






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 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  ['image_processor', 'tokenizer']
     __lowerCamelCase      				=  'Pix2StructImageProcessor'
     __lowerCamelCase      				=  ('T5Tokenizer', 'T5TokenizerFast')




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Any				      =			False
          super().__init__(_lowerCAmelCase	,						_lowerCAmelCase		)




     def __call__(				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = True	,						_lowerCAmelCase = False	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = 2048	,						_lowerCAmelCase = 0	,						_lowerCAmelCase = None	,						_lowerCAmelCase = None	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						_lowerCAmelCase = False	,						_lowerCAmelCase = True	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          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__      :  Any				      =			self.tokenizer
               UpperCAmelCase__      :  str				      =			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
               UpperCAmelCase__      :  Union[str, Any]				      =			self.image_processor(
                   _lowerCAmelCase	,						return_tensors=_lowerCAmelCase	,						max_patches=_lowerCAmelCase	,						**_lowerCAmelCase		)
          else:
               # add pixel_values and bbox
               UpperCAmelCase__      :  Union[str, Any]				      =			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:
               UpperCAmelCase__      :  List[Any]				      =			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:
                    UpperCAmelCase__      :  List[str]				      =			text_encoding.pop("""attention_mask"""		)
               if "input_ids" in text_encoding:
                    UpperCAmelCase__      :  List[str]				      =			text_encoding.pop("""input_ids"""		)
          else:
               UpperCAmelCase__      :  str				      =			None

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

          return encoding_image_processor




     def   __UpperCAmelCase  (				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          return self.tokenizer.batch_decode(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          return self.tokenizer.decode(*_lowerCAmelCase	,						**_lowerCAmelCase		)




     @property
     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[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		)		)












from math import atan, cos, radians, sin, tan

from .haversine_distance import haversine_distance

_lowerCamelCase      :						Dict							=  6_378_137.0
_lowerCamelCase      :						Union[str, Any]							=  6_356_752.314_245
_lowerCamelCase      :						List[Any]							=  6378137







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		)  ->			float:



       """simple docstring"""

       A__ 					=       (AXIS_A - AXIS_B) / AXIS_A
       # Parametric latitudes
       # https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)
       A__ 					=       atan((1 - flattening) * tan(radians(lowercase_		)		)		)

       # Compute central angle between two points
       # using haversine theta. sigma =  haversine_distance / equatorial radius
       A__ 					=       haversine_distance(lowercase_					,							lowercase_					,							lowercase_					,							lowercase_		) / EQUATORIAL_RADIUS

       # Intermediate P and Q values
       A__ 					=       (b_lata + b_lata) / 2
       A__ 					=       (b_lata - b_lata) / 2

       # Intermediate X value
       # X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
       A__ 					=       (sin(lowercase_		) ** 2) * (cos(lowercase_		) ** 2)
       A__ 					=       cos(sigma / 2		) ** 2
       A__ 					=       (sigma - sin(lowercase_		)) * (x_numerator / x_demonimator)

       # Intermediate Y value
       # Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
       A__ 					=       (cos(lowercase_		) ** 2) * (sin(lowercase_		) ** 2)
       A__ 					=       sin(sigma / 2		) ** 2
       A__ 					=       (sigma + sin(lowercase_		)) * (y_numerator / y_denominator)

       return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))


if __name__ == "__main__":
  import doctest

  doctest.testmod()


