
"""simple docstring"""


import os
from pathlib import Path






def   lowerCAmelCase_  (						snake_case_						:     str				,       snake_case_						:     Tuple				,       snake_case_						:     Tuple				,       snake_case_						:     Union[str, Any]     )  ->List[Any]:
			lowerCamelCase__      :	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]
			lowerCamelCase__      :	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],
			}
			lowerCamelCase__      :	Dict						=f"""{src_lang}-{tgt_lang}"""

			lowerCamelCase__      :	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=snake_case_				,       exist_ok=snake_case_     )
			lowerCamelCase__      :	str						=os.path.join(snake_case_				,       'README.md'     )
			print(f"""Generating {path}"""     )
			with open(snake_case_				,       'w'				,       encoding='utf-8'     ) as f:
						f.write(snake_case_     )


# make sure we are under the root of the project
lowerCAmelCase								=		Path(__file__).resolve().parent.parent.parent
lowerCAmelCase								=		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								=		model_cards_dir / """allenai""" / model_name
							write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)



'''simple docstring'''
lowerCAmelCase   : List[str]             =				2_5_6
# Modulus to hash a string
lowerCAmelCase   : Tuple             =				1_0_0_0_0_0_3




def 	_A  (       A							,A							)  ->				bool:
	lowercase						:		List[Any]   					=		len(A							)
	lowercase						:		List[Any]   					=		len(A							)
	if p_len > t_len:
		return False

	lowercase						:		List[str]   					=		0
	lowercase						:		Dict   					=		0
	lowercase						:		Any   					=		1

	# Calculating the hash of pattern and substring of text
	for i in range(A							):
		lowercase						:		Optional[int]   					=		(ord(pattern[i]							) + p_hash * alphabet_size) % modulus
		lowercase						:		Any   					=		(ord(text[i]							) + text_hash * alphabet_size) % modulus
		if i == p_len - 1:
			continue
		lowercase						:		List[str]   					=		(modulus_power * alphabet_size) % modulus

	for i in range(0							,t_len - p_len + 1							):
		if text_hash == p_hash and text[i : i + p_len] == pattern:
			return True
		if i == t_len - p_len:
			continue
		# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
		lowercase						:		Any   					=		(
		    (text_hash - ord(text[i]							) * modulus_power) * alphabet_size
		    + ord(text[i + p_len]							)
		) % modulus
	return False




def 	_A  (       )  ->				None:
	lowercase						:		Dict   					=		"abc1abc12"
	lowercase						:		Union[str, Any]   					=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
	lowercase						:		Any   					=		"alskfjaldsk23adsfabcabc"
	assert rabin_karp(A							,A							) and not rabin_karp(A							,A							)

	# Test 2)
	lowercase						:		str   					=		"ABABX"
	lowercase						:		Union[str, Any]   					=		"ABABZABABYABABX"
	assert rabin_karp(A							,A							)

	# Test 3)
	lowercase						:		str   					=		"AAAB"
	lowercase						:		List[str]   					=		"ABAAAAAB"
	assert rabin_karp(A							,A							)

	# Test 4)
	lowercase						:		List[str]   					=		"abcdabcy"
	lowercase						:		str   					=		"abcxabcdabxabcdabcdabcy"
	assert rabin_karp(A							,A							)

	# Test 5)
	lowercase						:		int   					=		"Lü"
	lowercase						:		Optional[Any]   					=		"Lüsai"
	assert rabin_karp(A							,A							)
	lowercase						:		Tuple   					=		"Lue"
	assert not rabin_karp(A							,A							)
	print("Success."							)


if __name__ == "__main__":
					test_rabin_karp()


