


'''simple docstring'''

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 _lowerCamelCase			(		__UpperCamelCase				):



		'''simple docstring'''







		A_      :     Dict        =							['image_processor', 'tokenizer']
		A_      :     int        =							'Pix2StructImageProcessor'
		A_      :     str        =							('T5Tokenizer', 'T5TokenizerFast')







		def __init__(   self						:		Any    ,	_A						:		Optional[Any]    ,	_A						:		Any     )    ->					Tuple:
			__magic_name__			:       List[Any]	      =	False
			super().__init__(_A    ,	_A     )







		def __call__(   self						:		int    ,	_A						:		Optional[Any]=None    ,	_A						:		Any = None    ,	_A						:		str = True    ,	_A						:		Any = False    ,	_A						:		Any = None    ,	_A						:		Any = None    ,	_A						:		int = 2048    ,	_A						:		int = 0    ,	_A						:		Union[str, Any] = None    ,	_A						:		int = None    ,	_A						:		Union[str, Any] = False    ,	_A						:		Tuple = False    ,	_A						:		Tuple = False    ,	_A						:		List[str] = False    ,	_A						:		List[str] = False    ,	_A						:		Optional[Any] = True    ,	_A						:		int = None    ,	**_A						:		Dict    ,	)    ->					Optional[Any]:
			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 and not self.image_processor.is_vqa:
				__magic_name__			:       Any	      =	self.tokenizer
				__magic_name__			:       Dict	      =	self.tokenizer(
				    text=_A    ,	add_special_tokens=_A    ,	padding=_A    ,	truncation=_A    ,	max_length=_A    ,	stride=_A    ,	pad_to_multiple_of=_A    ,	return_attention_mask=_A    ,	return_overflowing_tokens=_A    ,	return_special_tokens_mask=_A    ,	return_offsets_mapping=_A    ,	return_token_type_ids=_A    ,	return_length=_A    ,	verbose=_A    ,	return_tensors=_A    ,	**_A    ,	)
				return text_encoding

			if not self.image_processor.is_vqa:
				# add pixel_values
				__magic_name__			:       List[Any]	      =	self.image_processor(
				    _A    ,	return_tensors=_A    ,	max_patches=_A    ,	**_A     )
			else:
				# add pixel_values and bbox
				__magic_name__			:       Optional[Any]	      =	self.image_processor(
				    _A    ,	return_tensors=_A    ,	max_patches=_A    ,	header_text=_A    ,	**_A     )

			if text is not None and not self.image_processor.is_vqa:
				__magic_name__			:       List[str]	      =	self.tokenizer(
				    text=_A    ,	add_special_tokens=_A    ,	padding=_A    ,	truncation=_A    ,	max_length=_A    ,	stride=_A    ,	pad_to_multiple_of=_A    ,	return_attention_mask=_A    ,	return_overflowing_tokens=_A    ,	return_special_tokens_mask=_A    ,	return_offsets_mapping=_A    ,	return_token_type_ids=_A    ,	return_length=_A    ,	verbose=_A    ,	return_tensors=_A    ,	**_A    ,	)

				if "attention_mask" in text_encoding:
					__magic_name__			:       List[Any]	      =	text_encoding.pop('attention_mask'     )
				if "input_ids" in text_encoding:
					__magic_name__			:       int	      =	text_encoding.pop('input_ids'     )
			else:
				__magic_name__			:       Any	      =	None

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

			return encoding_image_processor







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







		def   __lowerCAmelCase						(   self						:		List[Any]    ,	*_A						:		List[Any]    ,	**_A						:		Tuple     )    ->					str:
			return self.tokenizer.decode(*_A    ,	**_A     )







		@property
		def   __lowerCAmelCase						(   self						:		str     )    ->					int:
			__magic_name__			:       Union[str, Any]	      =	self.tokenizer.model_input_names
			__magic_name__			:       Any	      =	self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

from __future__ import annotations

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5     ,		int(math.sqrt(__lowerCAmelCase				) + 1				)     ,		6				):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					str(__lowerCAmelCase				)
    snake_case__    =					[n]
    for i in range(1     ,		len(__lowerCAmelCase				)				):
        list_nums.append(int(str_num[i:]				)				)
        list_nums.append(int(str_num[:-i]				)				)
    return list_nums





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if len(str(__lowerCAmelCase				)				) > 3:
        if not is_prime(int(str(__lowerCAmelCase				)[-3:]				)				) or not is_prime(int(str(__lowerCAmelCase				)[:3]				)				):
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 11				):
    snake_case__    =					[]
    snake_case__    =					13
    while len(__lowerCAmelCase				) != count:
        if validate(__lowerCAmelCase				):
            snake_case__    =					list_truncated_nums(__lowerCAmelCase				)
            if all(is_prime(__lowerCAmelCase				) for i in list_nums				):
                list_truncated_primes.append(__lowerCAmelCase				)
        num += 2
    return list_truncated_primes





def 						SCREAMING_SNAKE_CASE__     (   ):
    return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(F'''{sum(compute_truncated_primes(11)) = }''')



