


"""simple docstring"""






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  =	logging.getLogger(__name__)


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

				SCREAMING_SNAKE_CASE__  				=					parser.parse_args()
				return args


def __lowercase  (   lowerCamelCase_	:   Optional[Any]	):
				SCREAMING_SNAKE_CASE__  				=					datasets.load_dataset(args.dataset_name    ,       args.dataset_config    ,       split="train"	)

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

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

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

				# Prepare the trainer.
				SCREAMING_SNAKE_CASE__  				=					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=lowerCamelCase_	)
				logger.info("Tokenizer training complete!"	)

				SCREAMING_SNAKE_CASE__  				=					tokenizer.token_to_id("[CLS]"	)
				SCREAMING_SNAKE_CASE__  				=					tokenizer.token_to_id("[SEP]"	)
				SCREAMING_SNAKE_CASE__  				=					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),
				    ]    ,       )
				SCREAMING_SNAKE_CASE__  				=					decoders.Metaspace()

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


if __name__ == "__main__":
	_lowerCamelCase  =	parse_args()
	main(args)




"""simple docstring"""






import torch

from transformers import AutoTokenizer


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					AutoTokenizer.from_pretrained(UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):

								if isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					None
												for l in x:
																SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
																    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)
																SCREAMING_SNAKE_CASE__  				=					torch.tensor([len(UpperCAmelCase__  )]  )
																if d is not None:
																				for k in d.keys():
																								SCREAMING_SNAKE_CASE__  				=					torch.cat((d[k], t[k])							,		0  )
																else:
																				SCREAMING_SNAKE_CASE__  				=					t

												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"  )  )
												SCREAMING_SNAKE_CASE__  				=					torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"  )  )

								elif isinstance(UpperCAmelCase__							,		UpperCAmelCase__  ) and all(isinstance(_x							,		UpperCAmelCase__  ) for _x in x  ):
												SCREAMING_SNAKE_CASE__  				=					self.tokenizer(
												    UpperCAmelCase__							,		padding="max_length"							,		max_length=384							,		truncation=UpperCAmelCase__							,		return_tensors="pt"							,		)

								else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."  )

								return d






				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__=0.70  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for idx in range(len(W_query["input_ids"]  )  ):
												SCREAMING_SNAKE_CASE__  				=					SCREAMING_SNAKE_CASE__  				=					range(p_start.shape[1]  )

												SCREAMING_SNAKE_CASE__  				=					[]
												for start_id in start_indexes:
																for end_id in end_indexes:
																				if start_id < end_id:
																								output.append(
																								    (
																								        start_id,
																								        end_id,
																								        p_start[idx][start_id].item(),
																								        p_end[idx][end_id].item(),
																								    )  )

												output.sort(key=lambda UpperCAmelCase__  : (tup[2] * tup[3])							,		reverse=UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					[]
												for k in range(len(UpperCAmelCase__  )  ):
																if output[k][2] * output[k][3] >= thresh:
																				SCREAMING_SNAKE_CASE__				,   SCREAMING_SNAKE_CASE__  				=					output[k][0], output[k][1]
																				SCREAMING_SNAKE_CASE__  				=					self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]  )
																				temp.append((decoded, output[k][2] * output[k][3])  )

												final_outputs.append(UpperCAmelCase__  )

								return final_outputs
