

"""simple docstring"""





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







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["image_processor", "tokenizer"]
					lowercase__				 =       "AutoImageProcessor"
					lowercase__				 =       "AutoTokenizer"







					def __init__(       self			:				Any   ,lowercase_			:				List[str]   ,lowercase_			:				str		):
						super().__init__(lowercase_   ,lowercase_		)
						lowerCAmelCase__	:						List[Any]  				=	self.image_processor







					def __call__(       self			:				str   ,lowercase_			:				Any=None   ,lowercase_			:				Any=None   ,lowercase_			:				Optional[Any]=None   ,**lowercase_			:				Dict		):

						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:
							lowerCAmelCase__	:						List[str]  				=	self.tokenizer(lowercase_   ,return_tensors=lowercase_   ,**lowercase_		)

						if images is not None:
							lowerCAmelCase__	:						Optional[int]  				=	self.image_processor(lowercase_   ,return_tensors=lowercase_   ,**lowercase_		)

						if text is not None and images is not None:
							lowerCAmelCase__	:						Any  				=	image_features.pixel_values
							return encoding
						elif text is not None:
							return encoding
						else:
							return BatchEncoding(data=dict(**lowercase_		)   ,tensor_type=lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,*lowercase_			:				List[Any]   ,**lowercase_			:				Optional[Any]		):
						return self.tokenizer.batch_decode(*lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,*lowercase_			:				int   ,**lowercase_			:				Optional[int]		):
						return self.tokenizer.decode(*lowercase_   ,**lowercase_		)







					@property
					def        __lowerCAmelCase (       self			:				List[Any]		):
						return ["input_ids", "attention_mask", "pixel_values"]







"""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  :      Any							      =		logging.getLogger(__name__)

if __name__ == "__main__":
							__UpperCamelCase  :      Union[str, Any]							      =		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_0_5_2_2, type=int)
							__UpperCamelCase  :      Any							      =		parser.parse_args()

							logger.info(F'''Loading data from {args.data_file}''')
							with open(args.data_file, '''rb''') as fp:
														__UpperCamelCase  :      List[Any]							      =		pickle.load(fp)

							logger.info('''Counting occurrences for MLM.''')
							__UpperCamelCase  :      Dict							      =		Counter()
							for tk_ids in data:
														counter.update(tk_ids)
							__UpperCamelCase  :      Optional[Any]							      =		[0] * args.vocab_size
							for k, v in counter.items():
														__UpperCamelCase  :      Dict							      =		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)




