





'''simple docstring'''


import warnings

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



class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     ['''image_processor''', '''tokenizer''']
		__lowerCAmelCase								=     '''AutoImageProcessor'''
		__lowerCAmelCase								=     '''AutoTokenizer'''







		def __init__(self    :   Optional[int]   ,   _lowerCAmelCase    :   Optional[Any]=None   ,   _lowerCAmelCase    :   Optional[Any]=None   ,   **_lowerCAmelCase    :   List[Any]   ):
			A					=   None
			if "feature_extractor" in kwargs:
				warnings.warn(
				    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
				    """ instead."""   ,   _lowerCAmelCase   ,   )
				A					=   kwargs.pop("""feature_extractor"""   )

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

			super().__init__(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   self.image_processor







		def __call__(self    :   Optional[int]   ,   _lowerCAmelCase    :   str=None   ,   _lowerCAmelCase    :   int=None   ,   _lowerCAmelCase    :   List[Any]=None   ,   **_lowerCAmelCase    :   Union[str, Any]   ):

			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:
				A					=   self.tokenizer(_lowerCAmelCase   ,   return_tensors=_lowerCAmelCase   ,   **_lowerCAmelCase   )

			if images is not None:
				A					=   self.image_processor(_lowerCAmelCase   ,   return_tensors=_lowerCAmelCase   ,   **_lowerCAmelCase   )

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







		def 							A  (self    :   Optional[int]   ,   *_lowerCAmelCase    :   Optional[int]   ,   **_lowerCAmelCase    :   Dict   ):
			return self.tokenizer.batch_decode(*_lowerCAmelCase   ,   **_lowerCAmelCase   )







		def 							A  (self    :   Tuple   ,   *_lowerCAmelCase    :   Optional[int]   ,   **_lowerCAmelCase    :   List[str]   ):
			return self.tokenizer.decode(*_lowerCAmelCase   ,   **_lowerCAmelCase   )







		@property
		def 							A  (self    :   Tuple   ):
			A					=   self.tokenizer.model_input_names
			A					=   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )







		@property
		def 							A  (self    :   Union[str, Any]   ):
			warnings.warn(
			    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""   ,   _lowerCAmelCase   ,   )
			return self.image_processor_class







		@property
		def 							A  (self    :   int   ):
			warnings.warn(
			    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""   ,   _lowerCAmelCase   ,   )
			return self.image_processor








'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
				for line in f_p:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('')
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

