





'''simple docstring'''



from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
__magic_name__   :						Any    =			299_792_458

# Symbols
__magic_name__   ,    __magic_name__   ,    __magic_name__   ,    __magic_name__   :						Any    =			symbols("""ct x y z""")




def 				snake_case_      (		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 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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




def 				snake_case_      (		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 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None       ):







  '''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:
 __magic_name__   :						str    =			transform(29_979_245)
 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
 __magic_name__   :						Optional[Any]    =			{ct: c, x: 1, y: 1, z: 1}
 __magic_name__   :						Tuple    =			[four_vector[i].subs(sub_dict) for i in range(4)]

 print(F'\n{numerical_vector}')









'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__   :						Dict    =			logging.get_logger(__name__)

__magic_name__   :						Dict    =			{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						List[Any]      =   '''mobilenet_v1'''

     def __init__(					self						,   lowerCamelCase=3						,   lowerCamelCase=224						,   lowerCamelCase=1.0						,   lowerCamelCase=8						,   lowerCamelCase="relu6"						,   lowerCamelCase=True						,   lowerCamelCase=0.999						,   lowerCamelCase=0.02						,   lowerCamelCase=0.001						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)

       if depth_multiplier <= 0:
         raise ValueError("depth_multiplier must be greater than zero."	)

       _snake_case     		=				num_channels
       _snake_case     		=				image_size
       _snake_case     		=				depth_multiplier
       _snake_case     		=				min_depth
       _snake_case     		=				hidden_act
       _snake_case     		=				tf_padding
       _snake_case     		=				classifier_dropout_prob
       _snake_case     		=				initializer_range
       _snake_case     		=				layer_norm_eps







class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						int      =   version.parse('''1.11'''			)

     @property
     def 	UpperCamelCase(					self	):
       return OrderedDict([("pixel_values", {0: "batch"})]	)

     @property
     def 	UpperCamelCase(					self	):
       if self.task == "image-classification":
         return OrderedDict([("logits", {0: "batch"})]	)
       else:
         return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]	)






     @property
     def 	UpperCamelCase(					self	):
       return 1e-4


