"""simple docstring"""
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[Any]   )   ->			Union[str, Any]:





						"""simple docstring"""






						snake_case        =     r'\w+[.]\d+'
						snake_case        =     re.findall(_UpperCamelCase	,     _UpperCamelCase   )
						for pat in pats:
												snake_case        =     key.replace(_UpperCamelCase	,     '_'.join(pat.split('.'   )   )   )
						return key





def 				lowerCAmelCase__      (     _UpperCamelCase    :			Tuple	,     _UpperCamelCase    :			int	,     _UpperCamelCase    :			int   )   ->			str:





						"""simple docstring"""






						snake_case        =     pt_tuple_key[:-1] + ('scale',)
						if (
						    any('norm' in str_ for str_ in pt_tuple_key   )
						    and (pt_tuple_key[-1] == "bias")
						    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
						    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
						):
												snake_case        =     pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor
						elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
												snake_case        =     pt_tuple_key[:-1] + ('scale',)
												return renamed_pt_tuple_key, pt_tensor

						# embedding
						if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
												snake_case        =     pt_tuple_key[:-1] + ('embedding',)
												return renamed_pt_tuple_key, pt_tensor

						# conv layer
						snake_case        =     pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
												snake_case        =     pt_tensor.transpose(2	,     3	,     1	,     0   )
												return renamed_pt_tuple_key, pt_tensor

						# linear layer
						snake_case        =     pt_tuple_key[:-1] + ('kernel',)
						if pt_tuple_key[-1] == "weight":
												snake_case        =     pt_tensor.T
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm weight
						snake_case        =     pt_tuple_key[:-1] + ('weight',)
						if pt_tuple_key[-1] == "gamma":
												return renamed_pt_tuple_key, pt_tensor

						# old PyTorch layer norm bias
						snake_case        =     pt_tuple_key[:-1] + ('bias',)
						if pt_tuple_key[-1] == "beta":
												return renamed_pt_tuple_key, pt_tensor

						return pt_tuple_key, pt_tensor





def 				lowerCAmelCase__      (     _UpperCamelCase    :			List[str]	,     _UpperCamelCase    :			Dict	,     _UpperCamelCase    :			Dict=4_2   )   ->			Dict:





						"""simple docstring"""






						snake_case        =     {k: v.numpy() for k, v in pt_state_dict.items()}

						# Step 2: Since the model is stateless, get random Flax params
						snake_case        =     flax_model.init_weights(PRNGKey(_UpperCamelCase   )   )

						snake_case        =     flatten_dict(_UpperCamelCase   )
						snake_case        =     {}

						# Need to change some parameters name to match Flax names
						for pt_key, pt_tensor in pt_state_dict.items():
												snake_case        =     rename_key(_UpperCamelCase   )
												snake_case        =     tuple(renamed_pt_key.split('.'   )   )

												# Correctly rename weight parameters
												snake_case   ,snake_case        =     rename_key_and_reshape_tensor(_UpperCamelCase	,     _UpperCamelCase	,     _UpperCamelCase   )

												if flax_key in random_flax_state_dict:
																		if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																								raise ValueError(
																								    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																								    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""   )

        # also add unexpected weight so that warning is thrown
												snake_case        =     jnp.asarray(_UpperCamelCase   )

						return unflatten_dict(_UpperCamelCase   )



"""simple docstring"""
import cmath
import math





def 				lowerCAmelCase__      (     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float	,     _UpperCamelCase    :			float   )   ->			complex:





						"""simple docstring"""






						snake_case        =     math.radians(_UpperCamelCase   )
						snake_case        =     math.radians(_UpperCamelCase   )

						# Convert voltage and current to rectangular form
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )
						snake_case        =     cmath.rect(_UpperCamelCase	,     _UpperCamelCase   )

						# Calculate apparent power
						return voltage_rect * current_rect


if __name__ == "__main__":
					import doctest

					doctest.testmod()


