

'''simple docstring'''






import heapq


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					[]

 # for each node and his adjacency list add them and the rank of the node to queue
 # using heapq module the queue will be filled like a Priority Queue
 # heapq works with a min priority queue, so I used -1*len(v) to build it
 for key, value in graph.items():
  # O(log(n))
  heapq.heappush(A__	,							[-1 * len(A__ ), (key, value)] )

 # chosen_vertices = set of chosen vertices
 __lowercase				    =					set()

 # while queue isn't empty and there are still edges
 #   (queue[0][0] is the rank of the node with max rank)
 while queue and queue[0][0] != 0:
  # extract vertex with max rank from queue and add it to chosen_vertices
  __lowercase				    =					heapq.heappop(A__ )[1][0]
  chosen_vertices.add(A__ )

  # Remove all arcs adjacent to argmax
  for elem in queue:
   # if v haven't adjacent node, skip
   if elem[0] == 0:
    continue
   # if argmax is reachable from elem
   # remove argmax from elem's adjacent list and update his rank
   if argmax in elem[1][1]:
    __lowercase				    =					elem[1][1].index(A__ )
    del elem[1][1][index]
    elem[0] += 1
        # re-order the queue
  heapq.heapify(A__ )
 return chosen_vertices


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__   		=      {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
 print(f'Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}')







# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path







def      lowerCamelCase_						(			UpperCamelCase__							: int					,	UpperCamelCase__							: Optional[int]					,	UpperCamelCase__							: int					,	UpperCamelCase__							: str )	->			List[str]:





				"""simple docstring"""


				__lowerCamelCase									=       {
				    '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]
				__lowerCamelCase									=       {
				    'wmt16-en-de-dist-12-1': [28.3, 27.52],
				    'wmt16-en-de-dist-6-1': [27.4, 27.11],
				    'wmt16-en-de-12-1': [26.9, 25.75],
				}
				__lowerCamelCase									=       F"""{src_lang}-{tgt_lang}"""

				__lowerCamelCase									=       F"""
---
language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt16
- allenai
license: apache-2.0
datasets:
- wmt16
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt16 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).

All 3 models are available:

* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)
* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)
* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)


## 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   | fairseq | transformers
-------|---------|----------
{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}

The score is slightly below the score reported in the paper, as the researchers don't use `sacrebleu` and measure the score on tokenized outputs. `transformers` score was measured using `sacrebleu` on detokenized outputs.

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 wmt16 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt16 -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/wmt16/)
- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)


### 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=UpperCamelCase__					,	exist_ok=UpperCamelCase__ )
				__lowerCamelCase									=       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								=       Path(__file__).resolve().parent.parent.parent
__A								=       repo_dir / "model_cards"

for model_name in ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
					__A								=       model_cards_dir / "allenai" / model_name
					write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)


