






'''simple docstring'''
import warnings
from typing import List, Optional, Union

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






class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							["""image_processor""", """tokenizer"""]
 snake_case_  							=							"""ViltImageProcessor"""
 snake_case_  							=							("""BertTokenizer""", """BertTokenizerFast""")




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

       SCREAMING_SNAKE_CASE__     :				str									=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__(__lowercase					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=self.image_processor




 def __call__(      self	:							Any					,      __lowercase	:							List[str]					,      __lowercase	:							Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,      __lowercase	:							bool = True					,      __lowercase	:							Union[bool, str, PaddingStrategy] = False					,      __lowercase	:							Union[bool, str, TruncationStrategy] = None					,      __lowercase	:							Optional[int] = None					,      __lowercase	:							int = 0					,      __lowercase	:							Optional[int] = None					,      __lowercase	:							Optional[bool] = None					,      __lowercase	:							Optional[bool] = None					,      __lowercase	:							bool = False					,      __lowercase	:							bool = False					,      __lowercase	:							bool = False					,      __lowercase	:							bool = False					,      __lowercase	:							bool = True					,      __lowercase	:							Optional[Union[str, TensorType]] = None					,      **__lowercase	:							Optional[Any]					,      )	->						BatchEncoding:
       SCREAMING_SNAKE_CASE__     :				Tuple									=self.tokenizer(
           text=__lowercase					,      add_special_tokens=__lowercase					,      padding=__lowercase					,      truncation=__lowercase					,      max_length=__lowercase					,      stride=__lowercase					,      pad_to_multiple_of=__lowercase					,      return_token_type_ids=__lowercase					,      return_attention_mask=__lowercase					,      return_overflowing_tokens=__lowercase					,      return_special_tokens_mask=__lowercase					,      return_offsets_mapping=__lowercase					,      return_length=__lowercase					,      verbose=__lowercase					,      return_tensors=__lowercase					,      **__lowercase					,      )
       # add pixel_values + pixel_mask
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.image_processor(__lowercase					,      return_tensors=__lowercase      )
       encoding.update(__lowercase      )

       return encoding




 def      __magic_name__	(      self	:							Any					,      *__lowercase	:							Any					,      **__lowercase	:							Optional[int]      )	->						List[str]:
       return self.tokenizer.batch_decode(*__lowercase					,      **__lowercase      )




 def      __magic_name__	(      self	:							Any					,      *__lowercase	:							Tuple					,      **__lowercase	:							List[str]      )	->						Union[str, Any]:
       return self.tokenizer.decode(*__lowercase					,      **__lowercase      )




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Dict									=self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )




 @property
 def      __magic_name__	(      self	:							str      )	->						Tuple:
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,      __lowercase					,      )
       return self.image_processor_class




 @property
 def      __magic_name__	(      self	:							Optional[int]      )	->						int:
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,      __lowercase					,      )
       return self.image_processor







'''simple docstring'''
def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=len(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Tuple									=[[False for _ in range(m + 1							)] for _ in range(n + 1							)]
      SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      for i in range(UpperCamelCase__							):
            for j in range(m + 1							):
                  if dp[i][j]:
                        if j < m and a[i].upper() == b[j]:
                              SCREAMING_SNAKE_CASE__     :				Optional[int]									=True
                        if a[i].islower():
                              SCREAMING_SNAKE_CASE__     :				List[Any]									=True
      return dp[n][m]


if __name__ == "__main__":
  import doctest

  doctest.testmod()