






from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
UpperCamelCase			    =     299_792_458

# Symbols
UpperCamelCase			    =     symbols('''ct x y z''')

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""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      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





						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 	__lowerCamelCase      (		snake_case__	,snake_case__ = None		)							->  List[Any]:






						"""simple docstring"""





						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:
				UpperCamelCase			    =     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
				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}")









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


UpperCamelCase			    =     logging.get_logger(__name__)

UpperCamelCase			    =     {
    '''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 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				Any									=							"mobilenet_v1"







			def __init__(     self:  List[str]				,     UpperCAmelCase_:  List[Any]=3				,     UpperCAmelCase_:  List[str]=224				,     UpperCAmelCase_:  Optional[Any]=1.0				,     UpperCAmelCase_:  Dict=8				,     UpperCAmelCase_:  List[str]="relu6"				,     UpperCAmelCase_:  List[Any]=True				,     UpperCAmelCase_:  Dict=0.9_99				,     UpperCAmelCase_:  Union[str, Any]=0.02				,     UpperCAmelCase_:  Tuple=0.0_01				,     **UpperCAmelCase_:  List[str]				,     ):







									'''simple docstring'''




									super().__init__(**UpperCAmelCase_					)

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

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



class 				__UpperCAmelCase		(_UpperCAmelCase   ):
			__snake_case    :				List[Any]									=							version.parse("1.11"   )







			@property
			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									return OrderedDict([("""pixel_values""", {0: """batch"""})]					)







			@property
			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									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:  int					):







									'''simple docstring'''




									return 1E-4

