

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

import os
from pathlib import Path






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					Tuple:
	__lowerCamelCase    :    Any 	=			{
	    '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]
	__lowerCamelCase    :    Any 	=			{
	    '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'],
	}
	__lowerCamelCase    :    Any 	=			F"{src_lang}-{tgt_lang}"

	__lowerCamelCase    :    List[Any] 	=			F"\n---\nlanguage: \n- {src_lang}\n- {tgt_lang}\nthumbnail:\ntags:\n- translation\n- wmt19\n- facebook\nlicense: apache-2.0\ndatasets:\n- wmt19\nmetrics:\n- bleu\n---\n\n# FSMT\n\n## Model description\n\nThis is a ported version of [fairseq wmt19 transformer](https://github.com/pytorch/fairseq/blob/master/examples/wmt19/README.md) for {src_lang}-{tgt_lang}.\n\nFor more details, please see, [Facebook FAIR's WMT19 News Translation Task Submission](https://arxiv.org/abs/1907.06616).\n\nThe abbreviation FSMT stands for FairSeqMachineTranslation\n\nAll four models are available:\n\n* [wmt19-en-ru](https://huggingface.co/facebook/wmt19-en-ru)\n* [wmt19-ru-en](https://huggingface.co/facebook/wmt19-ru-en)\n* [wmt19-en-de](https://huggingface.co/facebook/wmt19-en-de)\n* [wmt19-de-en](https://huggingface.co/facebook/wmt19-de-en)\n\n## Intended uses & limitations\n\n#### How to use\n\n```python\nfrom transformers import FSMTForConditionalGeneration, FSMTTokenizer\nmname = \"facebook/wmt19-{src_lang}-{tgt_lang}\"\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- 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)\n\n## Training data\n\nPretrained weights were left identical to the original model released by fairseq. For more details, please, see the [paper](https://arxiv.org/abs/1907.06616).\n\n## Eval results\n\npair   | fairseq | transformers\n-------|---------|----------\n{pair}  | {scores[pair][0]} | {scores[pair][1]}\n\nThe score is slightly below the score reported by `fairseq`, since `transformers`` currently doesn't support:\n- model ensemble, therefore the best performing checkpoint was ported (``model4.pt``).\n- re-ranking\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=15\nmkdir -p $DATA_DIR\nsacrebleu -t wmt19 -l $PAIR --echo src > $DATA_DIR/val.source\nsacrebleu -t wmt19 -l $PAIR --echo ref > $DATA_DIR/val.target\necho $PAIR\nPYTHONPATH=\"src:examples/seq2seq\" python examples/seq2seq/run_eval.py facebook/wmt19-$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\n```\nnote: fairseq reports using a beam of 50, so you should get a slightly higher score if re-run with `--num_beams 50`.\n\n## Data Sources\n\n- [training, etc.](http://www.statmt.org/wmt19/)\n- [test set](http://matrix.statmt.org/test_sets/newstest2019.tgz?1556572561)\n\n\n### BibTeX entry and citation info\n\n```bibtex\n@inproceedings{{...,\n  year={{2020}},\n  title={{Facebook FAIR's WMT19 News Translation Task Submission}},\n  author={{Ng, Nathan and Yee, Kyra and Baevski, Alexei and Ott, Myle and Auli, Michael and Edunov, Sergey}},\n  booktitle={{Proc. of WMT}},\n}}\n```\n\n\n## TODO\n\n- port model ensemble (fairseq uses 4 model checkpoints)\n\n"
	os.makedirs(lowerCamelCase__     ,			exist_ok=lowerCamelCase__      )
	__lowerCamelCase    :    Any 	=			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
a		=Path(__file__).resolve().parent.parent.parent
a		=repo_dir / """model_cards"""

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







import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a		="""true"""






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

	def collate_fn(lowerCamelCase__      ):
		if use_longest:
			return tokenizer.pad(lowerCamelCase__     ,			padding='longest'     ,			return_tensors='pt'      )
		return tokenizer.pad(lowerCamelCase__     ,			padding='max_length'     ,			max_length=1_2_8     ,			return_tensors='pt'      )

	return DataLoader(lowerCamelCase__     ,			shuffle=lowerCamelCase__     ,			collate_fn=lowerCamelCase__     ,			batch_size=1_6      )






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Optional[int] 	=			Accelerator(dispatch_batches=lowerCamelCase__     ,			split_batches=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			get_dataloader(lowerCamelCase__     ,			not dispatch_batches      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'     ,			return_dict=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = False     ,			lowerCamelCase__ = False      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )
	__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			get_mrpc_setup(lowerCamelCase__     ,			lowerCamelCase__      )
	# First do baseline
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			setup['no']
	model.to(lowerCamelCase__      )
	model.eval()
	for batch in dataloader:
		batch.to(lowerCamelCase__      )
		with torch.inference_mode():
			__lowerCamelCase    :    Dict 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			outputs.logits.argmax(dim=-1      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=batch['labels']      )
	__lowerCamelCase    :    str 	=			metric.compute()

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]     ,			distributed[key]      ), F"Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"






def        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('**Testing gather_for_metrics**'      )
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"      )
				test_mrpc(lowerCamelCase__     ,			lowerCamelCase__      )
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test torch metrics**'      )
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			__lowerCamelCase    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




