



"""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 			A__   (  _A							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	["image_processor", "tokenizer"]
	SCREAMING_SNAKE_CASE          =	"BlipImageProcessor"
	SCREAMING_SNAKE_CASE          =	("BertTokenizer", "BertTokenizerFast")

	def __init__(     self:		int       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  int												=       False
				super().__init__(_SCREAMING_SNAKE_CASE       ,			_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase      :  str												=       self.image_processor

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

				"""simple docstring"""

				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:
							__lowerCAmelCase      :  Optional[Any]												=       self.tokenizer
							__lowerCAmelCase      :  Tuple												=       self.tokenizer(
							    text=_SCREAMING_SNAKE_CASE       ,			add_special_tokens=_SCREAMING_SNAKE_CASE       ,			padding=_SCREAMING_SNAKE_CASE       ,			truncation=_SCREAMING_SNAKE_CASE       ,			max_length=_SCREAMING_SNAKE_CASE       ,			stride=_SCREAMING_SNAKE_CASE       ,			pad_to_multiple_of=_SCREAMING_SNAKE_CASE       ,			return_attention_mask=_SCREAMING_SNAKE_CASE       ,			return_overflowing_tokens=_SCREAMING_SNAKE_CASE       ,			return_special_tokens_mask=_SCREAMING_SNAKE_CASE       ,			return_offsets_mapping=_SCREAMING_SNAKE_CASE       ,			return_token_type_ids=_SCREAMING_SNAKE_CASE       ,			return_length=_SCREAMING_SNAKE_CASE       ,			verbose=_SCREAMING_SNAKE_CASE       ,			return_tensors=_SCREAMING_SNAKE_CASE       ,			**_SCREAMING_SNAKE_CASE       ,			)
							return text_encoding

				# add pixel_values
				__lowerCAmelCase      :  List[str]												=       self.image_processor(_SCREAMING_SNAKE_CASE       ,			return_tensors=_SCREAMING_SNAKE_CASE)

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

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

				return encoding_image_processor

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any]       ,			*_SCREAMING_SNAKE_CASE:		str       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       Optional[int]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		int       ,			*_SCREAMING_SNAKE_CASE:		Optional[int]       ,			**_SCREAMING_SNAKE_CASE:		Any)				->       int:

				"""simple docstring"""

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







	@property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.tokenizer.model_input_names
				__lowerCAmelCase      :  List[str]												=       self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))







"""simple docstring"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


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

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



