



"""simple docstring"""






import warnings
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 	__snake_case	(     _lowercase):
  snake_case__    :       Any        =						["image_processor", "tokenizer"]
  snake_case__    :       Dict        =						"FlavaImageProcessor"
  snake_case__    :       Dict        =						("BertTokenizer", "BertTokenizerFast")




  def __init__(			self					:						Any						, __lowerCAmelCase					:						List[Any]=None						, __lowerCAmelCase					:						Optional[int]=None						, **__lowerCAmelCase					:						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			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						, )
                _lowerCamelCase						:    int         =			kwargs.pop('''feature_extractor'''		)

         _lowerCamelCase						:    Any         =			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__(__lowerCAmelCase						, __lowerCAmelCase		)
         _lowerCamelCase						:    Dict         =			self.image_processor




  def __call__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Optional[ImageInput] = None						, __lowerCAmelCase					:						Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None						, __lowerCAmelCase					:						bool = True						, __lowerCAmelCase					:						Union[bool, str, PaddingStrategy] = False						, __lowerCAmelCase					:						Union[bool, str, TruncationStrategy] = False						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						int = 0						, __lowerCAmelCase					:						Optional[int] = None						, __lowerCAmelCase					:						Optional[bool] = None						, __lowerCAmelCase					:						Optional[bool] = None						, __lowerCAmelCase					:						Optional[bool] = None						, __lowerCAmelCase					:						Optional[bool] = None						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						bool = False						, __lowerCAmelCase					:						bool = True						, __lowerCAmelCase					:						Optional[Union[str, TensorType]] = None						, **__lowerCAmelCase					:						Dict						, ):






         """simple docstring"""






         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						:    Dict         =			self.tokenizer(
                    text=__lowerCAmelCase						, add_special_tokens=__lowerCAmelCase						, padding=__lowerCAmelCase						, truncation=__lowerCAmelCase						, max_length=__lowerCAmelCase						, stride=__lowerCAmelCase						, pad_to_multiple_of=__lowerCAmelCase						, return_token_type_ids=__lowerCAmelCase						, return_attention_mask=__lowerCAmelCase						, return_overflowing_tokens=__lowerCAmelCase						, return_special_tokens_mask=__lowerCAmelCase						, return_offsets_mapping=__lowerCAmelCase						, return_length=__lowerCAmelCase						, verbose=__lowerCAmelCase						, return_tensors=__lowerCAmelCase						, **__lowerCAmelCase						, )
         if images is not None:
                _lowerCamelCase						:    str         =			self.image_processor(
                    __lowerCAmelCase						, return_image_mask=__lowerCAmelCase						, return_codebook_pixels=__lowerCAmelCase						, return_tensors=__lowerCAmelCase						, **__lowerCAmelCase						, )

         if text is not None and images is not None:
                encoding.update(__lowerCAmelCase		)
                return encoding
         elif text is not None:
                return encoding
         else:
                return BatchEncoding(data=dict(**__lowerCAmelCase		)						, tensor_type=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, *__lowerCAmelCase					:						int						, **__lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         return self.tokenizer.batch_decode(*__lowerCAmelCase						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[str]						, *__lowerCAmelCase					:						str						, **__lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         return self.tokenizer.decode(*__lowerCAmelCase						, **__lowerCAmelCase		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						Any		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[int]         =			self.tokenizer.model_input_names
         _lowerCamelCase						:    Dict         =			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)




  @property
  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]		):






         """simple docstring"""






         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 						SCREAMING_SNAKE_CASE    (			self					:						str		):






         """simple docstring"""






         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						, __lowerCAmelCase						, )
         return self.image_processor







"""simple docstring"""






from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[Any]         =			[randint(-10_00,    10_00						) for i in range(10						)]
       _lowerCamelCase						:    Tuple         =			randint(-50_00,    50_00						)
       return (arr, r)


lowerCAmelCase__      =    make_dataset()






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       for triplet in permutations(A_,    3						):
              if sum(A_						) == target:
                     return tuple(sorted(A_						)						)
       return (0, 0, 0)






def 	snake_case_  (				A_ :		list[int],    A_ :		int						):


       '''simple docstring'''





       arr.sort()
       _lowerCamelCase						:    Optional[Any]         =			len(A_						)
       for i in range(n - 1						):
              _lowerCamelCase      ,   _lowerCamelCase						:    int         =			i + 1, n - 1
              while left < right:
                     if arr[i] + arr[left] + arr[right] == target:
                            return (arr[i], arr[left], arr[right])
                     elif arr[i] + arr[left] + arr[right] < target:
                            left += 1
                     elif arr[i] + arr[left] + arr[right] > target:
                            right -= 1
       return (0, 0, 0)






def 	snake_case_  (				):


       '''simple docstring'''





       _lowerCamelCase						:    Tuple         =			'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       _lowerCamelCase						:    List[Any]         =			'''
triplet_sum1(*dataset)
'''
       _lowerCamelCase						:    str         =			'''
triplet_sum2(*dataset)
'''
       _lowerCamelCase						:    Optional[int]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       _lowerCamelCase						:    Union[str, Any]         =			repeat(setup=A_,    stmt=A_,    repeat=5,    number=1_00_00						)
       return (min(A_						), min(A_						))


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   lowerCAmelCase__      =    solution_times()
   print(F"""The time for naive implementation is {times[0]}.""")
   print(F"""The time for optimized implementation is {times[1]}.""")


