






import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       List[Any]   ,  lowerCamelCase_       :       List[str]=1_0_2_4   ,  lowerCamelCase_       :       Optional[Any]=1_0_2_4   ,  lowerCamelCase_       :       Optional[Any]=False   ,  **lowerCamelCase_       :       str				):
			__a						:  Optional[Any]									=     AutoTokenizer.from_pretrained(lowerCamelCase_				)
			__a						:  Tuple									=     SeqaSeqDataset(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  type_path='train'   ,  **lowerCamelCase_				)
			__a						:  Dict									=     tok.pad_token_id

			def get_lens(lowerCamelCase_       :       int				):
						__a						:  Tuple									=     tqdm(
						    DataLoader(lowerCamelCase_   ,  batch_size=5_1_2   ,  num_workers=8   ,  shuffle=lowerCamelCase_   ,  collate_fn=ds.collate_fn				)   ,  desc=str(ds.len_file				)   ,  )
						__a						:  Optional[int]									=     []
						for batch in dl:
									__a						:  int									=     batch['input_ids'].ne(lowerCamelCase_				).sum(1				).tolist()
									__a						:  Optional[Any]									=     batch['labels'].ne(lowerCamelCase_				).sum(1				).tolist()
									if consider_target:
												for src, tgt in zip(lowerCamelCase_   ,  lowerCamelCase_				):
															max_lens.append(max(lowerCamelCase_   ,  lowerCamelCase_				)				)
									else:
												max_lens.extend(lowerCamelCase_				)
						return max_lens

			__a						:  int									=     get_lens(lowerCamelCase_				)
			__a						:  List[Any]									=     SeqaSeqDataset(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  type_path='val'   ,  **lowerCamelCase_				)
			__a						:  Any									=     get_lens(lowerCamelCase_				)
			pickle_save(lowerCamelCase_   ,  train_ds.len_file				)
			pickle_save(lowerCamelCase_   ,  val_ds.len_file				)


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









'''simple docstring'''
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
							import jax

							from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
							from transformers.models.bert.modeling_flax_bert import FlaxBertModel
							from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel






@require_flax
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						@slow
						def 				_UpperCAmelCase       (  self							:      Dict	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													for model_name in ["roberta-base", "roberta-large"]:
																				with self.subTest(lowerCAmelCase	):
																											A_			    =		AutoConfig.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)

																											A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase	)
																											self.assertIsNotNone(lowerCAmelCase	)
																											self.assertIsInstance(lowerCAmelCase ,  lowerCAmelCase	)






						@slow
						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													for model_name in ["bert-base-cased", "bert-large-uncased"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxBertModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Tuple	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						@slow
						def 				_UpperCAmelCase       (  self							:      str	):
													for model_name in ["roberta-base", "roberta-large"]:
																				A_			    =		AutoTokenizer.from_pretrained(lowerCAmelCase	)
																				A_			    =		FlaxRobertaModel.from_pretrained(lowerCAmelCase	)
																				A_			    =		tokenizer("Do you support jax jitted function?" ,  return_tensors=TensorType.JAX	)

																				@jax.jit
																				def eval(**lowerCAmelCase							:      Any	):
																											return model(**lowerCAmelCase	)

																				eval(**lowerCAmelCase	).block_until_ready()






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "bert-base is not a local folder and is not a valid model identifier"	):
																				A_			    =		FlaxAutoModel.from_pretrained("bert-base"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"	):
																				A_			    =		FlaxAutoModel.from_pretrained(lowerCAmelCase ,  revision="aaaaaa"	)






						def 				_UpperCAmelCase       (  self							:      Dict	):
													with self.assertRaisesRegex(
													    lowerCAmelCase ,  "hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack" ,  ):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/config-no-model"	)






						def 				_UpperCAmelCase       (  self							:      Any	):
													with self.assertRaisesRegex(lowerCAmelCase ,  "Use `from_pt=True` to load this model"	):
																				A_			    =		FlaxAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only"	)





