



"""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


_lowerCAmelCase    : Tuple      =							logging.get_logger(__name__)

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[int]    )  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Optional[Any]        =   r"""\w+[.]\d+"""
     _UpperCAmelCase      :     Union[str, Any]        =   re.findall(_lowerCAmelCase						,  _lowerCAmelCase    )
     for pat in pats:
          _UpperCAmelCase      :     Any        =   key.replace(_lowerCAmelCase						,  "_".join(pat.split("."    )    )    )
     return key

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Tuple    )  ->       Any:





     '''simple docstring'''

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

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

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

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

     # old PyTorch layer norm bias
     _UpperCAmelCase      :     int        =   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						(				SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						Tuple						,  SCREAMING_SNAKE_CASE__     :						Any=42    )  ->       List[Any]:





     '''simple docstring'''

     _UpperCAmelCase      :     Union[str, Any]        =   {k: v.numpy() for k, v in pt_state_dict.items()}

     # Step 2: Since the model is stateless, get random Flax params
     _UpperCAmelCase      :     Union[str, Any]        =   flax_model.init_weights(PRNGKey(_lowerCAmelCase    )    )

     _UpperCAmelCase      :     Optional[Any]        =   flatten_dict(_lowerCAmelCase    )
     _UpperCAmelCase      :     Optional[int]        =   {}

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

          # Correctly rename weight parameters
          _UpperCAmelCase      :     int        =   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
          _UpperCAmelCase      :     int        =   jnp.asarray(_lowerCAmelCase    )

     return unflatten_dict(_lowerCAmelCase    )





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   [], []
     while len(SCREAMING_SNAKE_CASE__    ) > 1:
          _UpperCAmelCase    ,	_UpperCAmelCase      :     int        =   min(SCREAMING_SNAKE_CASE__    ), max(SCREAMING_SNAKE_CASE__    )
          start.append(SCREAMING_SNAKE_CASE__    )
          end.append(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
          collection.remove(SCREAMING_SNAKE_CASE__    )
     end.reverse()
     return start + collection + end


if __name__ == "__main__":
 _lowerCAmelCase    : int							=							input("Enter numbers separated by a comma:\n").strip()
 _lowerCAmelCase    : List[str]							=							[int(item) for item in user_input.split(",")]
 print(*merge_sort(unsorted), sep=",")
