





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    :       str		       =    logging.get_logger(__name__)

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

def     a_       (					lowerCAmelCase_ :     Dict,      lowerCAmelCase_ :     str,      lowerCAmelCase_ :     List[str]      ):
   __lowerCAmelCase				      =      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)
   ):
      __lowerCAmelCase				      =      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:
      __lowerCAmelCase				      =      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:
      __lowerCAmelCase				      =      pt_tuple_key[:-1] + ('embedding',)
      return renamed_pt_tuple_key, pt_tensor

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

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

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

   # old PyTorch layer norm bias
   __lowerCAmelCase				      =      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_ :     Dict,      lowerCAmelCase_ :     Union[str, Any],      lowerCAmelCase_ :     List[Any]=42      ):
   # Step 1: Convert pytorch tensor to numpy
   __lowerCAmelCase				      =      {k: v.numpy() for k, v in pt_state_dict.items()}

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

   __lowerCAmelCase				      =      flatten_dict(lowerCAmelCase_      )
   __lowerCAmelCase				      =      {}

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

      # Correctly rename weight parameters
      __lowerCAmelCase      ,      __lowerCAmelCase				      =      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
      __lowerCAmelCase				      =      jnp.asarray(lowerCAmelCase_      )

   return unflatten_dict(lowerCAmelCase_      )









"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		str:






     '''simple docstring'''



     if not isinstance(snake_case			,					snake_case				):
          raise ValueError("iterations must be defined as integers"				)
     if not isinstance(snake_case			,					snake_case				) or not number >= 1:
          raise ValueError(
              "starting number must be\n                         and integer and be more than 0"				)
     if not iterations >= 1:
          raise ValueError("Iterations must be done more than 0 times to play FizzBuzz"				)

     UpperCAmelCase__						:    str							=							""
     while number <= iterations:
          if number % 3 == 0:
               out += "Fizz"
          if number % 5 == 0:
               out += "Buzz"
          if 0 not in (number % 3, number % 5):
               out += str(snake_case				)

          # print(out)
          number += 1
          out += " "
     return out


if __name__ == "__main__":
 import doctest

 doctest.testmod()


