


'''simple docstring'''
class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							)    ->  Tuple:
					lowercase						:		Union[str, Any]   					=		name
					lowercase						:		Union[str, Any]   					=		val

				def __str__(     self							)    ->  Any:
					return F'''{self.__class__.__name__}({self.name}, {self.val})'''


				def __lt__(     self							,      a_							)    ->  str:
					return self.val < other.val




class        _UpperCamelCase	:


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Dict:
					lowercase						:		str   					=		{}
					lowercase						:		Dict   					=		{}
					lowercase						:		Dict   					=		self.build_heap(a_							)

				def __getitem__(     self							,      a_							)    ->  List[Any]:
					return self.get_value(a_							)

				def 	a__		(     self							,      a_							)    ->  Any:
					return (idx - 1) // 2

				def 	a__		(     self							,      a_							)    ->  Optional[Any]:
					return idx * 2 + 1

				def 	a__		(     self							,      a_							)    ->  Optional[int]:
					return idx * 2 + 2

				def 	a__		(     self							,      a_							)    ->  List[Any]:
					return self.heap_dict[key]

				def 	a__		(     self							,      a_							)    ->  Optional[Any]:
					lowercase						:		Dict   					=		len(a_							) - 1
					lowercase						:		Dict   					=		self.get_parent_idx(a_							)

					for idx, i in enumerate(a_							):
						lowercase						:		List[str]   					=		idx
						lowercase						:		Tuple   					=		i.val

					for i in range(a_							,      -1							,      -1							):
						self.sift_down(a_							,      a_							)
					return array

				def 	a__		(     self							,      a_							,      a_							)    ->  Optional[int]:
					while True:
						lowercase						:		Tuple   					=		self.get_left_child_idx(a_							)  # noqa: E741
						lowercase						:		int   					=		self.get_right_child_idx(a_							)

						lowercase						:		str   					=		idx
						if l < len(a_							) and array[l] < array[idx]:
							lowercase						:		Any   					=		l
						if r < len(a_							) and array[r] < array[smallest]:
							lowercase						:		Dict   					=		r

						if smallest != idx:
							lowercase					,		lowercase						:		Any   					=		array[smallest], array[idx]
							(
							    (
							    lowercase
							)					,		(
							    lowercase
							)					,		
							)						:		Dict   					=		(
							    self.idx_of_element[array[smallest]],
							    self.idx_of_element[array[idx]],
							)
							lowercase						:		str   					=		smallest
						else:
							break

				def 	a__		(     self							,      a_							)    ->  List[str]:
					lowercase						:		str   					=		self.get_parent_idx(a_							)
					while p >= 0 and self.heap[p] > self.heap[idx]:
						lowercase					,		lowercase						:		Optional[Any]   					=		self.heap[idx], self.heap[p]
						lowercase					,		lowercase						:		List[str]   					=		(
						    self.idx_of_element[self.heap[idx]],
						    self.idx_of_element[self.heap[p]],
						)
						lowercase						:		Tuple   					=		p
						lowercase						:		List[str]   					=		self.get_parent_idx(a_							)

				def 	a__		(     self							)    ->  int:
					return self.heap[0]

				def 	a__		(     self							)    ->  Dict:
					lowercase					,		lowercase						:		str   					=		self.heap[-1], self.heap[0]
					lowercase					,		lowercase						:		Any   					=		(
					    self.idx_of_element[self.heap[-1]],
					    self.idx_of_element[self.heap[0]],
					)

					lowercase						:		Tuple   					=		self.heap.pop()
					del self.idx_of_element[x]
					self.sift_down(0							,      self.heap							)
					return x

				def 	a__		(     self							,      a_							)    ->  Dict:
					self.heap.append(a_							)
					lowercase						:		List[Any]   					=		len(self.heap							) - 1
					lowercase						:		Any   					=		node.val
					self.sift_up(len(self.heap							) - 1							)

				def 	a__		(     self							)    ->  int:
					return len(self.heap							) == 0


				def 	a__		(     self							,      a_							,      a_							)    ->  Dict:
					assert (
					    self.heap[self.idx_of_element[node]].val > new_value
					), "newValue must be less that current value"
					lowercase						:		Tuple   					=		new_value
					lowercase						:		List[str]   					=		new_value
					self.sift_up(self.idx_of_element[node]							)


lowerCAmelCase   : List[str]             =				Node("""R""", -1)
lowerCAmelCase   : Dict             =				Node("""B""", 6)
lowerCAmelCase   : Optional[Any]             =				Node("""A""", 3)
lowerCAmelCase   : Tuple             =				Node("""X""", 1)
lowerCAmelCase   : List[str]             =				Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase   : Tuple             =				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
					print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()






'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py

import os
from pathlib import Path




def 	_A  (       A							,A							,A							,A							)  ->				List[Any]:
	lowercase						:		Optional[int]   					=		{
	    "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]
	lowercase						:		Optional[int]   					=		{
	    "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],
	}
	lowercase						:		Dict   					=		F'''{src_lang}-{tgt_lang}'''

	lowercase						:		Union[str, Any]   					=		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=A							,exist_ok=A							)
	lowercase						:		str   					=		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
lowerCAmelCase   : Union[str, Any]             =				Path(__file__).resolve().parent.parent.parent
lowerCAmelCase   : Union[str, Any]             =				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"]:
					lowerCAmelCase   : Optional[Any]             =				model_cards_dir / """allenai""" / model_name
					write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)


