





'''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


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)
def 	UpperCAmelCase_							(					__lowercase							:      str       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   r"\w+[.]\d+"
     _UpperCAmelCase     							=   re.findall(__lowercase			,     __lowercase       )
     for pat in pats:
          _UpperCAmelCase     							=   key.replace(__lowercase			,     "_".join(pat.split("."       )       )       )
     return key
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      Optional[Any]			,     __lowercase							:      List[Any]       )		->       List[Any]:


     '''simple docstring'''



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

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

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

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

     # old PyTorch layer norm bias
     _UpperCAmelCase     							=   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_							(					__lowercase							:      Tuple			,     __lowercase							:      Dict			,     __lowercase							:      List[str]=42       )		->       Dict:


     '''simple docstring'''



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

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

     _UpperCAmelCase     							=   flatten_dict(__lowercase       )
     _UpperCAmelCase     							=   {}

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

          # Correctly rename weight parameters
          _UpperCAmelCase							,  _UpperCAmelCase     							=   rename_key_and_reshape_tensor(__lowercase			,     __lowercase			,     __lowercase       )

          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
          _UpperCAmelCase     							=   jnp.asarray(__lowercase       )

     return unflatten_dict(__lowercase       )











'''simple docstring'''




import math
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     return math.sqrt(__lowercase       ) * math.sqrt(__lowercase       ) == num
def 	UpperCAmelCase_							(					__lowercase							:      int       )		->       bool:


     '''simple docstring'''



     _UpperCAmelCase     							=   0
     _UpperCAmelCase     							=   n
     while left <= right:
          _UpperCAmelCase     							=   (left + right) // 2
          if mid**2 == n:
               return True
          elif mid**2 > n:
               _UpperCAmelCase     							=   mid - 1
          else:
               _UpperCAmelCase     							=   mid + 1
     return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()




