



"""simple docstring"""






# Usage:
# ./gen-card-allenai-wmt19.py

import os
from pathlib import Path






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       Any      ):
			"""simple docstring"""
			lowerCamelCase__						:						str						      ={
			    '''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__						:						List[Any]						      ={
			    '''wmt19-de-en-6-6-base''': [0, 38.37],
			    '''wmt19-de-en-6-6-big''': [0, 39.90],
			}
			lowerCamelCase__						:						Any						      =f'''{src_lang}-{tgt_lang}'''

			lowerCamelCase__						:						Tuple						      =f'''
---

language:
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- allenai
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of fairseq-based [wmt19 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).

2 models are available:

* [wmt19-de-en-6-6-big](https://huggingface.co/allenai/wmt19-de-en-6-6-big)
* [wmt19-de-en-6-6-base](https://huggingface.co/allenai/wmt19-de-en-6-6-base)


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

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 wmt19 -l $PAIR --echo src > $DATA_DIR/val.source
sacrebleu -t wmt19 -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/wmt19/)
- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)


### 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__						:						Optional[int]						      =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					:					Optional[Any] 		=		Path(__file__).resolve().parent.parent.parent
_lowercase					:					str 		=		repo_dir / "model_cards"

for model_name in ["wmt19-de-en-6-6-base", "wmt19-de-en-6-6-big"]:
		_lowercase					:					Any 		=		model_cards_dir / "allenai" / model_name
		write_model_card(model_card_dir, src_lang="de", tgt_lang="en", model_name=model_name)












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


A										=      logging.get_logger(__name__)

A										=      {
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}


class 				__a				(	__A    ):





		'''simple docstring'''



		UpperCAmelCase__   :					str					     =    """tvlt"""


		def __init__(    self			,     UpperCamelCase__=224			,     UpperCamelCase__=2048			,     UpperCamelCase__=128			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=[16, 16]			,     UpperCamelCase__=3			,     UpperCamelCase__=1			,     UpperCamelCase__=8			,     UpperCamelCase__=768			,     UpperCamelCase__=12			,     UpperCamelCase__=12			,     UpperCamelCase__=3072			,     UpperCamelCase__="gelu"			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.0			,     UpperCamelCase__=0.02			,     UpperCamelCase__=1E-6			,     UpperCamelCase__=True			,     UpperCamelCase__=False			,     UpperCamelCase__=16			,     UpperCamelCase__=512			,     UpperCamelCase__=8			,     UpperCamelCase__=2048			,     UpperCamelCase__=0.75			,     UpperCamelCase__=0.15			,     UpperCamelCase__="frame-level"			,     UpperCamelCase__=True			,     UpperCamelCase__=True			,     UpperCamelCase__="classification"			,     **UpperCamelCase__			,     ):
						super().__init__(**UpperCamelCase__							)

						if audio_mask_type not in ("frame-level", "patch_level"):
										raise ValueError(
										    'audio_mask_type must be one of two acceptable strategies - {\'frame_level\', \'patch-level\') '
										    F'''got {audio_mask_type}'''							)

						SCREAMING_SNAKE_CASE_ :						Tuple    				= image_size
						SCREAMING_SNAKE_CASE_ :						int    				= spectrogram_length
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= frequency_length
						SCREAMING_SNAKE_CASE_ :						Any    				= image_patch_size
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= audio_patch_size
						SCREAMING_SNAKE_CASE_ :						int    				= num_image_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_audio_channels
						SCREAMING_SNAKE_CASE_ :						Any    				= num_frames

						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_size
						SCREAMING_SNAKE_CASE_ :						str    				= num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= num_attention_heads
						SCREAMING_SNAKE_CASE_ :						str    				= intermediate_size
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= hidden_act
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= hidden_dropout_prob
						SCREAMING_SNAKE_CASE_ :						int    				= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE_ :						Dict    				= initializer_range
						SCREAMING_SNAKE_CASE_ :						Dict    				= layer_norm_eps
						SCREAMING_SNAKE_CASE_ :						Union[str, Any]    				= qkv_bias
						SCREAMING_SNAKE_CASE_ :						List[str]    				= use_mean_pooling

						SCREAMING_SNAKE_CASE_ :						str    				= decoder_num_attention_heads
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= decoder_hidden_size
						SCREAMING_SNAKE_CASE_ :						Any    				= decoder_num_hidden_layers
						SCREAMING_SNAKE_CASE_ :						List[Any]    				= decoder_intermediate_size
						SCREAMING_SNAKE_CASE_ :						List[str]    				= pixel_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[int]    				= audio_mask_ratio
						SCREAMING_SNAKE_CASE_ :						Optional[Any]    				= audio_mask_type

						SCREAMING_SNAKE_CASE_ :						Any    				= task_matching
						SCREAMING_SNAKE_CASE_ :						Any    				= task_mae
						SCREAMING_SNAKE_CASE_ :						int    				= loss_type