



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


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		bool = True ):
		lowerCAmelCase_			:     List[Any]									= (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

		lowerCAmelCase_			:     Optional[Any]									= is_compiled_module(__UpperCamelCase )
		if is_compiled:
				lowerCAmelCase_			:     Optional[Any]									= model
				lowerCAmelCase_			:     str									= model._orig_mod

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

		while isinstance(__UpperCamelCase     ,__UpperCamelCase ):
				lowerCAmelCase_			:     int									= model.module

		if not keep_fpaa_wrapper:
				lowerCAmelCase_			:     List[Any]									= getattr(__UpperCamelCase     ,'''forward''' )
				lowerCAmelCase_			:     Any									= model.__dict__.pop('''_original_forward'''     ,__UpperCamelCase )
				if original_forward is not None:
						while hasattr(__UpperCamelCase     ,'''__wrapped__''' ):
								lowerCAmelCase_			:     Tuple									= forward.__wrapped__
								if forward == original_forward:
										break
						lowerCAmelCase_			:     str									= forward
				if getattr(__UpperCamelCase     ,'''_converted_to_transformer_engine'''     ,__UpperCamelCase ):
						convert_model(__UpperCamelCase     ,to_transformer_engine=__UpperCamelCase )

		if is_compiled:
				lowerCAmelCase_			:     List[str]									= model
				lowerCAmelCase_			:     str									= compiled_model

		return model


def 			UpperCamelCase(  ):
		PartialState().wait_for_everyone()


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		Dict ):
		if PartialState().distributed_type == DistributedType.TPU:
				xm.save(__UpperCamelCase     ,__UpperCamelCase )
		elif PartialState().local_process_index == 0:
				torch.save(__UpperCamelCase     ,__UpperCamelCase )


@contextmanager
def 			UpperCamelCase(  **__UpperCamelCase							:		Optional[int] ):
		for key, value in kwargs.items():
				lowerCAmelCase_			:     Dict									= str(__UpperCamelCase )

		yield

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


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		if not hasattr(__UpperCamelCase     ,'''__qualname__''' ) and not hasattr(__UpperCamelCase     ,'''__name__''' ):
				lowerCAmelCase_			:     Dict									= getattr(__UpperCamelCase     ,'''__class__'''     ,__UpperCamelCase )
		if hasattr(__UpperCamelCase     ,'''__qualname__''' ):
				return obj.__qualname__
		if hasattr(__UpperCamelCase     ,'''__name__''' ):
				return obj.__name__
		return str(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		int ):
		for key, value in source.items():
				if isinstance(__UpperCamelCase     ,__UpperCamelCase ):
						lowerCAmelCase_			:     Optional[int]									= destination.setdefault(__UpperCamelCase     ,{} )
						merge_dicts(__UpperCamelCase     ,__UpperCamelCase )
				else:
						lowerCAmelCase_			:     Tuple									= value

		return destination





def 			UpperCamelCase(  __UpperCamelCase							:		int = None ):
		if port is None:
				lowerCAmelCase_			:     str									= 29500
		with socket.socket(socket.AF_INET     ,socket.SOCK_STREAM ) as s:
				return s.connect_ex(('''localhost''', port) ) == 0








'''simple docstring'''
class 							A__    :





  def __init__(		self      :	Union[str, Any]							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =size
       _SCREAMING_SNAKE_CASE							    =[0] * size
       _SCREAMING_SNAKE_CASE							    =[0] * size





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return index | (index + 1)





  @staticmethod
  def 						A  (		_a      :	int )	->    int:





       '''simple docstring'''






       return (index & (index + 1)) - 1





  def 						A  (		self      :	Tuple							,					_a      :	int							,					_a      :	int )	->    None:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE							    =value
       while index < self.size:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a ) + 1
            if current_left_border == index:
                 _SCREAMING_SNAKE_CASE							    =value
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					_a							,					_a )
            _SCREAMING_SNAKE_CASE							    =self.get_next(_a )





  def 						A  (		self      :	int							,					_a      :	int							,					_a      :	int )	->    int:





       '''simple docstring'''






       right -= 1  # Because of right is exclusive
       _SCREAMING_SNAKE_CASE							    =0
       while left <= right:
            _SCREAMING_SNAKE_CASE							    =self.get_prev(_a )
            if left <= current_left:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.tree[right] )
                 _SCREAMING_SNAKE_CASE							    =current_left
            else:
                 _SCREAMING_SNAKE_CASE							    =max(_a							,					self.arr[right] )
                 right -= 1
       return result


if __name__ == "__main__":
     import doctest

     doctest.testmod()
