclose(A  ,		A  ), "The model logits don't match the original one."

   __snake_case				:      int     							= name
   print(A  )

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name  )
      __snake_case				:      int     							= LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name  )

      print(F"""Pushed {checkpoint_name}"""  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Path  ,		A  :       str = None  ,		A  :       bool = True  )    -> List[Any]:







   """simple docstring"""
   __snake_case				:      Optional[Any]     							= 'imagenet-1k-id2label.json'
   __snake_case				:      Tuple     							= 10_00
   __snake_case				:      Dict     							= (1, num_labels)

   __snake_case				:      List[str]     							= 'huggingface/label-files'
   __snake_case				:      Any     							= num_labels
   __snake_case				:      str     							= json.load(open(hf_hub_download(A  ,		A  ,		repo_type='dataset'  )  ,		'r'  )  )
   __snake_case				:      Any     							= {int(A  ): v for k, v in idalabel.items()}

   __snake_case				:      int     							= idalabel
   __snake_case				:      Union[str, Any]     							= {v: k for k, v in idalabel.items()}

   __snake_case				:      Optional[int]     							= partial(A  ,		num_labels=A  ,		idalabel=A  ,		labelaid=A  )

   __snake_case				:      Dict     							= {
       'levit-128S': 1_28,
       'levit-128': 1_28,
       'levit-192': 1_92,
       'levit-256': 2_56,
       'levit-384': 3_84,
   }

   __snake_case				:      Union[str, Any]     							= {
       'levit-128S': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[2, 3, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-128': ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]  ,		num_attention_heads=[4, 8, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[16, 16, 16]  ,		drop_path_rate=0  ,		),
       'levit-192': ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]  ,		num_attention_heads=[3, 5, 6]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-256': ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]  ,		num_attention_heads=[4, 6, 8]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0  ,		),
       'levit-384': ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]  ,		num_attention_heads=[6, 9, 12]  ,		depths=[4, 4, 4]  ,		key_dim=[32, 32, 32]  ,		drop_path_rate=0.1  ,		),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]  ,		A  ,		names_to_config[model_name]  ,		A  ,		A  )
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]  ,		A  ,		A  ,		A  ,		A  )
   return config, expected_shape


if __name__ == "__main__":
      __A    	=   argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--model_name''',
          default=None,
          type=str,
          help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
      )
      parser.add_argument(
          '''--pytorch_dump_folder_path''',
          default='''levit-dump-folder/''',
          type=Path,
          required=False,
          help='''Path to the output PyTorch model directory.''',
      )
      parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
      parser.add_argument(
          '''--no-push_to_hub''',
          dest='''push_to_hub''',
          action='''store_false''',
          help='''Do not push model and image processor to the hub''',
      )

      __A    	=   parser.parse_args()
      __A    	=   args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)