



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


UpperCAmelCase							 =					logging.get_logger(__name__)

UpperCAmelCase							 =					{}

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      int         =					"""llama"""
							UpperCamelCase__				:      Union[str, Any]         =					["""past_key_values"""]


							def __init__(							self  ,  a_=32_000  ,  a_=4_096  ,  a_=11_008  ,  a_=32  ,  a_=32  ,  a_=None  ,  a_="silu"  ,  a_=2_048  ,  a_=0.02  ,  a_=1E-6  ,  a_=True  ,  a_=0  ,  a_=1  ,  a_=2  ,  a_=1  ,  a_=False  ,  a_=None  ,  **a_  ,  ):
											a__				      =		vocab_size
											a__				      =		max_position_embeddings
											a__				      =		hidden_size
											a__				      =		intermediate_size
											a__				      =		num_hidden_layers
											a__				      =		num_attention_heads

											# for backward compatibility
											if num_key_value_heads is None:
															a__				      =		num_attention_heads

											a__				      =		num_key_value_heads
											a__				      =		hidden_act
											a__				      =		initializer_range
											a__				      =		rms_norm_eps
											a__				      =		pretraining_tp
											a__				      =		use_cache
											a__				      =		rope_scaling
											self._rope_scaling_validation()

											super().__init__(
											    pad_token_id=a_  ,  bos_token_id=a_  ,  eos_token_id=a_  ,  tie_word_embeddings=a_  ,  **a_  ,  )







							def _a      (							self		):
											if self.rope_scaling is None:
															return

											if not isinstance(self.rope_scaling  ,  a_		) or len(self.rope_scaling		) != 2:
															raise ValueError(
															    """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
															    F'''got {self.rope_scaling}'''		)
											a__				      =		self.rope_scaling.get("""type"""  ,  a_		)
											a__				      =		self.rope_scaling.get("""factor"""  ,  a_		)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
															raise ValueError(
															    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''		)
											if rope_scaling_factor is None or not isinstance(a_  ,  a_		) or rope_scaling_factor <= 1.0:
															raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''		)






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

import os
from pathlib import Path

def 			A_      (	__a     :				List[Any] ,		__a     :				List[str] ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__				      =		{
				    """en""": """Machine learning is great, isn't it?""",
				    """ru""": """Машинное обучение - это здорово, не так ли?""",
				    """de""": """Maschinelles Lernen ist großartig, oder?""",
				}

				# BLUE scores as follows:
				# "pair": [fairseq, transformers]
				a__				      =		{
				    """ru-en""": ["""[41.3](http://matrix.statmt.org/matrix/output/1907?run_id=6937)""", """39.20"""],
				    """en-ru""": ["""[36.4](http://matrix.statmt.org/matrix/output/1914?run_id=6724)""", """33.47"""],
				    """en-de""": ["""[43.1](http://matrix.statmt.org/matrix/output/1909?run_id=6862)""", """42.83"""],
				    """de-en""": ["""[42.3](http://matrix.statmt.org/matrix/output/1902?run_id=6750)""", """41.35"""],
				}
				a__				      =		F'''{src_lang}-{tgt_lang}'''

				a__				      =		F'''
---
language: 
- {src_lang}
- {tgt_lang}
thumbnail:
tags:
- translation
- wmt19
- facebook
license: apache-2.0
datasets:
- wmt19
metrics:
- bleu
---

# FSMT

## Model description

This is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.

For more details, please see, [Facebook FAIR\'s WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).

The abbreviation FSMT stands for FairSeqMachineTranslation

All four models are available:

* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)
* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)
* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)
* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)

## Intended uses & limitations

#### How to use

```python
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
mname = "facebook/wmt19-{src_lang}-{tgt_lang}"
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

- The original (and this ported model) doesn\'t seem to handle well inputs with repeated sub-phrases, [content gets truncated](https://discuss.huggingface.co/t/issues-with-translating-inputs-containing-repeated-phrases/981)

## Training data

Pretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).

## Eval results

pair   | fairseq | transformers
-------|---------|----------
{pair}  | {scores[pair][0]} | {scores[pair][1]}

The score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn\'t support:
- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).
- re-ranking

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=15
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 $PAIR
PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval.py facebook/wmt19-$PAIR $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
```
note: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.

## 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

```bibtex
@inproceedings{{...,
  year={{2020}},
  title={{Facebook FAIR\'s WMT19 News Translation Task Submission}},
  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},
  booktitle={{Proc. of WMT}},
}}
```


## TODO

- port model ensemble (fairseq uses 4 model checkpoints)

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


# make sure we are under the root of the project
UpperCAmelCase							 =					Path(__file__).resolve().parent.parent.parent
UpperCAmelCase							 =					repo_dir / """model_cards"""

for model_name in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	UpperCAmelCase ,       UpperCAmelCase ,       UpperCAmelCase							 =					model_name.split("""-""")
	UpperCAmelCase							 =					model_cards_dir / """facebook""" / model_name
	write_model_card(model_card_dir, src_lang=src_lang, tgt_lang=tgt_lang)

