
"""simple docstring"""






import warnings

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





class 							snake_case       (     A__		):
   a_					:		Optional[Any]     					=     ["""image_processor""", """tokenizer"""]
   a_					:		Tuple     					=     """AutoImageProcessor"""
   a_					:		Optional[int]     					=     """AutoTokenizer"""







   def __init__( self       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=None       ,							**__UpperCAmelCase)   ->Union[str, 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       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=None       ,							__UpperCAmelCase=None       ,							**__UpperCAmelCase)   ->int:

      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    UpperCAmelCase__	( self       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Dict:
      return self.tokenizer.batch_decode(*_lowerCAmelCase       ,							**_lowerCAmelCase)







   def    UpperCAmelCase__	( self       ,							*__UpperCAmelCase       ,							**__UpperCAmelCase)   ->Optional[int]:
      return self.tokenizer.decode(*_lowerCAmelCase       ,							**_lowerCAmelCase)







   @property
   def    UpperCAmelCase__	( self)   ->str:
      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    UpperCAmelCase__	( self)   ->int:
      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    UpperCAmelCase__	( 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)

