


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		["image_processor", "tokenizer"]
   __A :      str     		=		"CLIPImageProcessor"
   __A :      List[str]     		=		("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")






   def __init__(		self						,				__A=None						,				__A=None						,				**__A ):






     """simple docstring"""





     lowerCamelCase :					str    =				None
     if "feature_extractor" in kwargs:
       warnings.warn(
           "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
           " instead."						,				__A						,				)
       lowerCamelCase :					Any    =				kwargs.pop("feature_extractor" )

     lowerCamelCase :					Any    =				image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError("You need to specify an `image_processor`." )
     if tokenizer is None:
       raise ValueError("You need to specify a `tokenizer`." )

     super().__init__(__A						,				__A )






   def __call__(		self						,				__A=None						,				__A=None						,				__A=None						,				**__A ):






     """simple docstring"""





     if text is None and images is None:
       raise ValueError("You have to specify either text or images. Both cannot be none." )

     if text is not None:
       lowerCamelCase :					Union[str, Any]    =				self.tokenizer(__A						,				return_tensors=__A						,				**__A )

     if images is not None:
       lowerCamelCase :					Tuple    =				self.image_processor(__A						,				return_tensors=__A						,				**__A )

     if text is not None and images is not None:
       lowerCamelCase :					Optional[Any]    =				image_features.pixel_values
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**__A )						,				tensor_type=__A )






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   def _snake_case    (		self						,				*__A						,				**__A ):






     """simple docstring"""





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






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     lowerCamelCase :					Union[str, Any]    =				self.tokenizer.model_input_names
     lowerCamelCase :					Optional[int]    =				self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )







from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_snake_case             =     2_99_79_24_58

# Symbols
_snake_case      ,       _snake_case      ,       _snake_case      ,       _snake_case             =     symbols('''ct x y z''')






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  if velocity > c:
    raise ValueError("Speed must not exceed light speed 299,792,458 [m/s]!"	)
  elif velocity < 1:
    # Usually the speed should be much higher than 1 (c order of magnitude)
    raise ValueError("Speed must be greater than or equal to 1!"	)

  return velocity / c






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_	) ** 2	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  return np.array(
      [
          [gamma(SCREAMING_SNAKE_CASE_	), -gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), 0, 0],
          [-gamma(SCREAMING_SNAKE_CASE_	) * beta(SCREAMING_SNAKE_CASE_	), gamma(SCREAMING_SNAKE_CASE_	), 0, 0],
          [0, 0, 1, 0],
          [0, 0, 0, 1],
      ]	)






def 		lowercase_(  SCREAMING_SNAKE_CASE_      ,						SCREAMING_SNAKE_CASE_ = None	):


  '''simple docstring'''

  if event is None:
    lowerCamelCase :					Tuple    =				np.array([ct, x, y, z]	)  # Symbolic four vector
  else:
    event[0] *= c  # x0 is ct (speed of light * time)

  return transformation_matrix(SCREAMING_SNAKE_CASE_	) @ event


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   # Example of symbolic vector:
   _snake_case             =     transform(29_97_92_45)
   print('''Example of four vector: ''')
   print(f'''ct\' = {four_vector[0]}''')
   print(f'''x\' = {four_vector[1]}''')
   print(f'''y\' = {four_vector[2]}''')
   print(f'''z\' = {four_vector[3]}''')

   # Substitute symbols with numerical values
   _snake_case             =     {ct: c, x: 1, y: 1, z: 1}
   _snake_case             =     [four_vector[i].subs(sub_dict) for i in range(4)]

   print(f'''\n{numerical_vector}''')



