





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment





def lowerCAmelCase__(__snake_case ,__snake_case=() ,__snake_case=None ,__snake_case="no" ,__snake_case="29500"							)       ->   Dict:







	'''simple docstring'''







	lowerCamelCase__              =	False
	lowerCamelCase__              =	False
	if any(key.startswith('''KAGGLE'''							) for key in os.environ.keys()							):
		lowerCamelCase__              =	True
	elif "IPython" in sys.modules:
		lowerCamelCase__              =	'''google.colab''' in str(sys.modules['''IPython'''].get_ipython()							)

	try:
		lowerCamelCase__              =	PrecisionType(mixed_precision.lower()							)
	except ValueError:
		raise ValueError(
		    F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.'							)

	if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME''' ,__snake_case							) is not None):
		# TPU launch
		import torch_xla.distributed.xla_multiprocessing as xmp

		if len(AcceleratorState._shared_state							) > 0:
			raise ValueError(
			    '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
			    '''your training function. Restart your notebook and make sure no cells initializes an '''
			    '''`Accelerator`.'''							)
		if num_processes is None:
			lowerCamelCase__              =	8

		lowerCamelCase__              =	PrepareForLaunch(__snake_case ,distributed_type='''TPU'''							)
		print(F'Launching a training on {num_processes} TPU cores.'							)
		xmp.spawn(__snake_case ,args=__snake_case ,nprocs=__snake_case ,start_method='''fork'''							)
	elif in_colab:
		# No need for a distributed launch otherwise as it's either CPU or one GPU.
		if torch.cuda.is_available():
			print('''Launching training on one GPU.'''							)
		else:
			print('''Launching training on one CPU.'''							)
		function(*__snake_case							)
	else:
		if num_processes is None:
			raise ValueError(
			    '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.'''							)

		if num_processes > 1:
			# Multi-GPU launch
			from torch.multiprocessing import start_processes
			from torch.multiprocessing.spawn import ProcessRaisedException

			if len(AcceleratorState._shared_state							) > 0:
				raise ValueError(
				    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
				    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
				    '''`Accelerator`.'''							)

			if torch.cuda.is_initialized():
				raise ValueError(
				    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
				    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
				    '''function.'''							)

			# torch.distributed will expect a few environment variable to be here. We set the ones common to each
			# process here (the other ones will be set be the launcher).
			with patch_environment(
			    world_size=__snake_case ,master_addr='''127.0.01''' ,master_port=__snake_case ,mixed_precision=__snake_case							):
				lowerCamelCase__              =	PrepareForLaunch(__snake_case ,distributed_type='''MULTI_GPU'''							)
				print(F'Launching training on {num_processes} GPUs.'							)
				try:
					start_processes(__snake_case ,args=__snake_case ,nprocs=__snake_case ,start_method='''fork'''							)
				except ProcessRaisedException as e:
					if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
						raise RuntimeError(
						    '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
						    '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
						    '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
						    '''which one is problematic.'''							) from e

		else:
			# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
			if is_mps_available():
				lowerCamelCase__              =	'''1'''
				print('''Launching training on MPS.'''							)
			elif torch.cuda.is_available():
				print('''Launching training on one GPU.'''							)
			else:
				print('''Launching training on CPU.'''							)
			function(*__snake_case							)





def lowerCAmelCase__(__snake_case ,__snake_case=() ,__snake_case=2							)       ->   Optional[Any]:







	'''simple docstring'''







	from torch.multiprocessing import start_processes

	with tempfile.NamedTemporaryFile() as tmp_file:
		# torch.distributed will expect a few environment variable to be here. We set the ones common to each
		# process here (the other ones will be set be the launcher).
		with patch_environment(
		    world_size=__snake_case ,master_addr='''127.0.01''' ,master_port='''29500''' ,accelerate_mixed_precision='''no''' ,accelerate_debug_rdv_file=tmp_file.name ,accelerate_use_cpu='''yes''' ,):
			lowerCamelCase__              =	PrepareForLaunch(__snake_case ,debug=__snake_case							)
			start_processes(__snake_case ,args=__snake_case ,nprocs=__snake_case ,start_method='''fork'''							)










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

import os
from pathlib import Path





def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case							)       ->   Any:







	'''simple docstring'''







	lowerCamelCase__              =	{
	    '''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]
	lowerCamelCase__              =	{
	    '''wmt16-en-de-dist-12-1''': [2_8.3, 2_7.5_2],
	    '''wmt16-en-de-dist-6-1''': [2_7.4, 2_7.1_1],
	    '''wmt16-en-de-12-1''': [2_6.9, 2_5.7_5],
	}
	lowerCamelCase__              =	F'{src_lang}-{tgt_lang}'

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


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



