





"""simple docstring"""


def SCREAMING_SNAKE_CASE_   (       snake_case   :					str = "mytext.txt"						,  snake_case   :					bool = False		)->		None:
       import socket

       _lowerCamelCase    	=					12_312  # Reserve a port for your service.
       _lowerCamelCase    	=					socket.socket()  # Create a socket object
       _lowerCamelCase    	=					socket.gethostname()  # Get local machine name
       sock.bind((host, port)		)  # Bind to the port
       sock.listen(5		)  # Now wait for client connection.

       print('Server listening....'		)

       while True:
              _lowerCamelCase	,	_lowerCamelCase    	=					sock.accept()  # Establish connection with client.
              print(f'Got connection from {addr}'		)
              _lowerCamelCase    	=					conn.recv(1_024		)
              print(f'Server received: {data = }'		)

              with open(snake_case						,  'rb'		) as in_file:
                     _lowerCamelCase    	=					in_file.read(1_024		)
                     while data:
                            conn.send(snake_case		)
                            print(f'Sent {data!r}'		)
                            _lowerCamelCase    	=					in_file.read(1_024		)

              print('Done sending'		)
              conn.close()
              if testing:  # Allow the test to complete
                     break

       sock.shutdown(1		)
       sock.close()


if __name__ == "__main__":
      send_file()













"""simple docstring"""


import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm





def SCREAMING_SNAKE_CASE_   (       snake_case   :					int						,  snake_case   :					bool = False		)->		Any:
       if device_specific:
              seed += AcceleratorState().process_index
       random.seed(snake_case		)
       np.random.seed(snake_case		)
       torch.manual_seed(snake_case		)
       if is_xpu_available():
              torch.xpu.manual_seed_all(snake_case		)
       elif is_npu_available():
              torch.npu.manual_seed_all(snake_case		)
       else:
              torch.cuda.manual_seed_all(snake_case		)
       # ^^ safe to call this function even if cuda is not available
       if is_tpu_available():
              xm.set_rng_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					Optional[RNGType] = None						,  snake_case   :					Optional[torch.Generator] = None		)->		str:
       # Get the proper rng state
       if rng_type == RNGType.TORCH:
              _lowerCamelCase    	=					torch.get_rng_state()
       elif rng_type == RNGType.CUDA:
              _lowerCamelCase    	=					torch.cuda.get_rng_state()
       elif rng_type == RNGType.XLA:
              assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
              _lowerCamelCase    	=					torch.tensor(xm.get_rng_state()		)
       elif rng_type == RNGType.NPU:
              assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
              _lowerCamelCase    	=					torch.npu.get_rng_state()
       elif rng_type == RNGType.XPU:
              assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
              _lowerCamelCase    	=					torch.xpu.get_rng_state()
       elif rng_type == RNGType.GENERATOR:
              assert generator is not None, "Need a generator to synchronize its seed."
              _lowerCamelCase    	=					generator.get_state()

       # Broadcast the rng state from device 0 to other devices
       _lowerCamelCase    	=					AcceleratorState()
       if state.distributed_type == DistributedType.TPU:
              _lowerCamelCase    	=					rng_state.to(xm.xla_device()		)
              xm.collective_broadcast([rng_state]		)
              xm.mark_step()
              _lowerCamelCase    	=					rng_state.cpu()
       elif (
           state.distributed_type in CUDA_DISTRIBUTED_TYPES
           or state.distributed_type == DistributedType.MULTI_NPU
           or state.distributed_type == DistributedType.MULTI_XPU
       ):
              _lowerCamelCase    	=					rng_state.to(state.device		)
              torch.distributed.broadcast(snake_case						,  0		)
              _lowerCamelCase    	=					rng_state.cpu()
       elif state.distributed_type == DistributedType.MULTI_CPU:
              torch.distributed.broadcast(snake_case						,  0		)

       # Set the broadcast rng state
       if rng_type == RNGType.TORCH:
              torch.set_rng_state(snake_case		)
       elif rng_type == RNGType.CUDA:
              torch.cuda.set_rng_state(snake_case		)
       elif rng_type == RNGType.NPU:
              torch.npu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XPU:
              torch.xpu.set_rng_state(snake_case		)
       elif rng_type == RNGType.XLA:
              xm.set_rng_state(rng_state.item()		)
       elif rng_type == RNGType.GENERATOR:
              generator.set_state(snake_case		)





def SCREAMING_SNAKE_CASE_   (       snake_case   :					List[Union[str, RNGType]]						,  snake_case   :					Optional[torch.Generator] = None		)->		int:
       for rng_type in rng_types:
              synchronize_rng_state(RNGType(snake_case		)						,  generator=snake_case		)






