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

import os
from pathlib import Path

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						__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=_lowerCAmelCase ,    exist_ok=_lowerCAmelCase						)
						__lowerCAmelCase     =  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
SCREAMING_SNAKE_CASE_					     =	Path(__file__).resolve().parent.parent.parent
SCREAMING_SNAKE_CASE_					     =	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"]:
			SCREAMING_SNAKE_CASE_					     =	model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)


"""simple docstring"""
from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''efficientnet'''




							def __init__( self    ,				snake_case_ = 3    ,				snake_case_ = 600    ,				snake_case_ = 2.0    ,				snake_case_ = 3.1    ,				snake_case_ = 8    ,				snake_case_ = [3, 3, 5, 3, 5, 5, 3]    ,				snake_case_ = [32, 16, 24, 40, 80, 112, 192]    ,				snake_case_ = [16, 24, 40, 80, 112, 192, 320]    ,				snake_case_ = []    ,				snake_case_ = [1, 2, 2, 2, 1, 2, 1]    ,				snake_case_ = [1, 2, 2, 3, 3, 4, 1]    ,				snake_case_ = [1, 6, 6, 6, 6, 6, 6]    ,				snake_case_ = 0.25    ,				snake_case_ = "swish"    ,				snake_case_ = 2_560    ,				snake_case_ = "mean"    ,				snake_case_ = 0.02    ,				snake_case_ = 0.001    ,				snake_case_ = 0.99    ,				snake_case_ = 0.5    ,				snake_case_ = 0.2    ,				**snake_case_    ,				)       ->  List[str]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  width_coefficient
													__lowerCAmelCase     =  depth_coefficient
													__lowerCAmelCase     =  depth_divisor
													__lowerCAmelCase     =  kernel_sizes
													__lowerCAmelCase     =  in_channels
													__lowerCAmelCase     =  out_channels
													__lowerCAmelCase     =  depthwise_padding
													__lowerCAmelCase     =  strides
													__lowerCAmelCase     =  num_block_repeats
													__lowerCAmelCase     =  expand_ratios
													__lowerCAmelCase     =  squeeze_expansion_ratio
													__lowerCAmelCase     =  hidden_act
													__lowerCAmelCase     =  hidden_dim
													__lowerCAmelCase     =  pooling_type
													__lowerCAmelCase     =  initializer_range
													__lowerCAmelCase     =  batch_norm_eps
													__lowerCAmelCase     =  batch_norm_momentum
													__lowerCAmelCase     =  dropout_rate
													__lowerCAmelCase     =  drop_connect_rate
													__lowerCAmelCase     =  sum(snake_case_				) * 4
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		version.parse('''1.11''' )




							@property
							def    A__						( self				)       ->  Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
													    ]				)




							@property
							def    A__						( self				)       ->  float:
													return 1e-5
