



"""simple docstring"""






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase			:			Optional[int]										=						logging.get_logger(__name__)

_lowercase			:			int										=						{
    "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json",
}

class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'mvp'
					_a             =							['past_key_values']
					_a             =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



					def __init__(	self							:  str,  lowerCamelCase							:  Union[str, Any]=5_0267,  lowerCamelCase							:  Dict=1024,  lowerCamelCase							:  Tuple=12,  lowerCamelCase							:  Tuple=4096,  lowerCamelCase							:  Any=16,  lowerCamelCase							:  List[Any]=12,  lowerCamelCase							:  Tuple=4096,  lowerCamelCase							:  Dict=16,  lowerCamelCase							:  List[Any]=0.0,  lowerCamelCase							:  int=0.0,  lowerCamelCase							:  Optional[int]="gelu",  lowerCamelCase							:  Dict=1024,  lowerCamelCase							:  Union[str, Any]=0.1,  lowerCamelCase							:  List[Any]=0.0,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  str=0.02,  lowerCamelCase							:  Any=0.0,  lowerCamelCase							:  Any=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  str=1,  lowerCamelCase							:  int=0,  lowerCamelCase							:  List[str]=2,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Any=2,  lowerCamelCase							:  str=2,  lowerCamelCase							:  int=False,  lowerCamelCase							:  Any=100,  lowerCamelCase							:  List[str]=800,  **lowerCamelCase							:  int,  )-> Optional[Any]:
								lowerCamelCase__		:		Optional[int]		     =vocab_size
								lowerCamelCase__		:		Tuple		     =max_position_embeddings
								lowerCamelCase__		:		List[str]		     =d_model
								lowerCamelCase__		:		List[Any]		     =encoder_ffn_dim
								lowerCamelCase__		:		str		     =encoder_layers
								lowerCamelCase__		:		Optional[Any]		     =encoder_attention_heads
								lowerCamelCase__		:		Union[str, Any]		     =decoder_ffn_dim
								lowerCamelCase__		:		str		     =decoder_layers
								lowerCamelCase__		:		Dict		     =decoder_attention_heads
								lowerCamelCase__		:		int		     =dropout
								lowerCamelCase__		:		int		     =attention_dropout
								lowerCamelCase__		:		int		     =activation_dropout
								lowerCamelCase__		:		Optional[Any]		     =activation_function
								lowerCamelCase__		:		List[str]		     =init_std
								lowerCamelCase__		:		Dict		     =encoder_layerdrop
								lowerCamelCase__		:		Optional[int]		     =decoder_layerdrop
								lowerCamelCase__		:		Any		     =classifier_dropout
								lowerCamelCase__		:		Optional[int]		     =use_cache
								lowerCamelCase__		:		List[Any]		     =encoder_layers
								lowerCamelCase__		:		List[str]		     =scale_embedding  # scale factor will be sqrt(d_model) if True
								lowerCamelCase__		:		List[Any]		     =use_prompt
								lowerCamelCase__		:		Optional[Any]		     =prompt_length
								lowerCamelCase__		:		Any		     =prompt_mid_dim

								super().__init__(
								    pad_token_id=lowerCamelCase,  bos_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  **lowerCamelCase,  )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''',  lowerCamelCase					):
											lowerCamelCase__		:		str		     =self.bos_token_id
											warnings.warn(
											    F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
											    '''The config can simply be saved and uploaded again to be fixed.'''					)









"""simple docstring"""






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

import os
from pathlib import Path






def 					snake_case__       (		__lowerCamelCase    :   Union[str, Any]							,		__lowerCamelCase    :   int							,		__lowerCamelCase    :   Tuple							,		__lowerCamelCase    :   Union[str, Any]     ):
			"""simple docstring"""
			lowerCamelCase__		:		Any		     ={
			    '''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[Any]		     ={
			    '''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__		:		Any		     =f'''{src_lang}-{tgt_lang}'''

			lowerCamelCase__		:		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=__lowerCamelCase							,		exist_ok=__lowerCamelCase     )
			lowerCamelCase__		:		str		     =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
_lowercase			:			List[str]										=						Path(__file__).resolve().parent.parent.parent
_lowercase			:			Dict										=						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"]:
		_lowercase			:			int										=						model_cards_dir / "allenai" / model_name
		write_model_card(model_card_dir, src_lang="en", tgt_lang="de", model_name=model_name)




