




import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


__lowerCAmelCase		:    Dict	      =		logging.getLogger(__name__)




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."       )
			parser.add_argument(
			    "--dataset_name",  type=A,  default="wikitext",  help="Name of the training. Explore datasets at: hf.co/datasets.",  )
			parser.add_argument(
			    "--dataset_config",  type=A,  default="wikitext-103-raw-v1",  help="Configuration name of the dataset."       )
			parser.add_argument(
			    "--batch_size",  type=A,  default=1000,  help="Batch size during training.",  )
			parser.add_argument(
			    "--vocab_size",  type=A,  default=10048,  help="Size of the desired vocabulary.",  )
			parser.add_argument(
			    "--limit",  default=A,  type=A,  help="Limit the number of shards (used for debugging).",  )
			parser.add_argument(
			    "--export_to_hub",  action="store_true",  )

			a   =      parser.parse_args()
			return args




def       __magic_name__    (			A		: List[str]       ):






			'''simple docstring'''







			a   =      datasets.load_dataset(args.dataset_name,  args.dataset_config,  split="train"       )

			if args.limit is not None:
						a   =      min(len(A       ),  args.limit       )
						a   =      dataset.select(range(A       )       )
						logger.info(F"""Limiting the dataset to {args.limit} entries."""       )

			def batch_iterator():
						for i in range(0,  len(A       ),  args.batch_size       ):
									yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
			a   =      Tokenizer(Unigram()       )
			a   =      normalizers.Sequence([normalizers.Replace("``",  "\""       ), normalizers.Replace("''",  "\""       )]       )
			a   =      pre_tokenizers.Metaspace()

			# Prepare the trainer.
			a   =      UnigramTrainer(
			    unk_token="<unk>",  special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"],  vocab_size=args.vocab_size,  )

			logger.info("Training the tokenizer."       )
			tokenizer.train_from_iterator(batch_iterator(),  trainer=A       )
			logger.info("Tokenizer training complete!"       )

			a   =      tokenizer.token_to_id("[CLS]"       )
			a   =      tokenizer.token_to_id("[SEP]"       )
			a   =      processors.TemplateProcessing(
			    single="[CLS]:0 $A:0 [SEP]:0",  pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",  special_tokens=[
			        ("[CLS]", cls_token_id),
			        ("[SEP]", sep_token_id),
			    ],  )
			a   =      decoders.Metaspace()

			if args.export_to_hub:
						logger.info("Exporting the trained tokenzier to Hub."       )
						a   =      AlbertTokenizerFast(tokenizer_object=A       )
						new_tokenizer.push_to_hub("unigram-tokenizer-dataset"       )


if __name__ == "__main__":
		__lowerCAmelCase		:    List[str]	      =		parse_args()
		main(args)












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():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






