'''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 		__A							(A_ ):
       snake_case							:List[str]					     =       ["image_processor", "tokenizer"]
       snake_case							:Tuple					     =       "Pix2StructImageProcessor"
       snake_case							:Any					     =       ("T5Tokenizer", "T5TokenizerFast")






       def __init__(   self					,  UpperCamelCase_					,  UpperCamelCase_   ):
              __UpperCAmelCase	:					Union[str, Any]												=		False
              super().__init__(UpperCamelCase_					,  UpperCamelCase_   )






       def __call__(   self					,  UpperCamelCase_=None					,  UpperCamelCase_ = None					,  UpperCamelCase_ = True					,  UpperCamelCase_ = False					,  UpperCamelCase_ = None					,  UpperCamelCase_ = None					,  UpperCamelCase_ = 20_48					,  UpperCamelCase_ = 0					,  UpperCamelCase_ = None					,  UpperCamelCase_ = None					,  UpperCamelCase_ = False					,  UpperCamelCase_ = False					,  UpperCamelCase_ = False					,  UpperCamelCase_ = False					,  UpperCamelCase_ = False					,  UpperCamelCase_ = True					,  UpperCamelCase_ = None					,  **UpperCamelCase_					,  ):
              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:
                     __UpperCAmelCase	:					List[Any]												=		self.tokenizer
                     __UpperCAmelCase	:					Any												=		self.tokenizer(
                         text=UpperCamelCase_					,  add_special_tokens=UpperCamelCase_					,  padding=UpperCamelCase_					,  truncation=UpperCamelCase_					,  max_length=UpperCamelCase_					,  stride=UpperCamelCase_					,  pad_to_multiple_of=UpperCamelCase_					,  return_attention_mask=UpperCamelCase_					,  return_overflowing_tokens=UpperCamelCase_					,  return_special_tokens_mask=UpperCamelCase_					,  return_offsets_mapping=UpperCamelCase_					,  return_token_type_ids=UpperCamelCase_					,  return_length=UpperCamelCase_					,  verbose=UpperCamelCase_					,  return_tensors=UpperCamelCase_					,  **UpperCamelCase_					,  )
                     return text_encoding

              if not self.image_processor.is_vqa:
                     # add pixel_values
                     __UpperCAmelCase	:					Any												=		self.image_processor(
                         UpperCamelCase_					,  return_tensors=UpperCamelCase_					,  max_patches=UpperCamelCase_					,  **UpperCamelCase_   )
              else:
                     # add pixel_values and bbox
                     __UpperCAmelCase	:					Union[str, Any]												=		self.image_processor(
                         UpperCamelCase_					,  return_tensors=UpperCamelCase_					,  max_patches=UpperCamelCase_					,  header_text=UpperCamelCase_					,  **UpperCamelCase_   )

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

                     if "attention_mask" in text_encoding:
                            __UpperCAmelCase	:					int												=		text_encoding.pop("attention_mask"   )
                     if "input_ids" in text_encoding:
                            __UpperCAmelCase	:					str												=		text_encoding.pop("input_ids"   )
              else:
                     __UpperCAmelCase	:					List[str]												=		None

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

              return encoding_image_processor






       def       _snake_case    (   self					,  *UpperCamelCase_					,  **UpperCamelCase_   ):
              return self.tokenizer.batch_decode(*UpperCamelCase_					,  **UpperCamelCase_   )






       def       _snake_case    (   self					,  *UpperCamelCase_					,  **UpperCamelCase_   ):
              return self.tokenizer.decode(*UpperCamelCase_					,  **UpperCamelCase_   )







       @property
       def       _snake_case    (   self   ):
              __UpperCAmelCase	:					Any												=		self.tokenizer.model_input_names
              __UpperCAmelCase	:					int												=		self.image_processor.model_input_names
              return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )










"""simple docstring"""
from __future__ import annotations

from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray

def 				_snake_case      (   __snake_case							:      float						, __snake_case							:      float						, __snake_case							:      bool = False   ):




   """simple docstring"""







   if radian_mode:
      return [magnitude * cos(__snake_case   ), magnitude * sin(__snake_case   )]
   return [magnitude * cos(radians(__snake_case   )   ), magnitude * sin(radians(__snake_case   )   )]

def 				_snake_case      (   __snake_case							:      NDArray[floataa]						, __snake_case							:      NDArray[floataa]						, __snake_case							:      float = 10**-1   ):




   """simple docstring"""







   _lowerCamelCase	:					NDArray[floataa]   =		cross(__snake_case						, __snake_case   )
   _lowerCamelCase	:					float   =		sum(__snake_case   )
   return abs(__snake_case   ) < eps


if __name__ == "__main__":
 # Test to check if it works
 UpperCAmelCase				=						array(
     [
         polar_force(718.4, 180 - 30),
         polar_force(879.54, 45),
         polar_force(100, -90),
     ]
 )

 UpperCAmelCase				=						array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem 1 in image_data/2D_problems.jpg
 UpperCAmelCase				=						array(
     [
         polar_force(30 * 9.81, 15),
         polar_force(215, 180 - 45),
         polar_force(264, 90 - 30),
     ]
 )

 UpperCAmelCase				=						array([[0, 0], [0, 0], [0, 0]])

 assert in_static_equilibrium(forces, location)

 # Problem in image_data/2D_problems_1.jpg
 UpperCAmelCase				=						array([[0, -2000], [0, -1200], [0, 1_5600], [0, -1_2400]])

 UpperCAmelCase				=						array([[0, 0], [6, 0], [10, 0], [12, 0]])

 assert in_static_equilibrium(forces, location)

 import doctest

 doctest.testmod()

