






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

import os
from pathlib import Path







def     a_   (__A				,       __A				,       __A				,       __A      )				->						List[str]:



					"""simple docstring"""
					__a					:						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]
					__a					:						int      =					{
					    "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],
					}
					__a					:						Union[str, Any]      =					f'{src_lang}-{tgt_lang}'

					__a					:						str      =					f'\n---\nlanguage:\n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt16\n- allenai\nlicense: apache-2.0\ndatasets:\n- wmt16\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of fairseq-based [wmt16 transformer](https://github.com/jungokasai/deep-shallow/) for {src_lang}-{tgt_lang}.\n\nFor more details, please, see [Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation](https://arxiv.org/abs/2006.10369).\n\nAll 3 models are available:\n\n* [wmt16-en-de-dist-12-1](https://huggingface.co/allenai/wmt16-en-de-dist-12-1)\n* [wmt16-en-de-dist-6-1](https://huggingface.co/allenai/wmt16-en-de-dist-6-1)\n* [wmt16-en-de-12-1](https://huggingface.co/allenai/wmt16-en-de-12-1)\n\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = "allenai/{model_name}"\ntokenizer = FSMTTokenizer.from_pretrained(mname)\nmodel = FSMTForConditionalGeneration.from_pretrained(mname)\n\ninput = "{texts[src_lang]}"\ninput_ids = tokenizer.encode(input, return_tensors="pt")\noutputs = model.generate(input_ids)\ndecoded = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(decoded) # {texts[tgt_lang]}\n\n```\n\n#### Limitations and bias\n\n\n## Training data\n\nPretrained weights were left identical to the original model released by allenai. For more details, please, see the [paper](https://arxiv.org/abs/2006.10369).\n\n## Eval results\n\nHere are the BLEU scores:\n\nmodel   | fairseq | transformers\n-------|---------|----------\n{model_name}  | {scores[model_name][0]} | {scores[model_name][1]}\n\nThe 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.\n\nThe score was calculated using this code:\n\n```bash\ngit clone https://github.com/huggingface/transformers\ncd transformers\nexport PAIR={pair}\nexport DATA_DIR=data/$PAIR\nexport SAVE_DIR=data/$PAIR\nexport BS=8\nexport NUM_BEAMS=5\nmkdir -p $DATA_DIR\nsacrebleu -t wmt16 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt16 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH="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\n```\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt16/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2016.tgz?1504722372)\n\n\n### BibTeX entry and citation info\n\n```\n@misc{{kasai2020deep,\n    title={{Deep Encoder, Shallow Decoder: Reevaluating the Speed-Quality Tradeoff in Machine Translation}},\n    author={{Jungo Kasai and Nikolaos Pappas and Hao Peng and James Cross and Noah A. Smith}},\n    year={{2020}},\n    eprint={{2006.10369}},\n    archivePrefix={{arXiv}},\n    primaryClass={{cs.CL}}\n}}\n```\n\n'
					model_card_dir.mkdir(parents=__A				,       exist_ok=__A      )
					__a					:						Tuple      =					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 ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
			UpperCAmelCase__      			=						model_cards_dir / '''allenai''' / model_name
			write_model_card(model_card_dir, src_lang='''en''', tgt_lang='''de''', model_name=model_name)













import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCAmelCase__      			=						{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

UpperCAmelCase__      			=						logging.get_logger(__name__)




class 							snake_case_       (    __UpperCamelCase      ):

						"""simple docstring"""



						snake_case__        =      """mask2former"""
						snake_case__        =      ["""swin"""]
						snake_case__        =      {"""hidden_size""": """hidden_dim"""}



