




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
    from deepspeed import DeepSpeedEngine

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


def    A						(__A   :    int       )   ->      List[Any]:







     """simple docstring"""


     if is_torch_version('''<'''   ,				'''2.0.0'''       ) or not hasattr(__A   ,				'''_dynamo'''       ):
          return False
     return isinstance(__A   ,				torch._dynamo.eval_frame.OptimizedModule       )


def    A						(__A   :    List[Any]   ,				__A   :    bool = True       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     UpperCAmelCase_          =							is_compiled_module(__A       )
     if is_compiled:
          UpperCAmelCase_          =							model
          UpperCAmelCase_          =							model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(__A   ,				__A       ):
          UpperCAmelCase_          =							model.module

     if not keep_fpaa_wrapper:
          UpperCAmelCase_          =							getattr(__A   ,				'''forward'''       )
          UpperCAmelCase_          =							model.__dict__.pop('''_original_forward'''   ,				__A       )
          if original_forward is not None:
               while hasattr(__A   ,				'''__wrapped__'''       ):
                    UpperCAmelCase_          =							forward.__wrapped__
                    if forward == original_forward:
                         break
               UpperCAmelCase_          =							forward
          if getattr(__A   ,				'''_converted_to_transformer_engine'''   ,				__A       ):
               convert_model(__A   ,				to_transformer_engine=__A       )

     if is_compiled:
          UpperCAmelCase_          =							model
          UpperCAmelCase_          =							compiled_model

     return model


def    A						()   ->      List[str]:







     """simple docstring"""


     PartialState().wait_for_everyone()


def    A						(__A   :    Dict   ,				__A   :    Union[str, Any]       )   ->      List[str]:







     """simple docstring"""


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(__A   ,				__A       )
     elif PartialState().local_process_index == 0:
          torch.save(__A   ,				__A       )


@contextmanager
def    A						(**__A   :    Union[str, Any]       )   ->      Dict:







     """simple docstring"""


     for key, value in kwargs.items():
          UpperCAmelCase_          =							str(__A       )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]


def    A						(__A   :    List[Any]       )   ->      Any:







     """simple docstring"""


     if not hasattr(__A   ,				'''__qualname__'''       ) and not hasattr(__A   ,				'''__name__'''       ):
          UpperCAmelCase_          =							getattr(__A   ,				'''__class__'''   ,				__A       )
     if hasattr(__A   ,				'''__qualname__'''       ):
          return obj.__qualname__
     if hasattr(__A   ,				'''__name__'''       ):
          return obj.__name__
     return str(__A       )


def    A						(__A   :    Tuple   ,				__A   :    str       )   ->      List[str]:







     """simple docstring"""


     for key, value in source.items():
          if isinstance(__A   ,				__A       ):
               UpperCAmelCase_          =							destination.setdefault(__A   ,				{}       )
               merge_dicts(__A   ,				__A       )
          else:
               UpperCAmelCase_          =							value

     return destination


def    A						(__A   :    int = None       )   ->      bool:







     """simple docstring"""


     if port is None:
          UpperCAmelCase_          =							29500
     with socket.socket(socket.AF_INET   ,				socket.SOCK_STREAM       ) as s:
          return s.connect_ex(('''localhost''', port)       ) == 0







'''simple docstring'''

import doctest
from collections import deque

import numpy as np



class 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self )  ->  None:
        lowercase__				:    str  =					[2, 1, 2, -1]
        lowercase__				:    str  =					[1, 2, 3, 4]


       def _lowerCAmelCase(						self )  ->  list[float]:
        lowercase__				:    Optional[Any]  =					len(self.first_signal )
        lowercase__				:    Union[str, Any]  =					len(self.second_signal )

        lowercase__				:    int  =					max(__lowerCAmelCase	,   __lowerCAmelCase )

        # create a zero matrix of max_length x max_length
        lowercase__				:    List[str]  =					[[0] * max_length for i in range(__lowerCAmelCase )]

        # fills the smaller signal with zeros to make both signals of same length
        if length_first_signal < length_second_signal:
         self.first_signal += [0] * (max_length - length_first_signal)
        elif length_first_signal > length_second_signal:
         self.second_signal += [0] * (max_length - length_second_signal)
        for i in range(__lowerCAmelCase ):
         lowercase__				:    int  =					deque(self.second_signal )
         rotated_signal.rotate(__lowerCAmelCase )
         for j, item in enumerate(__lowerCAmelCase ):
          matrix[i][j] += item

        # multiply the matrix with the first signal
        lowercase__				:    Optional[int]  =					np.matmul(np.transpose(__lowerCAmelCase )	,   np.transpose(self.first_signal ) )

        # rounding-off to two decimal places
        return [round(__lowerCAmelCase	,   2 ) for i in final_signal]


if __name__ == "__main__":
  doctest.testmod()

