"""simple docstring"""



import warnings
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   _A						(    SCREAMING_SNAKE_CASE__       ):




    """simple docstring"""




    UpperCamelCase_						:       Tuple      					=       ['''image_processor''', '''tokenizer''']
    UpperCamelCase_						:       List[Any]      					=       '''FlavaImageProcessor'''
    UpperCamelCase_						:       int      					=       ('''BertTokenizer''', '''BertTokenizerFast''')
    def __init__(		self				:  Union[str, Any]						,   A_				:  Any=None						,   A_				:  Optional[Any]=None						,   **A_				:  int     )     ->							List[Any]:
         __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.'''						,   snake_case__						,   )
              __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__(snake_case__						,   snake_case__     )
         __snake_case				=				self.image_processor
    def __call__(		self				:  Tuple						,   A_				:  Optional[ImageInput] = None						,   A_				:  Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None						,   A_				:  bool = True						,   A_				:  Union[bool, str, PaddingStrategy] = False						,   A_				:  Union[bool, str, TruncationStrategy] = False						,   A_				:  Optional[int] = None						,   A_				:  int = 0						,   A_				:  Optional[int] = None						,   A_				:  Optional[bool] = None						,   A_				:  Optional[bool] = None						,   A_				:  Optional[bool] = None						,   A_				:  Optional[bool] = None						,   A_				:  bool = False						,   A_				:  bool = False						,   A_				:  bool = False						,   A_				:  bool = False						,   A_				:  bool = True						,   A_				:  Optional[Union[str, TensorType]] = None						,   **A_				:  Union[str, Any]						,   )     ->							int:

         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(
                  text=snake_case__						,   add_special_tokens=snake_case__						,   padding=snake_case__						,   truncation=snake_case__						,   max_length=snake_case__						,   stride=snake_case__						,   pad_to_multiple_of=snake_case__						,   return_token_type_ids=snake_case__						,   return_attention_mask=snake_case__						,   return_overflowing_tokens=snake_case__						,   return_special_tokens_mask=snake_case__						,   return_offsets_mapping=snake_case__						,   return_length=snake_case__						,   verbose=snake_case__						,   return_tensors=snake_case__						,   **snake_case__						,   )
         if images is not None:
              __snake_case				=				self.image_processor(
                  snake_case__						,   return_image_mask=snake_case__						,   return_codebook_pixels=snake_case__						,   return_tensors=snake_case__						,   **snake_case__						,   )

         if text is not None and images is not None:
              encoding.update(snake_case__     )
              return encoding
         elif text is not None:
              return encoding
         else:
              return BatchEncoding(data=dict(**snake_case__     )						,   tensor_type=snake_case__     )
    def 			lowercase       (		self				:  Dict						,   *A_				:  str						,   **A_				:  Union[str, Any]     )     ->							List[Any]:
         return self.tokenizer.batch_decode(*snake_case__						,   **snake_case__     )
    def 			lowercase       (		self				:  Union[str, Any]						,   *A_				:  Dict						,   **A_				:  int     )     ->							Optional[int]:
         return self.tokenizer.decode(*snake_case__						,   **snake_case__     )
    @property
    def 			lowercase       (		self				:  str     )     ->							Tuple:
         __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 			lowercase       (		self				:  str     )     ->							str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''						,   snake_case__						,   )
         return self.image_processor_class







    @property
    def 			lowercase       (		self				:  Dict     )     ->							Optional[int]:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''						,   snake_case__						,   )
         return self.image_processor





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
SCREAMING_SNAKE_CASE:     Optional[int]									= 2_9_9_7_9_2_4_5_8

# Symbols
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Tuple									= symbols('''ct x y z''')


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


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


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase = None )->	np.ndarray:
		# Ensure event is not empty
		if event is None:
				SCREAMING_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(lowerCAmelCase ) @ event


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# Example of symbolic vector:
						SCREAMING_SNAKE_CASE:     int									= transform(2_9_9_7_9_2_4_5)
						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:     List[Any]									= {ct: c, x: 1, y: 1, z: 1}
						SCREAMING_SNAKE_CASE:     Optional[Any]									= [four_vector[i].subs(sub_dict) for i in range(4)]

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