


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


snake_case							:       int										=	logging.get_logger(__name__)
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int		):




      """simple docstring"""





      a		:Union[str, Any]									=			R'''\w+[.]\d+'''
      a		:Dict									=			re.findall(UpperCAmelCase_					,    UpperCAmelCase_		)
      for pat in pats:
            a		:List[Any]									=			key.replace(UpperCAmelCase_					,    '''_'''.join(pat.split('''.'''		)		)		)
      return key
def 							__lowerCamelCase		(					UpperCAmelCase_		:						Optional[Any]					,    UpperCAmelCase_		:						Any					,    UpperCAmelCase_		:						Tuple		):




      """simple docstring"""





      a		:Dict									=			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		:str									=			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		:List[Any]									=			pt_tuple_key[:-1] + ('''embedding''',)
            return renamed_pt_tuple_key, pt_tensor

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

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

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

      # old PyTorch layer norm bias
      a		:Dict									=			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 							__lowerCamelCase		(					UpperCAmelCase_		:						List[str]					,    UpperCAmelCase_		:						str					,    UpperCAmelCase_		:						List[Any]=42		):




      """simple docstring"""





      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		:List[str]									=			flax_model.init_weights(PRNGKey(UpperCAmelCase_		)		)

      a		:Dict									=			flatten_dict(UpperCAmelCase_		)
      a		:List[str]									=			{}

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

            # Correctly rename weight parameters
            a				,							a		:Dict									=			rename_key_and_reshape_tensor(UpperCAmelCase_					,    UpperCAmelCase_					,    UpperCAmelCase_		)

            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		:List[Any]									=			jnp.asarray(UpperCAmelCase_		)

      return unflatten_dict(UpperCAmelCase_		)









def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      while b:
            a				,							a		:int									=			b, a % b
      return a
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						int		):




      """simple docstring"""





      return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase_					,    a % b		)
def 							__lowerCamelCase		(					):




      """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()





