


"""simple docstring"""







from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a__    :							str            =					typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a__    :							Union[str, Any]            =					typing.Union[np.floataa, int, float]  # noqa: UP007




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.sqrt(np.sum((np.asarray(lowerCAmelCase_       ) - np.asarray(lowerCAmelCase_       )) ** 2       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return sum((va - va) ** 2 for va, va in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       ) ** (1 / 2)


if __name__ == "__main__":




		def        UpperCAmelCase__	():




							'''simple docstring'''


							from timeit import timeit

							print("Without Numpy"       )
							print(
							    timeit(
							        "euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"  ,						number=1_0000  ,						globals=globals()  ,						)       )
							print("With Numpy"       )
							print(
							    timeit(
							        "euclidean_distance([1, 2, 3], [4, 5, 6])"  ,						number=1_0000  ,						globals=globals()  ,						)       )



		benchmark()




"""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


a__    :							Union[str, Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    R"\w+[.]\d+"
					__SCREAMING_SNAKE_CASE						       =    re.findall(lowerCAmelCase_  ,						lowerCAmelCase_       )
					for pat in pats:
										__SCREAMING_SNAKE_CASE						       =    key.replace(lowerCAmelCase_  ,						"_".join(pat.split("."       )       )       )
					return key




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_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)
					):
										__SCREAMING_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:
										__SCREAMING_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:
										__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

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

					# linear layer
					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("kernel",)
					if pt_tuple_key[-1] == "weight":
										__SCREAMING_SNAKE_CASE						       =    pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					__SCREAMING_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
					__SCREAMING_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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=42       ):




					'''simple docstring'''


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

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

					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}

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

										# Correctly rename weight parameters
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    rename_key_and_reshape_tensor(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

										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
										__SCREAMING_SNAKE_CASE						       =    jnp.asarray(lowerCAmelCase_       )

					return unflatten_dict(lowerCAmelCase_       )
