




"""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


UpperCamelCase__			:     int			       =				logging.getLogger(__name__)




def    snake_case__						(							):
					__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=1_000					,       help="""Batch size during training."""					,       )
					parser.add_argument(
					    """--vocab_size"""					,       type=lowerCAmelCase					,       default=10_048					,       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"""					,       )

					__snake_case        =						parser.parse_args()
					return args




def    snake_case__						(							lowerCAmelCase :		str		):
					__snake_case        =						datasets.load_dataset(args.dataset_name					,       args.dataset_config					,       split="""train"""		)

					if args.limit is not None:
										__snake_case        =						min(len(lowerCAmelCase		)					,       args.limit		)
										__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.
					__snake_case        =						Tokenizer(Unigram()		)
					__snake_case        =						normalizers.Sequence([normalizers.Replace("""``"""					,       """\""""		), normalizers.Replace("""''"""					,       """\""""		)]		)
					__snake_case        =						pre_tokenizers.Metaspace()

					# Prepare the trainer.
					__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!"""		)

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

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


if __name__ == "__main__":
		UpperCamelCase__			:     Dict			       =				parse_args()
		main(args)








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   """vision-text-dual-encoder"""
	A_				:					Tuple      =   True




	def __init__( self	:						Tuple   ,     lowercase	:						List[Any]=512   ,     lowercase	:						Any=2.65_92   ,     **lowercase	:						int						)       ->			int:
						super().__init__(**lowercase						)

						if "vision_config" not in kwargs:
											raise ValueError("""`vision_config` can not be `None`."""						)

						if "text_config" not in kwargs:
											raise ValueError("""`text_config` can not be `None`."""						)

						__snake_case        =						kwargs.pop("""vision_config"""						)
						__snake_case        =						kwargs.pop("""text_config"""						)

						__snake_case        =						vision_config.pop("""model_type"""						)
						__snake_case        =						text_config.pop("""model_type"""						)

						if vision_model_type == "clip":
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						).vision_config
						elif vision_model_type == "clip_vision_model":
											__snake_case        =						CLIPVisionConfig(**lowercase						)
						else:
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						projection_dim
						__snake_case        =						logit_scale_init_value




	@classmethod
	def 				a_		( cls	:						Union[str, Any]   ,     lowercase	:						PretrainedConfig   ,     lowercase	:						PretrainedConfig   ,     **lowercase	:						Optional[int]						)       ->			Dict:
						return cls(vision_config=vision_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **lowercase						)




	def 				a_		( self	:						Dict						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.vision_config.to_dict()
						__snake_case        =						self.text_config.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


