

from pathlib import Path

import fire
from tqdm import tqdm




def 					_lowercase     (				a_				:					Optional[Any]="ro"					,a_				:					Optional[Any]="en"					,a_				:					int="wmt16"					,a_				:					List[Any]=None				)				->	None:




					'''simple docstring'''







					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('run pip install datasets'				)
					__magic_name__							=    F'{src_lang}-{tgt_lang}'
					print(F'Converting {dataset}-{pair}'				)
					__magic_name__							=    datasets.load_dataset(a_					,a_				)
					if save_dir is None:
										__magic_name__							=    F'{dataset}-{pair}'
					__magic_name__							=    Path(a_				)
					save_dir.mkdir(exist_ok=a_				)

					for split in ds.keys():
										print(F'Splitting {split} with {ds[split].num_rows} records'				)

										# to save to val.source, val.target like summary datasets
										__magic_name__							=    'val' if split == 'validation' else split
										__magic_name__							=    save_dir.joinpath(F'{fn}.source'				)
										__magic_name__							=    save_dir.joinpath(F'{fn}.target'				)
										__magic_name__							=    src_path.open('w+'				)
										__magic_name__							=    tgt_path.open('w+'				)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]				):
															__magic_name__							=    x['translation']
															src_fp.write(ex[src_lang] + '\n'				)
															tgt_fp.write(ex[tgt_lang] + '\n'				)

					print(F'Saved {dataset} dataset to {save_dir}'				)


if __name__ == "__main__":
						fire.Fire(download_wmt_dataset)







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
						from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 				__UpperCamelCase				(					unittest.TestCase			):







	@slow
	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple      ):

						'''simple docstring'''





						__magic_name__							=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  , return_dict=__UpperCamelCase      ).to(__UpperCamelCase      )
						__magic_name__							=    AutoTokenizer.from_pretrained('google/mt5-small'      )

						__magic_name__							=    tokenizer('Hello there'  , return_tensors='pt'      ).input_ids
						__magic_name__							=    tokenizer('Hi I am'  , return_tensors='pt'      ).input_ids

						__magic_name__							=    model(input_ids.to(__UpperCamelCase      )  , labels=labels.to(__UpperCamelCase      )      ).loss
						__magic_name__							=    -(labels.shape[-1] * loss.item())

						__magic_name__							=    -84.9127
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1E-4      )




