





from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowerCAmelCase_    =				2_9_9_7_9_2_4_5_8

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






def      lowerCamelCase_						(				_UpperCamelCase )		->						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_						(				_UpperCamelCase )		->						float:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase )		->						np.ndarray:






				"""simple docstring"""


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






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase = None )		->						np.ndarray:






				"""simple docstring"""


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

				return transformation_matrix(_UpperCamelCase ) @ event


if __name__ == "__main__":
	import doctest

	doctest.testmod()

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

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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_    =				logging.get_logger(__name__)

lowerCAmelCase_    =				{
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 					__lowerCAmelCase       (			_a     ):
			lowerCamelCase_     :     Any       			=      '''megatron-bert'''






			def __init__(self					,							__magic_name__=2_9056					,							__magic_name__=1024					,							__magic_name__=24					,							__magic_name__=16					,							__magic_name__=4096					,							__magic_name__="gelu"					,							__magic_name__=0.1					,							__magic_name__=0.1					,							__magic_name__=512					,							__magic_name__=2					,							__magic_name__=0.02					,							__magic_name__=1e-12					,							__magic_name__=0					,							__magic_name__="absolute"					,							__magic_name__=True					,							**__magic_name__					,							)     ->   Any:


							'''simple docstring'''



							super().__init__(pad_token_id=__magic_name__					,							**__magic_name__    )

							snake_case_						:    Union[str, Any]				=  vocab_size
							snake_case_						:    Dict				=  hidden_size
							snake_case_						:    Dict				=  num_hidden_layers
							snake_case_						:    Optional[int]				=  num_attention_heads
							snake_case_						:    int				=  hidden_act
							snake_case_						:    List[str]				=  intermediate_size
							snake_case_						:    Dict				=  hidden_dropout_prob
							snake_case_						:    str				=  attention_probs_dropout_prob
							snake_case_						:    str				=  max_position_embeddings
							snake_case_						:    Any				=  type_vocab_size
							snake_case_						:    int				=  initializer_range
							snake_case_						:    int				=  layer_norm_eps
							snake_case_						:    List[str]				=  position_embedding_type
							snake_case_						:    Dict				=  use_cache
