


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_				:			Dict   =	logging.get_logger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]    )     ->		List[str]:

       """simple docstring"""

       a_ :		str							=  R'\w+[.]\d+'
       a_ :		List[str]							=  re.findall(__A  ,   __A    )
       for pat in pats:
              a_ :		str							=  key.replace(__A  ,   '_'.join(pat.split('.'    )    )    )
       return key







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      Optional[Any]  ,   __A			:      Any    )     ->		List[str]:

       """simple docstring"""

       a_ :		str							=  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_ :		Tuple							=  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_ :		int							=  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_ :		Optional[Any]							=  pt_tuple_key[:-1] + ('embedding',)
              return renamed_pt_tuple_key, pt_tensor

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

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

       # old PyTorch layer norm weight
       a_ :		str							=  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 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[Any]  ,   __A			:      Optional[Any]  ,   __A			:      Union[str, Any]=42    )     ->		List[Any]:

       """simple docstring"""

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

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

       a_ :		List[Any]							=  flatten_dict(__A    )
       a_ :		int							=  {}

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

              # Correctly rename weight parameters
              a_							,		a_ :		Union[str, Any]							=  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_ :		int							=  jnp.asarray(__A    )

       return unflatten_dict(__A    )






def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		int:

       """simple docstring"""

       while b:
              a_							,		a_ :		int							=  b, a % b
       return a







def 	SCREAMING_SNAKE_CASE_						(      __A			:      int  ,   __A			:      int    )     ->		int:

       """simple docstring"""

       return a if b == 0 else euclidean_gcd_recursive(__A  ,   a % b    )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		str:

       """simple docstring"""

       print(F"""euclidean_gcd(3, 5) = {euclidean_gcd(3  ,   5    )}"""    )
       print(F"""euclidean_gcd(5, 3) = {euclidean_gcd(5  ,   3    )}"""    )
       print(F"""euclidean_gcd(1, 3) = {euclidean_gcd(1  ,   3    )}"""    )
       print(F"""euclidean_gcd(3, 6) = {euclidean_gcd(3  ,   6    )}"""    )
       print(F"""euclidean_gcd(6, 3) = {euclidean_gcd(6  ,   3    )}"""    )

       print(F"""euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3  ,   5    )}"""    )
       print(F"""euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5  ,   3    )}"""    )
       print(F"""euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1  ,   3    )}"""    )
       print(F"""euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3  ,   6    )}"""    )
       print(F"""euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6  ,   3    )}"""    )


if __name__ == "__main__":
   main()


