



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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_						=       logging.get_logger(__name__)

lowercase_						=       {
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class 			_snake_case (   lowercase__      ,		lowercase__):
				UpperCamelCase__			:   Tuple       						="""resnet"""
				UpperCamelCase__			:   str       						=["""basic""", """bottleneck"""]




				def __init__(  self   :  str,   __lowercase   :  Dict=3,   __lowercase   :  Optional[int]=64,   __lowercase   :  Tuple=[256, 512, 1024, 2048],   __lowercase   :  Optional[Any]=[3, 4, 6, 3],   __lowercase   :  Optional[Any]="bottleneck",   __lowercase   :  Optional[int]="relu",   __lowercase   :  Tuple=False,   __lowercase   :  Union[str, Any]=None,   __lowercase   :  Optional[int]=None,   **__lowercase   :  Optional[int],   ):
							super().__init__(**__lowercase  )
							if layer_type not in self.layer_types:
										raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types  )}'''  )
							lowercase__   					=			num_channels
							lowercase__   					=			embedding_size
							lowercase__   					=			hidden_sizes
							lowercase__   					=			depths
							lowercase__   					=			layer_type
							lowercase__   					=			hidden_act
							lowercase__   					=			downsample_in_first_stage
							lowercase__   					=			["stem"] + [F'''stage{idx}''' for idx in range(1,   len(__lowercase  ) + 1  )]
							lowercase__					,    lowercase__   					=			get_aligned_output_features_output_indices(
							    out_features=__lowercase,   out_indices=__lowercase,   stage_names=self.stage_names  )
class 			_snake_case (   lowercase__):
				UpperCamelCase__			:   List[str]       						=version.parse("""1.11""")




				@property
				def 							A__ (  self   :  Tuple  ):
							return OrderedDict(
							    [
							        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
							    ]  )




				@property
				def 							A__ (  self   :  Optional[Any]  ):
							return 1e-3









from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
lowercase_						=       2_9979_2458

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



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			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	(       SCREAMING_SNAKE_CASE_					):
			return 1 / sqrt(1 - beta(SCREAMING_SNAKE_CASE_					) ** 2					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = None					):
			# 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(SCREAMING_SNAKE_CASE_					) @ event


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Example of symbolic vector:
							lowercase_						=       transform(2997_9245)
							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
							lowercase_						=       {ct: c, x: 1, y: 1, z: 1}
							lowercase_						=       [four_vector[i].subs(sub_dict) for i in range(4)]

							print(F'\n{numerical_vector}')