						def __init__(self:					Any ,					__UpperCAmelCase:					Optional[Dict] = None ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 256 ,					__UpperCAmelCase:					int = 1024 ,					__UpperCAmelCase:					str = "relu" ,					__UpperCAmelCase:					int = 6 ,					__UpperCAmelCase:					int = 10 ,					__UpperCAmelCase:					int = 8 ,					__UpperCAmelCase:					float = 0.0 ,					__UpperCAmelCase:					int = 2048 ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					bool = False ,					__UpperCAmelCase:					int = 4 ,					__UpperCAmelCase:					int = 255 ,					__UpperCAmelCase:					int = 100 ,					__UpperCAmelCase:					float = 0.1 ,					__UpperCAmelCase:					float = 2.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					float = 5.0 ,					__UpperCAmelCase:					int = 12544 ,					__UpperCAmelCase:					float = 3.0 ,					__UpperCAmelCase:					float = 0.75 ,					__UpperCAmelCase:					float = 0.02 ,					__UpperCAmelCase:					float = 1.0 ,					__UpperCAmelCase:					bool = True ,					__UpperCAmelCase:					List[int] = [4, 8, 16, 32] ,					__UpperCAmelCase:					bool = None ,					**__UpperCAmelCase:					Any ,					)    ->     int:




											'''simple docstring'''



											if backbone_config is None:
																logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."   )
																__a					:						str      =					CONFIG_MAPPING["swin"](
																    image_size=224 ,					in_channels=3 ,					patch_size=4 ,					embed_dim=96 ,					depths=[2, 2, 18, 2] ,					num_heads=[3, 6, 12, 24] ,					window_size=7 ,					drop_path_rate=0.3 ,					use_absolute_embeddings=__UpperCAmelCase ,					out_features=["stage1", "stage2", "stage3", "stage4"] ,					)

											if isinstance(__UpperCAmelCase ,					__UpperCAmelCase   ):
																__a					:						List[Any]      =					backbone_config.pop("model_type"   )
																__a					:						List[str]      =					CONFIG_MAPPING[backbone_model_type]
																__a					:						Dict      =					config_class.from_dict(__UpperCAmelCase   )

											# verify that the backbone is supported
											if backbone_config.model_type not in self.backbones_supported:
																logger.warning_once(
																    f'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
																    f'Supported model types: {",".join(self.backbones_supported   )}'   )

											__a					:						Tuple      =					backbone_config
											__a					:						Any      =					feature_size
											__a					:						int      =					mask_feature_size
											__a					:						Optional[int]      =					hidden_dim
											__a					:						int      =					encoder_feedforward_dim
											__a					:						List[str]      =					activation_function
											__a					:						Optional[Any]      =					encoder_layers
											__a					:						List[Any]      =					decoder_layers
											__a					:						Union[str, Any]      =					num_attention_heads
											__a					:						Optional[Any]      =					dropout
											__a					:						Tuple      =					dim_feedforward
											__a					:						List[str]      =					pre_norm
											__a					:						int      =					enforce_input_projection
											__a					:						List[str]      =					common_stride
											__a					:						Any      =					ignore_value
											__a					:						Union[str, Any]      =					num_queries
											__a					:						int      =					no_object_weight
											__a					:						Tuple      =					class_weight
											__a					:						int      =					mask_weight
											__a					:						Union[str, Any]      =					dice_weight
											__a					:						int      =					train_num_points
											__a					:						Any      =					oversample_ratio
											__a					:						Any      =					importance_sample_ratio
											__a					:						List[str]      =					init_std
											__a					:						List[str]      =					init_xavier_std
											__a					:						Dict      =					use_auxiliary_loss
											__a					:						List[Any]      =					feature_strides
											__a					:						Tuple      =					output_auxiliary_logits
											__a					:						List[Any]      =					decoder_layers

											super().__init__(**__UpperCAmelCase   )



						@classmethod
						def        UpperCAmelCase__					(cls:					Tuple ,					__UpperCAmelCase:					PretrainedConfig ,					**__UpperCAmelCase:					List[str]   )    ->     Any:




											'''simple docstring'''



											return cls(
											    backbone_config=__UpperCAmelCase ,					**__UpperCAmelCase ,					)



						def        UpperCAmelCase__					(self:					str   )    ->     Dict[str, any]:




											'''simple docstring'''



											__a					:						Tuple      =					copy.deepcopy(self.__dict__   )
											__a					:						List[str]      =					self.backbone_config.to_dict()
											__a					:						Any      =					self.__class__.model_type
											return output





