
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
							import tensorflow as tf

							from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM







@require_tf
@require_sentencepiece
@require_tokenizers
class     lowercase		( unittest.TestCase		):




							@slow
							def 			A__				(    self):
												lowercase						     =						TFAutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''')
												lowercase						     =						AutoTokenizer.from_pretrained('''google/mt5-small''')

												lowercase						     =						tokenizer('''Hello there'''       ,return_tensors='''tf''').input_ids
												lowercase						     =						tokenizer('''Hi I am'''       ,return_tensors='''tf''').input_ids

												lowercase						     =						model(A__       ,labels=A__).loss
												lowercase						     =						-tf.math.reduce_mean(A__).numpy()

												lowercase						     =						-21.228168
												self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 2E-4)






from pathlib import Path

import fire
from tqdm import tqdm



def 	UpperCamelCase   (							lowerCAmelCase__="ro"			,      lowerCAmelCase__="en"			,      lowerCAmelCase__="wmt16"			,      lowerCAmelCase__=None		):
					'''simple docstring'''
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError('''run pip install datasets'''		)
					lowercase						     =						f'{src_lang}-{tgt_lang}'
					print(f'Converting {dataset}-{pair}'		)
					lowercase						     =						datasets.load_dataset(lowerCAmelCase__			,      lowerCAmelCase__		)
					if save_dir is None:
										lowercase						     =						f'{dataset}-{pair}'
					lowercase						     =						Path(lowerCAmelCase__		)
					save_dir.mkdir(exist_ok=lowerCAmelCase__		)

					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
										lowercase						     =						'''val''' if split == '''validation''' else split
										lowercase						     =						save_dir.joinpath(f'{fn}.source'		)
										lowercase						     =						save_dir.joinpath(f'{fn}.target'		)
										lowercase						     =						src_path.open('''w+'''		)
										lowercase						     =						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]		):
															lowercase						     =						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)




