


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class lowerCAmelCase_		(				lowerCamelCase__ ):

	'''simple docstring'''

	__snake_case				=						['image_processor', 'tokenizer']
	__snake_case				=						'CLIPImageProcessor'
	__snake_case				=						('CLIPTokenizer', 'CLIPTokenizerFast')



	def __init__(			self	,   _UpperCAmelCase=None	,   _UpperCAmelCase=None	,   **_UpperCAmelCase      ):
							snake_case_ =   None
							if "feature_extractor" in kwargs:
													warnings.warn(
													    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
													    ''' instead.'''	,   UpperCAmelCase__	,   )
													snake_case_ =   kwargs.pop('''feature_extractor'''      )

							snake_case_ =   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__(UpperCAmelCase__	,   UpperCAmelCase__      )



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

							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:
													snake_case_ =   self.tokenizer(UpperCAmelCase__	,   return_tensors=UpperCAmelCase__	,   **UpperCAmelCase__      )

							if images is not None:
													snake_case_ =   self.image_processor(UpperCAmelCase__	,   return_tensors=UpperCAmelCase__	,   **UpperCAmelCase__      )

							if text is not None and images is not None:
													snake_case_ =   image_features.pixel_values
													return encoding
							elif text is not None:
													return encoding
							else:
													return BatchEncoding(data=dict(**UpperCAmelCase__      )	,   tensor_type=UpperCAmelCase__      )



	def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
							return self.tokenizer.batch_decode(*UpperCAmelCase__	,   **UpperCAmelCase__      )



	def 		UpperCamelCase__  (			self	,   *_UpperCAmelCase	,   **_UpperCAmelCase      ):
							return self.tokenizer.decode(*UpperCAmelCase__	,   **UpperCAmelCase__      )



	@property
	def 		UpperCamelCase__  (			self      ):
							snake_case_ =   self.tokenizer.model_input_names
							snake_case_ =   self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



	@property
	def 		UpperCamelCase__  (			self      ):
							warnings.warn(
							    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,   UpperCAmelCase__	,   )
							return self.image_processor_class





	@property
	def 		UpperCamelCase__  (			self      ):
							warnings.warn(
							    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,   UpperCAmelCase__	,   )
							return self.image_processor



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCAmelCase    =					2_9979_2458

# Symbols
UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase,							UpperCAmelCase    =					symbols("""ct x y z""")






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						float:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						np.ndarray:




      """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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE = None      )->						np.ndarray:




      """simple docstring"""

      if event is None:
            snake_case_ =   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:
   UpperCAmelCase    =					transform(2997_9245)
   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
   UpperCAmelCase    =					{ct: c, x: 1, y: 1, z: 1}
   UpperCAmelCase    =					[four_vector[i].subs(sub_dict) for i in range(4)]

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