

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


lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   Tuple     )    ->      Tuple:





      """simple docstring"""



      _lowercase	       =r'\w+[.]\d+'
      _lowercase	       =re.findall(A__      ,   A__     )
      for pat in pats:
            _lowercase	       =key.replace(A__      ,   '_'.join(pat.split('.'     )     )     )
      return key
def      a      (			A__	:   str      ,   A__	:   Any      ,   A__	:   Tuple     )    ->      Union[str, Any]:





      """simple docstring"""



      _lowercase	       =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)
      ):
            _lowercase	       =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:
            _lowercase	       =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:
            _lowercase	       =pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

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

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

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

      # old PyTorch layer norm bias
      _lowercase	       =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      (			A__	:   Union[str, Any]      ,   A__	:   str      ,   A__	:   List[str]=42     )    ->      Union[str, Any]:





      """simple docstring"""



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

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

      _lowercase	       =flatten_dict(A__     )
      _lowercase	       ={}

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

            # Correctly rename weight parameters
            _lowercase       ,       _lowercase	       =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
            _lowercase	       =jnp.asarray(A__     )

      return unflatten_dict(A__     )





import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


