






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__      :	Tuple       	=      logging.get_logger(__name__)






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






def     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase):
   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     _snake_case    (__lowercase    ,      __lowercase    ,      __lowercase=42):
   # Step 1: Convert pytorch tensor to numpy
   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)













import os
from math import logaa






def     _snake_case    (__lowercase = "base_exp.txt"):
   UpperCamelCase_									=     0
   UpperCamelCase_									=     0
   for i, line in enumerate(open(os.path.join(os.path.dirname(__lowercase)    ,      __lowercase))):
      UpperCamelCase_		,      UpperCamelCase_									=     list(map(__lowercase    ,      line.split(',')))
      if x * logaa(__lowercase) > largest:
         UpperCamelCase_									=     x * logaa(__lowercase)
         UpperCamelCase_									=     i + 1
   return result


if __name__ == "__main__":
 print(solution())





