






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


snake_case_   :	List[str]							      =logging.get_logger(__name__)

snake_case_   :	List[str]							      ={
    '''layoutlmv2-base-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json''',
    '''layoutlmv2-large-uncased''': '''https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json''',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
					import detectrona





class        a__      (	lowerCAmelCase__		):
		UpperCAmelCase_							:       Dict 					= 'layoutlmv2'






		def __init__(      self							, lowercase__=30522							, lowercase__=768							, lowercase__=12							, lowercase__=12							, lowercase__=3072							, lowercase__="gelu"							, lowercase__=0.1							, lowercase__=0.1							, lowercase__=512							, lowercase__=2							, lowercase__=0.02							, lowercase__=1e-12							, lowercase__=0							, lowercase__=1024							, lowercase__=128							, lowercase__=32							, lowercase__=True							, lowercase__=256							, lowercase__=64							, lowercase__=True							, lowercase__=[7, 7, 256]							, lowercase__=128							, lowercase__=128							, lowercase__=True							, lowercase__=True							, lowercase__=False							, lowercase__=None							, **lowercase__							, )				->				Tuple:
				super().__init__(
				    vocab_size=lowercase__							, hidden_size=lowercase__							, num_hidden_layers=lowercase__							, num_attention_heads=lowercase__							, intermediate_size=lowercase__							, hidden_act=lowercase__							, hidden_dropout_prob=lowercase__							, attention_probs_dropout_prob=lowercase__							, max_position_embeddings=lowercase__							, type_vocab_size=lowercase__							, initializer_range=lowercase__							, layer_norm_eps=lowercase__							, pad_token_id=lowercase__							, **lowercase__							, )
				__A    =							max_ad_position_embeddings
				__A    =							max_rel_pos
				__A    =							rel_pos_bins
				__A    =							fast_qkv
				__A    =							max_rel_ad_pos
				__A    =							rel_ad_pos_bins
				__A    =							convert_sync_batchnorm
				__A    =							image_feature_pool_shape
				__A    =							coordinate_size
				__A    =							shape_size
				__A    =							has_relative_attention_bias
				__A    =							has_spatial_attention_bias
				__A    =							has_visual_segment_embedding
				__A    =							(
				    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
				)






		@classmethod
		def 					_lowerCamelCase					(      self						)				->				str:
				return {
				    "MODEL.MASK_ON": True,
				    "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
				    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
				    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
				    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
				    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
				    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2000,
				    "MODEL.RPN.PRE_NMS_TOPK_TEST": 1000,
				    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1000,
				    "MODEL.POST_NMS_TOPK_TEST": 1000,
				    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
				    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
				    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
				    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
				    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
				    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
				    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
				    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
				    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
				    "MODEL.RESNETS.DEPTH": 101,
				    "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
				    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
				    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
				    "MODEL.RESNETS.NUM_GROUPS": 32,
				    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
				    "MODEL.RESNETS.STRIDE_IN_1X1": False,
				}






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							detectrona.config.get_cfg()
				for k, v in self.detectrona_config_args.items():
						__A    =							k.split("."						)
						__A    =							detectrona_config
						for attribute in attributes[:-1]:
								__A    =							getattr(lowercase__							, lowercase__						)
						setattr(lowercase__							, attributes[-1]							, lowercase__						)

				return detectrona_config









# 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)

