


"""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


UpperCAmelCase    :     Tuple   						=		logging.get_logger(__name__)
def      lowerCamelCase       (     _UpperCamelCase		:      Any							)		->       Union[str, Any]:

							'''simple docstring'''



							__UpperCAmelCase   :						int      							=					R"""\w+[.]\d+"""
							__UpperCAmelCase   :						Any      							=					re.findall(__UpperCamelCase       ,			__UpperCamelCase							)
							for pat in pats:
														__UpperCAmelCase   :						Optional[Any]      							=					key.replace(__UpperCamelCase       ,			"""_""".join(pat.split("""."""							)							)							)
							return key
def      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      int       ,			_UpperCamelCase		:      List[Any]							)		->       str:

							'''simple docstring'''



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

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

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

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

							# old PyTorch layer norm bias
							__UpperCAmelCase   :						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      lowerCamelCase       (     _UpperCamelCase		:      List[Any]       ,			_UpperCamelCase		:      Union[str, Any]       ,			_UpperCamelCase		:      Optional[int]=4_2							)		->       Optional[int]:

							'''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   :						List[Any]      							=					flax_model.init_weights(PRNGKey(__UpperCamelCase							)							)

							__UpperCAmelCase   :						Union[str, Any]      							=					flatten_dict(__UpperCamelCase							)
							__UpperCAmelCase   :						Union[str, Any]      							=					{}

							# Need to change some parameters name to match Flax names
							for pt_key, pt_tensor in pt_state_dict.items():
														__UpperCAmelCase   :						Tuple      							=					rename_key(__UpperCamelCase							)
														__UpperCAmelCase   :						str      							=					tuple(renamed_pt_key.split("""."""							)							)

														# Correctly rename weight parameters
														__UpperCAmelCase							,__UpperCAmelCase   :						Optional[Any]      							=					rename_key_and_reshape_tensor(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase							)

														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(__UpperCamelCase							)

							return unflatten_dict(__UpperCamelCase							)

import socket



def 	__SCREAMING_SNAKE_CASE  (					)							->					Optional[int]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__										=      socket.socket(socket.AF_INET ,     socket.SOCK_STREAM	)
  SCREAMING_SNAKE_CASE__										=      socket.gethostname()
  SCREAMING_SNAKE_CASE__										=      1_23_12

  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:
      SCREAMING_SNAKE_CASE__										=      sock.recv(10_24	)
      if not data:
        break
      out_file.write(__UpperCamelCase	)

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


if __name__ == "__main__":
   main()
