





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


lowercase      :       int									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		Union[str, Any]:
       a__						:	Any =				R'\w+[.]\d+'
       a__						:	int =				re.findall(A__		,      A__				)
       for pat in pats:
              a__						:	List[Any] =				key.replace(A__		,      '_'.join(pat.split('.'				)				)				)
       return key



def A_	(		A__		,      A__		,      A__				)  ->		Dict:
       a__						:	Optional[Any] =				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__						:	str =				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__						:	Optional[Any] =				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__						:	Dict =				pt_tuple_key[:-1] + ('embedding',)
              return renamed_pt_tuple_key, pt_tensor

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

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

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

       # old PyTorch layer norm bias
       a__						:	str =				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 A_	(		A__		,      A__		,      A__=42				)  ->		str:
       # Step 1: Convert pytorch tensor to numpy
       a__						:	int =				{k: v.numpy() for k, v in pt_state_dict.items()}

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

       a__						:	Dict =				flatten_dict(A__				)
       a__						:	Optional[Any] =				{}

       # Need to change some parameters name to match Flax names
       for pt_key, pt_tensor in pt_state_dict.items():
              a__						:	Tuple =				rename_key(A__				)
              a__						:	List[str] =				tuple(renamed_pt_key.split('.'				)				)

              # Correctly rename weight parameters
              a__ ,							a__						:	Tuple =				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__						:	str =				jnp.asarray(A__				)

       return unflatten_dict(A__				)









from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


