






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 		lowercase_					(	lowerCamelCase__   ):






      '''simple docstring'''




      __snake_case       =  ['''image_processor''', '''tokenizer''']
      __snake_case       =  '''Pix2StructImageProcessor'''
      __snake_case       =  ('''T5Tokenizer''', '''T5TokenizerFast''')







      def __init__(      self      :						Optional[Any] ,					__UpperCAmelCase      :						str ,					__UpperCAmelCase      :						int  )    ->str:




       """simple docstring"""


       a		  =      False
       super().__init__(lowerCAmelCase__ ,					lowerCAmelCase__  )







      def __call__(      self      :						Dict ,					__UpperCAmelCase      :						Optional[int]=None ,					__UpperCAmelCase      :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Union[bool, str, PaddingStrategy] = False ,					__UpperCAmelCase      :						Union[bool, str, TruncationStrategy] = None ,					__UpperCAmelCase      :						Optional[int] = None ,					__UpperCAmelCase      :						Optional[int] = 2_048 ,					__UpperCAmelCase      :						int = 0 ,					__UpperCAmelCase      :						Optional[int] = None ,					__UpperCAmelCase      :						Optional[bool] = None ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						bool = True ,					__UpperCAmelCase      :						Optional[Union[str, TensorType]] = None ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->List[str]:




       """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 and not self.image_processor.is_vqa:
        a		  =      self.tokenizer
        a		  =      self.tokenizer(
            text=lowerCAmelCase__ ,					add_special_tokens=lowerCAmelCase__ ,					padding=lowerCAmelCase__ ,					truncation=lowerCAmelCase__ ,					max_length=lowerCAmelCase__ ,					stride=lowerCAmelCase__ ,					pad_to_multiple_of=lowerCAmelCase__ ,					return_attention_mask=lowerCAmelCase__ ,					return_overflowing_tokens=lowerCAmelCase__ ,					return_special_tokens_mask=lowerCAmelCase__ ,					return_offsets_mapping=lowerCAmelCase__ ,					return_token_type_ids=lowerCAmelCase__ ,					return_length=lowerCAmelCase__ ,					verbose=lowerCAmelCase__ ,					return_tensors=lowerCAmelCase__ ,					**lowerCAmelCase__ ,					)
        return text_encoding

       if not self.image_processor.is_vqa:
        # add pixel_values
        a		  =      self.image_processor(
            lowerCAmelCase__ ,					return_tensors=lowerCAmelCase__ ,					max_patches=lowerCAmelCase__ ,					**lowerCAmelCase__  )
       else:
        # add pixel_values and bbox
        a		  =      self.image_processor(
            lowerCAmelCase__ ,					return_tensors=lowerCAmelCase__ ,					max_patches=lowerCAmelCase__ ,					header_text=lowerCAmelCase__ ,					**lowerCAmelCase__  )

       if text is not None and not self.image_processor.is_vqa:
        a		  =      self.tokenizer(
            text=lowerCAmelCase__ ,					add_special_tokens=lowerCAmelCase__ ,					padding=lowerCAmelCase__ ,					truncation=lowerCAmelCase__ ,					max_length=lowerCAmelCase__ ,					stride=lowerCAmelCase__ ,					pad_to_multiple_of=lowerCAmelCase__ ,					return_attention_mask=lowerCAmelCase__ ,					return_overflowing_tokens=lowerCAmelCase__ ,					return_special_tokens_mask=lowerCAmelCase__ ,					return_offsets_mapping=lowerCAmelCase__ ,					return_token_type_ids=lowerCAmelCase__ ,					return_length=lowerCAmelCase__ ,					verbose=lowerCAmelCase__ ,					return_tensors=lowerCAmelCase__ ,					**lowerCAmelCase__ ,					)

        if "attention_mask" in text_encoding:
         a		  =      text_encoding.pop('''attention_mask'''  )
        if "input_ids" in text_encoding:
         a		  =      text_encoding.pop('''input_ids'''  )
       else:
        a		  =      None

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

       return encoding_image_processor







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					*__UpperCAmelCase      :						int ,					**__UpperCAmelCase      :						Optional[int]  )    ->Tuple:




       """simple docstring"""


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







      def       __lowerCAmelCase					(      self      :						int ,					*__UpperCAmelCase      :						Optional[int] ,					**__UpperCAmelCase      :						List[Any]  )    ->List[Any]:




       """simple docstring"""


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







      @property
      def       __lowerCAmelCase					(      self      :						List[str]  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      self.tokenizer.model_input_names
       a		  =      self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )







'''simple docstring'''




from math import ceil, sqrt


def 				lowerCAmelCase_   (	_lowerCamelCase:   int = 1_00_00_00					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		0

		for outer_width in range(3       ,      (limit // 4) + 2					):
				if outer_width**2 > limit:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		max(ceil(sqrt(outer_width**2 - limit					)					)       ,      1					)
				else:
						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		1
				if (outer_width - hole_width_lower_bound) % 2:
						hole_width_lower_bound += 1

				answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

		return answer


if __name__ == "__main__":
						print(f"{solution() = }")