



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

import os
from pathlib import Path

def   lowerCAmelCase__						(							_SCREAMING_SNAKE_CASE       :			Optional[int]				,     _SCREAMING_SNAKE_CASE       :			List[str]				,     _SCREAMING_SNAKE_CASE       :			int				,     _SCREAMING_SNAKE_CASE       :			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, nicht wahr?""",
				}

				# BLUE scores as follows:
				# "pair": [fairseq, transformers]
				__a       =		{
				    """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       =		f"{src_lang}-{tgt_lang}"

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


# 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 ["wmt16-en-de-dist-12-1", "wmt16-en-de-dist-6-1", "wmt16-en-de-12-1"]:
						lowerCamelCase__			= model_cards_dir / """allenai""" / model_name
						write_model_card(model_card_dir, src_lang="""en""", tgt_lang="""de""", model_name=model_name)





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 the experiment tracking capability,
# 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__						(							_SCREAMING_SNAKE_CASE       :			Accelerator				,     _SCREAMING_SNAKE_CASE       :			int = 16    ):
				"""simple docstring"""







				__a       =		AutoTokenizer.from_pretrained("""bert-base-cased"""    )
				__a       =		load_dataset("""glue"""				,     """mrpc"""    )

				def tokenize_function(_SCREAMING_SNAKE_CASE       :			int    ):
								# max_length=None => use the model max length (it's actually the default)
								__a       =		tokenizer(examples["""sentence1"""]				,     examples["""sentence2"""]				,     truncation=_SCREAMING_SNAKE_CASE				,     max_length=_SCREAMING_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():
								__a       =		datasets.map(
								    _SCREAMING_SNAKE_CASE				,     batched=_SCREAMING_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
				__a       =		tokenized_datasets.rename_column("""label"""				,     """labels"""    )

				def collate_fn(_SCREAMING_SNAKE_CASE       :			Any    ):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__a       =		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":
												__a       =		16
								elif accelerator.mixed_precision != "no":
												__a       =		8
								else:
												__a       =		None

								return tokenizer.pad(
								    _SCREAMING_SNAKE_CASE				,     padding="""longest"""				,     max_length=_SCREAMING_SNAKE_CASE				,     pad_to_multiple_of=_SCREAMING_SNAKE_CASE				,     return_tensors="""pt"""				,     )

				# Instantiate dataloaders.
				__a       =		DataLoader(
				    tokenized_datasets["""train"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_SNAKE_CASE    )
				__a       =		DataLoader(
				    tokenized_datasets["""validation"""]				,     shuffle=_SCREAMING_SNAKE_CASE				,     collate_fn=_SCREAMING_SNAKE_CASE				,     batch_size=_SCREAMING_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__						(							_SCREAMING_SNAKE_CASE       :			Dict				,     _SCREAMING_SNAKE_CASE       :			Dict    ):
				"""simple docstring"""







				if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,     _SCREAMING_SNAKE_CASE    ) == "1":
								__a       =		2
				# Initialize Accelerator

				# New Code #
				# We pass in "all" to `log_with` to grab all available trackers in the environment
				# Note: If using a custom `Tracker` class, should be passed in here such as:
				# >>> log_with = ["all", MyCustomTrackerClassInstance()]
				if args.with_tracking:
								__a       =		Accelerator(
								    cpu=args.cpu				,     mixed_precision=args.mixed_precision				,     log_with="""all"""				,     project_dir=args.project_dir    )
				else:
								__a       =		Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision    )
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__a       =		config["""lr"""]
				__a       =		int(config["""num_epochs"""]    )
				__a       =		int(config["""seed"""]    )
				__a       =		int(config["""batch_size"""]    )
				set_seed(_SCREAMING_SNAKE_CASE    )

				__a     ,					__a       =		get_dataloaders(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				__a       =		evaluate.load("""glue"""				,     """mrpc"""    )

				# If the batch size is too big we use gradient accumulation
				__a       =		1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__a       =		batch_size // MAX_GPU_BATCH_SIZE
								__a       =		MAX_GPU_BATCH_SIZE

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__a       =		AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,     return_dict=_SCREAMING_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).
				__a       =		model.to(accelerator.device    )

				# Instantiate optimizer
				__a       =		AdamW(params=model.parameters()				,     lr=_SCREAMING_SNAKE_CASE    )

				# Instantiate scheduler
				__a       =		get_linear_schedule_with_warmup(
				    optimizer=_SCREAMING_SNAKE_CASE				,     num_warmup_steps=100				,     num_training_steps=(len(_SCREAMING_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.
				__a     ,					__a     ,					__a     ,					__a     ,					__a       =		accelerator.prepare(
				    _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# New Code #
				# We need to initialize the trackers we use. Overall configurations can also be stored
				if args.with_tracking:
								__a       =		os.path.split(_SCREAMING_SNAKE_CASE    )[-1].split("""."""    )[0]
								accelerator.init_trackers(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )

				# Now we train the model
				for epoch in range(_SCREAMING_SNAKE_CASE    ):
								model.train()
								# New Code #
								# For our tracking example, we will log the total loss of each epoch
								if args.with_tracking:
												__a       =		0
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device    )
												__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.loss
												# New Code #
												if args.with_tracking:
																total_loss += loss.detach().float()
												__a       =		loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE    )
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
												# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
												batch.to(accelerator.device    )
												with torch.no_grad():
																__a       =		model(**_SCREAMING_SNAKE_CASE    )
												__a       =		outputs.logits.argmax(dim=-1    )
												__a     ,					__a       =		accelerator.gather_for_metrics((predictions, batch["""labels"""])    )
												metric.add_batch(
												    predictions=_SCREAMING_SNAKE_CASE				,     references=_SCREAMING_SNAKE_CASE				,     )

								__a       =		metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f"epoch {epoch}:"				,     _SCREAMING_SNAKE_CASE    )

								# New Code #
								# To actually log, we call `Accelerator.log`
								# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
								if args.with_tracking:
												accelerator.log(
												    {
												        """accuracy""": eval_metric["""accuracy"""],
												        """f1""": eval_metric["""f1"""],
												        """train_loss""": total_loss.item() / len(_SCREAMING_SNAKE_CASE    ),
												        """epoch""": epoch,
												    }				,     step=_SCREAMING_SNAKE_CASE				,     )

    # New Code #
    # When a run is finished, you should call `accelerator.end_training()`
    # to close all of the open trackers
				if args.with_tracking:
								accelerator.end_training()

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		argparse.ArgumentParser(description="""Simple example of training script."""    )
				parser.add_argument(
				    """--mixed_precision"""				,     type=_SCREAMING_SNAKE_CASE				,     default=_SCREAMING_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."""    )
				parser.add_argument(
				    """--with_tracking"""				,     action="""store_true"""				,     help="""Whether to load in all available experiment trackers from the environment and use them for logging."""				,     )
				parser.add_argument(
				    """--project_dir"""				,     type=_SCREAMING_SNAKE_CASE				,     default="""logs"""				,     help="""Location on where to store experiment tracking logs` and relevent project information"""				,     )
				__a       =		parser.parse_args()
				__a       =		{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
				training_function(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
						main()
