



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


UpperCAmelCase_							:      Any      =  logging.get_logger(__name__)
def  UpperCamelCase						(       _A			:				List[str]    )->       Optional[Any]:





      """simple docstring"""





      A__							=       R"\w+[.]\d+"
      A__							=       re.findall(_A			,							_A    )
      for pat in pats:
            A__							=       key.replace(_A			,							"_".join(pat.split("."    )    )    )
      return key
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				Optional[Any]			,							_A			:				Optional[Any]    )->       str:





      """simple docstring"""





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

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

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

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

      # old PyTorch layer norm bias
      A__							=       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			:				int			,							_A			:				Union[str, Any]			,							_A			:				Optional[int]=42    )->       str:





      """simple docstring"""





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

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

      A__							=       flatten_dict(_A    )
      A__							=       {}

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

            # Correctly rename weight parameters
            A__						,       A__							=       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
            A__							=       jnp.asarray(_A    )

      return unflatten_dict(_A    )










from __future__ import annotations






class      UpperCamelCase    :



	def __init__(       self			,	UpperCAmelCase__=None		):
							A__							=       data
							A__							=       None



	def __repr__(       self		):
							A__							=       []
							A__							=       self
							while temp:
													string_rep.append(F"""{temp.data}"""		)
													A__							=       temp.next
							return "->".join(UpperCAmelCase__		)





def  UpperCamelCase						(       _A			:				list    )->       Dict:





						"""simple docstring"""





						if not elements_list:
												raise Exception("The Elements List is empty"    )

						A__							=       A__							=       Node(elements_list[0]    )
						for i in range(1			,							len(_A    )    ):
												A__							=       Node(elements_list[i]    )
												A__							=       current.next
						return head
def  UpperCamelCase						(       _A			:				Node    )->       None:





						"""simple docstring"""





						if head_node is not None and isinstance(_A			,							_A    ):
												print_reverse(head_node.next    )
												print(head_node.data    )
def  UpperCamelCase						(       )->       Tuple:





						"""simple docstring"""





						from doctest import testmod

						testmod()

						A__							=       make_linked_list([14, 52, 14, 12, 43]    )
						print("Linked List:"    )
						print(_A    )
						print("Elements in Reverse:"    )
						print_reverse(_A    )


if __name__ == "__main__":
						main()





