






import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       str 					= ['image_processor', 'tokenizer']
		UpperCAmelCase_							:       int 					= 'VideoMAEImageProcessor'
		UpperCAmelCase_							:       Optional[Any] 					= ('CLIPTokenizer', 'CLIPTokenizerFast')






		def __init__(      self							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Tuple:
				__A    =							None
				if "feature_extractor" in kwargs:
						warnings.warn(
						    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
						    " instead."							, lowercase__							, )
						__A    =							kwargs.pop("feature_extractor"						)

				__A    =							image_processor if image_processor is not None else feature_extractor
				if image_processor is None:
						raise ValueError("You need to specify an `image_processor`."						)
				if tokenizer is None:
						raise ValueError("You need to specify a `tokenizer`."						)

				super().__init__(lowercase__							, lowercase__						)
				__A    =							self.image_processor






		def __call__(      self							, lowercase__=None							, lowercase__=None							, lowercase__=None							, **lowercase__						)				->				Any:

				if text is None and videos is None:
						raise ValueError("You have to specify either text or videos. Both cannot be none."						)

				if text is not None:
						__A    =							self.tokenizer(lowercase__							, return_tensors=lowercase__							, **lowercase__						)

				if videos is not None:
						__A    =							self.image_processor(lowercase__							, return_tensors=lowercase__							, **lowercase__						)

				if text is not None and videos is not None:
						__A    =							image_features.pixel_values
						return encoding
				elif text is not None:
						return encoding
				else:
						return BatchEncoding(data=dict(**lowercase__						)							, tensor_type=lowercase__						)






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				Optional[int]:
				return self.tokenizer.batch_decode(*lowercase__							, **lowercase__						)






		def 					_lowerCamelCase					(      self							, *lowercase__							, **lowercase__						)				->				Tuple:
				return self.tokenizer.decode(*lowercase__							, **lowercase__						)






		@property
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				return ["input_ids", "attention_mask", "position_ids", "pixel_values"]






		@property
		def 					_lowerCamelCase					(      self						)				->				int:
				warnings.warn(
				    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase__							, )
				return self.image_processor_class






		@property
		def 					_lowerCamelCase					(      self						)				->				Any:
				warnings.warn(
				    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase__							, )
				return self.image_processor









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

import os
from pathlib import Path



def      UpperCAmelCase						( lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__	):


		'''simple docstring'''

		__A    =							{
		    "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]
		__A    =							{
		    "wmt19-de-en-6-6-base": [0, 38.37],
		    "wmt19-de-en-6-6-big": [0, 39.90],
		}
		__A    =							F"""{src_lang}-{tgt_lang}"""

		__A    =							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__	)
		__A    =							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
snake_case_   :	Optional[int]							      =Path(__file__).resolve().parent.parent.parent
snake_case_   :	Dict							      =repo_dir / '''model_cards'''

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

