



"""simple docstring"""

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class    __lowercase     (					_UpperCAmelCase):







						"""simple docstring"""







						_A		:							Optional[Any]							       =		["""image_processor""", """tokenizer"""]
						_A		:							str							       =		"""BlipImageProcessor"""
						_A		:							List[Any]							       =		"""AutoTokenizer"""







						def __init__(self   ,		lowercase__   ,		lowercase__       ):
											snake_case_					:		Any 					=					False
											super().__init__(lowercase__   ,		lowercase__       )
											snake_case_					:		List[Any] 					=					self.image_processor







						def __call__(self   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = True   ,		lowercase__ = False   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = 0   ,		lowercase__ = None   ,		lowercase__ = None   ,		lowercase__ = False   ,		lowercase__ = False   ,		lowercase__ = False   ,		lowercase__ = False   ,		lowercase__ = False   ,		lowercase__ = True   ,		lowercase__ = None   ,		**lowercase__   ,		):
											if images is None and text is None:
																raise ValueError("""You have to specify either images or text."""       )

											# Get only text
											if images is None:
																snake_case_					:		Optional[Any] 					=					self.tokenizer
																snake_case_					:		int 					=					self.tokenizer(
																    text=lowercase__   ,		add_special_tokens=lowercase__   ,		padding=lowercase__   ,		truncation=lowercase__   ,		max_length=lowercase__   ,		stride=lowercase__   ,		pad_to_multiple_of=lowercase__   ,		return_attention_mask=lowercase__   ,		return_overflowing_tokens=lowercase__   ,		return_special_tokens_mask=lowercase__   ,		return_offsets_mapping=lowercase__   ,		return_token_type_ids=lowercase__   ,		return_length=lowercase__   ,		verbose=lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__   ,		)
																return text_encoding

											# add pixel_values
											snake_case_					:		int 					=					self.image_processor(lowercase__   ,		return_tensors=lowercase__       )

											if text is not None:
																snake_case_					:		Any 					=					self.tokenizer(
																    text=lowercase__   ,		add_special_tokens=lowercase__   ,		padding=lowercase__   ,		truncation=lowercase__   ,		max_length=lowercase__   ,		stride=lowercase__   ,		pad_to_multiple_of=lowercase__   ,		return_attention_mask=lowercase__   ,		return_overflowing_tokens=lowercase__   ,		return_special_tokens_mask=lowercase__   ,		return_offsets_mapping=lowercase__   ,		return_token_type_ids=lowercase__   ,		return_length=lowercase__   ,		verbose=lowercase__   ,		return_tensors=lowercase__   ,		**lowercase__   ,		)
											else:
																snake_case_					:		Union[str, Any] 					=					None

											if text_encoding is not None:
																encoding_image_processor.update(lowercase__       )

											return encoding_image_processor







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.batch_decode(*lowercase__   ,		**lowercase__       )







						def 						__UpperCamelCase							(self   ,		*lowercase__   ,		**lowercase__       ):
											return self.tokenizer.decode(*lowercase__   ,		**lowercase__       )







						@property
						# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
						def 						__UpperCamelCase							(self       ):
											snake_case_					:		str 					=					self.tokenizer.model_input_names
											snake_case_					:		List[str] 					=					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
)
a_			   =					logging.getLogger(__name__)

if __name__ == "__main__":
			a_			   =					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=30522, type=int)
			a_			   =					parser.parse_args()

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

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




