


'''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


__snake_case											=					logging.get_logger(__name__)


def       a				(      __a       )  ->     Union[str, Any]:



     '''simple docstring'''






     UpperCamelCase__       :Tuple							 =			R'''\w+[.]\d+'''
     UpperCamelCase__       :Dict							 =			re.findall(__a			, __a       )
     for pat in pats:
          UpperCamelCase__       :Dict							 =			key.replace(__a			, '''_'''.join(pat.split('''.'''       )       )       )
     return key


def       a				(      __a			, __a			, __a       )  ->     Dict:



     '''simple docstring'''






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

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

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

     # old PyTorch layer norm bias
     UpperCamelCase__       :List[Any]							 =			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			, __a			, __a=42       )  ->     str:



     '''simple docstring'''






     UpperCamelCase__       :List[str]							 =			{k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCamelCase__       :Optional[int]							 =			flatten_dict(__a       )
     UpperCamelCase__       :List[Any]							 =			{}

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

          # Correctly rename weight parameters
          UpperCamelCase__       :int							 =			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
          UpperCamelCase__       :Any							 =			jnp.asarray(__a       )

     return unflatten_dict(__a       )



'''simple docstring'''
import socket


def       a				(      )  ->     Dict:



					'''simple docstring'''






					UpperCamelCase__       :int							 =			socket.socket(socket.AF_INET			, socket.SOCK_STREAM       )
					UpperCamelCase__       :List[Any]							 =			socket.gethostname()
					UpperCamelCase__       :List[str]							 =			12312

					sock.connect((host, port)       )
					sock.send(B'''Hello server!'''       )

					with open('''Received_file'''			, '''wb'''       ) as out_file:
										print('''File opened'''       )
										print('''Receiving data...'''       )
										while True:
															UpperCamelCase__       :str							 =			sock.recv(1024       )
															if not data:
																				break
															out_file.write(__a       )

					print('''Successfully received the file'''       )
					sock.close()
					print('''Connection closed'''       )


if __name__ == "__main__":
		main()