





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_												=     logging.get_logger(__name__)





def    __UpperCamelCase      (    a) ->Optional[int]:
					lowerCamelCase__       =       R"\w+[.]\d+"
					lowerCamelCase__       =       re.findall(a,     a)
					for pat in pats:
										lowerCamelCase__       =       key.replace(a,     "_".join(pat.split(".")))
					return key





def    __UpperCamelCase      (    a,     a,     a) ->Tuple:
					lowerCamelCase__       =       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)
					):
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       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:
										lowerCamelCase__       =       pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

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

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

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

					# old PyTorch layer norm bias
					lowerCamelCase__       =       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      (    a,     a,     a=42) ->Optional[Any]:
					# Step 1: Convert pytorch tensor to numpy
					lowerCamelCase__       =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

					lowerCamelCase__       =       flatten_dict(a)
					lowerCamelCase__       =       {}

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

										# Correctly rename weight parameters
										lowerCamelCase__    ,		lowerCamelCase__       =       rename_key_and_reshape_tensor(a,     a,     a)

										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
										lowerCamelCase__       =       jnp.asarray(a)

					return unflatten_dict(a)








A_												=     {str(digit): digit**5 for digit in range(1_0)}





def    __UpperCamelCase      (    a) ->int:
					return sum(DIGITS_FIFTH_POWER[digit] for digit in str(a))





def    __UpperCamelCase      (    ) ->int:
					return sum(
					    number
					    for number in range(1000,     1000000)
					    if number == digits_fifth_powers_sum(a))


if __name__ == "__main__":
						print(solution())

