






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def __A   (				__lowerCAmelCase     )->  Tuple:





						"""simple docstring"""



						_UpperCAmelCase     =      args.serialization_dir
						_UpperCAmelCase     =      args.pruning_method
						_UpperCAmelCase     =      args.threshold

						_UpperCAmelCase     =      torch.load(os.path.join(__lowerCAmelCase			,		'pytorch_model.bin'     )			,		map_location='cpu'     )

						_UpperCAmelCase     =      0  # Number of remaining (not pruned) params in the encoder
						_UpperCAmelCase     =      0  # Number of params in the encoder

						print('name'.ljust(60			,		' '     )			,		'Remaining Weights %'			,		'Remaining Weight'     )
						for name, param in st.items():
												if "encoder" not in name:
																		continue

												if "mask_scores" in name:
																		if pruning_method == "topK":
																								_UpperCAmelCase     =      TopKBinarizer.apply(__lowerCAmelCase			,		__lowerCAmelCase     ).sum().item()
																		elif pruning_method == "sigmoied_threshold":
																								_UpperCAmelCase     =      ThresholdBinarizer.apply(__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     ).sum().item()
																		elif pruning_method == "l0":
																								_UpperCAmelCase ,			_UpperCAmelCase     =      -0.1, 1.1
																								_UpperCAmelCase     =      torch.sigmoid(__lowerCAmelCase     )
																								_UpperCAmelCase     =      s * (r - l) + l
																								_UpperCAmelCase     =      s_bar.clamp(min=0.0			,		max=1.0     )
																								_UpperCAmelCase     =      (mask > 0.0).sum().item()
																		else:
																								raise ValueError('Unknown pruning method'     )
																		remaining_count += mask_ones
																		print(name.ljust(60			,		' '     )			,		str(round(100 * mask_ones / param.numel()			,		3     )     ).ljust(20			,		' '     )			,		str(__lowerCAmelCase     )     )
												else:
																		encoder_count += param.numel()
																		if "bias" in name or "LayerNorm" in name:
																								remaining_count += param.numel()

						print(''     )
						print('Remaining Weights (global) %: '			,		100 * remaining_count / encoder_count     )


if __name__ == "__main__":
		_a				     =  argparse.ArgumentParser()

		parser.add_argument(
		    '''--pruning_method''',
		    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
		    type=str,
		    required=True,
		    help=(
		        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
		        ''' pruning)'''
		    ),
		)
		parser.add_argument(
		    '''--threshold''',
		    type=float,
		    required=False,
		    help=(
		        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
		        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
		        '''Not needed for `l0`'''
		    ),
		)
		parser.add_argument(
		    '''--serialization_dir''',
		    type=str,
		    required=True,
		    help='''Folder containing the model that was previously fine-pruned''',
		)

		_a				     =  parser.parse_args()

		main(args)








# 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)
