


"""simple docstring"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding





class    lowerCamelCase__						(		_a    ):
      a :     List[Any]											=		["""image_processor""", """tokenizer"""]
      a :     Union[str, Any]											=		"""CLIPImageProcessor"""
      a :     Union[str, Any]											=		("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")
      def __init__(		self	:	Union[str, Any]      ,       A_	:	Any=None      ,       A_	:	Dict=None      ,       **A_	:	Optional[Any]							):






       '''simple docstring'''


       __lowercase									=	None
       if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""      ,       A_      ,       )
        __lowercase									=	kwargs.pop("""feature_extractor"""							)

       __lowercase									=	image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""							)
       if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""							)

       super().__init__(A_      ,       A_							)
      def __call__(		self	:	Optional[Any]      ,       A_	:	Dict=None      ,       A_	:	Dict=None      ,       A_	:	str=None      ,       **A_	:	Any							):






       '''simple docstring'''


       if text is None and images is None:
        raise ValueError("""You have to specify either text or images. Both cannot be none."""							)

       if text is not None:
        __lowercase									=	self.tokenizer(A_      ,       return_tensors=A_      ,       **A_							)

       if images is not None:
        __lowercase									=	self.image_processor(A_      ,       return_tensors=A_      ,       **A_							)

       if text is not None and images is not None:
        __lowercase									=	image_features.pixel_values
        return encoding
       elif text is not None:
        return encoding
       else:
        return BatchEncoding(data=dict(**A_							)      ,       tensor_type=A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	Union[str, Any]      ,       *A_	:	Union[str, Any]      ,       **A_	:	Optional[Any]							):






       '''simple docstring'''


       return self.tokenizer.batch_decode(*A_      ,       **A_							)
      def SCREAMING_SNAKE_CASE_	(		self	:	str      ,       *A_	:	Tuple      ,       **A_	:	Union[str, Any]							):






       '''simple docstring'''


       return self.tokenizer.decode(*A_      ,       **A_							)






      @property
      def SCREAMING_SNAKE_CASE_	(		self	:	Dict							):






       '''simple docstring'''


       __lowercase									=	self.tokenizer.model_input_names
       __lowercase									=	self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




"""simple docstring"""

import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__  							=logging.getLogger(__name__)

if __name__ == "__main__":
       UpperCAmelCase__  							=argparse.ArgumentParser(
           description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
       )
       parser.add_argument(
           "--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
       )
       parser.add_argument(
           "--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
       )
       parser.add_argument("--vocab_size", default=3_0522, type=int)
       UpperCAmelCase__  							=parser.parse_args()

       logger.info(f"""Loading data from {args.data_file}""")
       with open(args.data_file, "rb") as fp:
              UpperCAmelCase__  							=pickle.load(fp)

       logger.info("Counting occurrences for MLM.")
       UpperCAmelCase__  							=Counter()
       for tk_ids in data:
              counter.update(tk_ids)
       UpperCAmelCase__  							=[0] * args.vocab_size
       for k, v in counter.items():
              UpperCAmelCase__  							=v

       logger.info(f"""Dump to {args.token_counts_dump}""")
       with open(args.token_counts_dump, "wb") as handle:
              pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
