


'''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_   :      Optional[int]				    =      logging.get_logger(__name__)






def 		_A			(lowerCAmelCase__					:Tuple						)				->					List[Any]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Tuple						)				->					Tuple:




     '''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 		_A			(lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Union[str, Any]			,  lowerCAmelCase__					:Any=42						)				->					Dict:




     '''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(lowerCAmelCase__						)						)

     _a      =							flatten_dict(lowerCAmelCase__						)
     _a      =							{}

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

          # Correctly rename weight parameters
          _a ,				_a      =							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
          _a      =							jnp.asarray(lowerCAmelCase__						)

     return unflatten_dict(lowerCAmelCase__						)





'''simple docstring'''






from __future__ import annotations






def 		_A			(lowerCAmelCase__					:int						)				->					list[int]:




     '''simple docstring'''



     _a      =							2
     _a      =							[]
     while i * i <= n:
          if n % i:
               i += 1
          else:
               n //= i
               factors.append(lowerCAmelCase__						)
     if n > 1:
          factors.append(lowerCAmelCase__						)
     return factors


if __name__ == "__main__":
 import doctest

 doctest.testmod()

