


"""simple docstring"""



import unittest

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


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel







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







			@slow
			def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   XLMRobertaModel.from_pretrained("xlm-roberta-base"						)
							snake_case   :				str				 =   torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]						)
							# The dog is cute and lives in the garden house

							snake_case   :				Tuple				 =   torch.Size((1, 12, 768)						)  # batch_size, sequence_length, embedding_vector_dim
							snake_case   :				Optional[Any]				 =   torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]						)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
											snake_case   :				Any				 =   model(_A						)['last_hidden_state'].detach()
							self.assertEqual(output.shape						,     _A						)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,     _A						,     atol=1e-3						)						)







			@slow
			def 			lowerCamelCase		(     self						)     ->		Union[str, Any]:

							'''simple docstring'''




							snake_case   :				Any				 =   XLMRobertaModel.from_pretrained("xlm-roberta-large"						)
							snake_case   :				List[str]				 =   torch.tensor([[0, 581, 1_0269, 83, 9_9942, 136, 6_0742, 23, 70, 8_0583, 1_8276, 2]]						)
							# The dog is cute and lives in the garden house

							snake_case   :				Any				 =   torch.Size((1, 12, 1024)						)  # batch_size, sequence_length, embedding_vector_dim
							snake_case   :				List[Any]				 =   torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]						)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
											snake_case   :				Optional[int]				 =   model(_A						)['last_hidden_state'].detach()
							self.assertEqual(output.shape						,     _A						)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]						,     _A						,     atol=1e-3						)						)









"""simple docstring"""



from pathlib import Path

import fire
from tqdm import tqdm







def   __lowerCAmelCase						(						lowercase  :  Optional[Any]="ro"						,							lowercase  :  Union[str, Any]="en"						,							lowercase  :  str="wmt16"						,							lowercase  :  Any=None			)     ->						None:


    """simple docstring"""





    try:
        import datasets
    except (ModuleNotFoundError, ImportError):
        raise ImportError("run pip install datasets"			)
    snake_case   :				Any				 =   F'{src_lang}-{tgt_lang}'
    print(F'Converting {dataset}-{pair}'			)
    snake_case   :				Union[str, Any]				 =   datasets.load_dataset(lowercase						,							lowercase			)
    if save_dir is None:
        snake_case   :				int				 =   F'{dataset}-{pair}'
    snake_case   :				Optional[Any]				 =   Path(lowercase			)
    save_dir.mkdir(exist_ok=lowercase			)

    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
        snake_case   :				Any				 =   "val" if split == "validation" else split
        snake_case   :				List[str]				 =   save_dir.joinpath(F'{fn}.source'			)
        snake_case   :				int				 =   save_dir.joinpath(F'{fn}.target'			)
        snake_case   :				str				 =   src_path.open("w+"			)
        snake_case   :				Optional[int]				 =   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]			):
            snake_case   :				int				 =   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)





