
"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase__        =		16
lowerCamelCase__        =		32
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase = 16		):
		__lowerCAmelCase      :						List[Any]						      =				AutoTokenizer.from_pretrained('bert-base-cased'		)
		__lowerCAmelCase      :						int						      =				load_dataset('glue'	,  'mrpc'		)

		def tokenize_function(_UpperCamelCase		):
				# max_length=None => use the model max length (it's actually the default)
				__lowerCAmelCase      :						str						      =				tokenizer(examples['sentence1']	,  examples['sentence2']	,  truncation=__snake_case	,  max_length=__snake_case		)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				__lowerCAmelCase      :						Dict						      =				datasets.map(
				    __snake_case	,  batched=__snake_case	,  remove_columns=['idx', 'sentence1', 'sentence2']	,  )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		__lowerCAmelCase      :						List[str]						      =				tokenized_datasets.rename_column('label'	,  'labels'		)

		def collate_fn(_UpperCamelCase		):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				__lowerCAmelCase      :						List[str]						      =				128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						__lowerCAmelCase      :						int						      =				16
				elif accelerator.mixed_precision != "no":
						__lowerCAmelCase      :						Optional[int]						      =				8
				else:
						__lowerCAmelCase      :						List[Any]						      =				None

				return tokenizer.pad(
				    __snake_case	,  padding='longest'	,  max_length=__snake_case	,  pad_to_multiple_of=__snake_case	,  return_tensors='pt'	,  )

		# Instantiate dataloaders.
		__lowerCAmelCase      :						Union[str, Any]						      =				DataLoader(
		    tokenized_datasets['train']	,  shuffle=__snake_case	,  collate_fn=__snake_case	,  batch_size=__snake_case		)
		__lowerCAmelCase      :						Optional[int]						      =				DataLoader(
		    tokenized_datasets['validation']	,  shuffle=__snake_case	,  collate_fn=__snake_case	,  batch_size=__snake_case		)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
							from accelerate.test_utils.training import mocked_dataloaders

							lowerCamelCase__        =		mocked_dataloaders  # noqa: F811
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase		):
		# For testing only
		if os.environ.get('TESTING_MOCKED_DATALOADERS'	,  __snake_case		) == "1":
				__lowerCAmelCase      :						Tuple						      =				2
		# Initialize accelerator
		__lowerCAmelCase      :						Optional[Any]						      =				Accelerator(cpu=args.cpu	,  mixed_precision=args.mixed_precision		)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		__lowerCAmelCase      :						Any						      =				config["lr"]
		__lowerCAmelCase      :						Any						      =				int(config['num_epochs']		)
		__lowerCAmelCase      :						Any						      =				int(config['seed']		)
		__lowerCAmelCase      :						Union[str, Any]						      =				int(config['batch_size']		)

		__lowerCAmelCase      :						Tuple						      =				evaluate.load('glue'	,  'mrpc'		)

		# If the batch size is too big we use gradient accumulation
		__lowerCAmelCase      :						Union[str, Any]						      =				1
		if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
				__lowerCAmelCase      :						List[Any]						      =				batch_size // MAX_GPU_BATCH_SIZE
				__lowerCAmelCase      :						Optional[int]						      =				MAX_GPU_BATCH_SIZE

		set_seed(__snake_case		)
		__lowerCAmelCase      :						str						      =				get_dataloaders(__snake_case	,  __snake_case		)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		__lowerCAmelCase      :						Union[str, Any]						      =				AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,  return_dict=__snake_case		)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		__lowerCAmelCase      :						Optional[Any]						      =				model.to(accelerator.device		)

		# Instantiate optimizer
		__lowerCAmelCase      :						str						      =				AdamW(params=model.parameters()	,  lr=__snake_case		)

		# Instantiate scheduler
		__lowerCAmelCase      :						Optional[Any]						      =				get_linear_schedule_with_warmup(
		    optimizer=__snake_case	,  num_warmup_steps=100	,  num_training_steps=(len(__snake_case		) * num_epochs) // gradient_accumulation_steps	,  )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		__lowerCAmelCase      :						List[str]						      =				accelerator.prepare(
		    __snake_case	,  __snake_case	,  __snake_case	,  __snake_case	,  __snake_case		)

		# Now we train the model
		for epoch in range(__snake_case		):
				model.train()
				for step, batch in enumerate(__snake_case		):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device		)
						__lowerCAmelCase      :						List[Any]						      =				model(**__snake_case		)
						__lowerCAmelCase      :						Dict						      =				outputs.loss
						__lowerCAmelCase      :						str						      =				loss / gradient_accumulation_steps
						accelerator.backward(__snake_case		)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				__lowerCAmelCase      :						List[str]						      =				0
				for step, batch in enumerate(__snake_case		):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device		)
						with torch.no_grad():
								__lowerCAmelCase      :						Union[str, Any]						      =				model(**__snake_case		)
						__lowerCAmelCase      :						str						      =				outputs.logits.argmax(dim=-1		)
						__lowerCAmelCase      :						Union[str, Any]						      =				accelerator.gather((predictions, batch['labels'])		)
						# New Code #
						# First we check if it's a distributed system
						if accelerator.use_distributed:
								# Then see if we're on the last batch of our eval dataloader
								if step == len(__snake_case		) - 1:
										# Last batch needs to be truncated on distributed systems as it contains additional samples
										__lowerCAmelCase      :						List[str]						      =				predictions[: len(eval_dataloader.dataset		) - samples_seen]
										__lowerCAmelCase      :						Any						      =				references[: len(eval_dataloader.dataset		) - samples_seen]
								else:
										# Otherwise we add the number of samples seen
										samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
						metric.add_batch(
						    predictions=__snake_case	,  references=__snake_case	,  )

				__lowerCAmelCase      :						Tuple						      =				metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(F"epoch {epoch}:"	,  __snake_case		)
def    __lowerCAmelCase    ():
		__lowerCAmelCase      :						List[Any]						      =				argparse.ArgumentParser(description='Simple example of training script.'		)
		parser.add_argument(
		    '--mixed_precision'	,  type=__snake_case	,  default=__snake_case	,  choices=['no', 'fp16', 'bf16', 'fp8']	,  help='Whether to use mixed precision. Choose'
		    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
		    'and an Nvidia Ampere GPU.'	,  )
		parser.add_argument('--cpu'	,  action='store_true'	,  help='If passed, will train on the CPU.'		)
		__lowerCAmelCase      :						Union[str, Any]						      =				parser.parse_args()
		__lowerCAmelCase      :						Tuple						      =				{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
		training_function(__snake_case	,  __snake_case		)


if __name__ == "__main__":
							main()

"""simple docstring"""







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

import os
from pathlib import Path
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	__lowerCAmelCase      :						str						      =				{
	    '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      :						Union[str, 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      :						List[str]						      =				F"{src_lang}-{tgt_lang}"

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


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

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