

from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class     __UpperCamelCase     (       _a    ):
	SCREAMING_SNAKE_CASE__     					=  ['''image_processor''', '''tokenizer''']
	SCREAMING_SNAKE_CASE__     					=  '''BlipImageProcessor'''
	SCREAMING_SNAKE_CASE__     					=  ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(		self						:    Optional[int]		,						lowerCAmelCase						:    int		,						lowerCAmelCase						:    Optional[int]				):


		'''simple docstring'''







		UpperCAmelCase_												=      False
		super().__init__(lowerCAmelCase		,						lowerCAmelCase				)
		UpperCAmelCase_												=      self.image_processor




	def __call__(		self						:    Dict		,						lowerCAmelCase						:    ImageInput = None		,						lowerCAmelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,						lowerCAmelCase						:    bool = True		,						lowerCAmelCase						:    Union[bool, str, PaddingStrategy] = False		,						lowerCAmelCase						:    Union[bool, str, TruncationStrategy] = None		,						lowerCAmelCase						:    Optional[int] = None		,						lowerCAmelCase						:    int = 0		,						lowerCAmelCase						:    Optional[int] = None		,						lowerCAmelCase						:    Optional[bool] = None		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = False		,						lowerCAmelCase						:    bool = True		,						lowerCAmelCase						:    Optional[Union[str, TensorType]] = None		,						**lowerCAmelCase						:    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:
			UpperCAmelCase_												=      self.tokenizer
			UpperCAmelCase_												=      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

		# add pixel_values
		UpperCAmelCase_												=      self.image_processor(lowerCAmelCase		,						return_tensors=lowerCAmelCase				)

		if text is not None:
			UpperCAmelCase_												=      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		,						)
		else:
			UpperCAmelCase_												=      None

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

		return encoding_image_processor




	def    __A       (		self						:    List[Any]		,						*lowerCAmelCase						:    int		,						**lowerCAmelCase						:    str				):


		'''simple docstring'''







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




	def    __A       (		self						:    Tuple		,						*lowerCAmelCase						:    List[str]		,						**lowerCAmelCase						:    str				):


		'''simple docstring'''







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




	@property
	def    __A       (		self						:    List[Any]				):


		'''simple docstring'''







		UpperCAmelCase_												=      self.tokenizer.model_input_names
		UpperCAmelCase_												=      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)
_SCREAMING_SNAKE_CASE   =  2_99_79_24_58

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





def        _snake_case   (_snake_case				:       float)					->	float:
   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        _snake_case   (_snake_case				:       float)					->	float:
   return 1 / sqrt(1 - beta(_snake_case) ** 2)





def        _snake_case   (_snake_case				:       float)					->	np.ndarray:
   return np.array(
       [
           [gamma(_snake_case), -gamma(_snake_case) * beta(_snake_case), 0, 0],
           [-gamma(_snake_case) * beta(_snake_case), gamma(_snake_case), 0, 0],
           [0, 0, 1, 0],
           [0, 0, 0, 1],
       ])





def        _snake_case   (_snake_case				:       float      ,    _snake_case				:       np.ndarray | None = None)					->	np.ndarray:
   # Ensure event is not empty
   if event is None:
      _lowercase              =np.array([ct, x, y, z])  # Symbolic four vector
   else:
      event[0] *= c  # x0 is ct (speed of light * time)

   return transformation_matrix(_snake_case) @ event


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Example of symbolic vector:
    _SCREAMING_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
    _SCREAMING_SNAKE_CASE   =  {ct: c, x: 1, y: 1, z: 1}
    _SCREAMING_SNAKE_CASE   =  [four_vector[i].subs(sub_dict) for i in range(4)]

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


