





"""simple docstring"""




from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)





class  lowerCAmelCase							(       lowercase_ ):







  def __init__(      self  :Dict   ,							_lowercase  :Distribution   ,							_lowercase  :Union[str, Any]=None   ,							_lowercase  :int=None   ,							_lowercase  :Tuple=0 ):






     '''simple docstring'''





     lowercase__			       =  1.0 if scale is None else scale
     lowercase__			       =  0.0 if loc is None else loc

     super().__init__(__UpperCAmelCase   ,							[AffineTransform(loc=self.loc   ,							scale=self.scale   ,							event_dim=__UpperCAmelCase )] )







  @property
  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     return self.base_dist.mean * self.scale + self.loc







  @property
  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     return self.base_dist.variance * self.scale**2







  @property
  def   UpperCAmelCase					(      self  :Dict ):






     '''simple docstring'''





     return self.variance.sqrt()





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :List[Any]   ,							_lowercase  :int   ,							_lowercase  :Dict[str, int]   ,							_lowercase  :Callable[..., Tuple[torch.Tensor]]   ,							**_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     super().__init__(**__UpperCAmelCase )
     lowercase__			       =  args_dim
     lowercase__			       =  nn.ModuleList([nn.Linear(__UpperCAmelCase   ,							__UpperCAmelCase ) for dim in args_dim.values()] )
     lowercase__			       =  domain_map







  def   UpperCAmelCase					(      self  :List[str]   ,							_lowercase  :torch.Tensor ):






     '''simple docstring'''





     lowercase__			       =  [proj(__UpperCAmelCase ) for proj in self.proj]

     return self.domain_map(*__UpperCAmelCase )





class  lowerCAmelCase							(       nn.Module ):







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :Tuple ):






     '''simple docstring'''





     super().__init__()
     lowercase__			       =  function







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :int   ,							*_lowercase  :List[str] ):






     '''simple docstring'''





     return self.function(__UpperCAmelCase   ,							*__UpperCAmelCase )





class  lowerCAmelCase							:
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42
  __lowerCamelCase		=      42







  def __init__(      self  :Optional[Any]   ,							_lowercase  :int = 1 ):






     '''simple docstring'''





     lowercase__			       =  dim
     lowercase__			       =  {k: dim * self.args_dim[k] for k in self.args_dim}







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :List[Any] ):






     '''simple docstring'''





     if self.dim == 1:
        return self.distribution_class(*__UpperCAmelCase )
     else:
        return Independent(self.distribution_class(*__UpperCAmelCase )   ,							1 )







  def   UpperCAmelCase					(      self  :List[Any]   ,							_lowercase  :Tuple   ,							_lowercase  :Optional[torch.Tensor] = None   ,							_lowercase  :Optional[torch.Tensor] = None   ,							):






     '''simple docstring'''





     lowercase__			       =  self._base_distribution(__UpperCAmelCase )
     if loc is None and scale is None:
        return distr
     else:
        return AffineTransformed(__UpperCAmelCase   ,							loc=__UpperCAmelCase   ,							scale=__UpperCAmelCase   ,							event_dim=self.event_dim )







  @property
  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     return () if self.dim == 1 else (self.dim,)







  @property
  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     return len(self.event_shape )







  @property
  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     return 0.0







  def   UpperCAmelCase					(      self  :Tuple   ,							_lowercase  :int ):






     '''simple docstring'''





     return ParameterProjection(
         in_features=__UpperCAmelCase   ,							args_dim=self.args_dim   ,							domain_map=LambdaLayer(self.domain_map )   ,							)







  def   UpperCAmelCase					(      self  :List[str]   ,							*_lowercase  :torch.Tensor ):






     '''simple docstring'''





     raise NotImplementedError()







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :torch.Tensor ):






     '''simple docstring'''





     return (x + torch.sqrt(torch.square(__UpperCAmelCase ) + 4.0 )) / 2.0





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      {'df': 1, 'loc': 1, 'scale': 1}
  __lowerCamelCase		=      StudentT







  @classmethod
  def   UpperCAmelCase					(      cls  :Union[str, Any]   ,							_lowercase  :torch.Tensor   ,							_lowercase  :torch.Tensor   ,							_lowercase  :torch.Tensor ):






     '''simple docstring'''





     lowercase__			       =  cls.squareplus(__UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
     lowercase__			       =  2.0 + cls.squareplus(__UpperCAmelCase )
     return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      {'loc': 1, 'scale': 1}
  __lowerCamelCase		=      Normal







  @classmethod
  def   UpperCAmelCase					(      cls  :Dict   ,							_lowercase  :torch.Tensor   ,							_lowercase  :torch.Tensor ):






     '''simple docstring'''





     lowercase__			       =  cls.squareplus(__UpperCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
     return loc.squeeze(-1 ), scale.squeeze(-1 )





class  lowerCAmelCase							(       lowercase_ ):
  __lowerCamelCase		=      {'total_count': 1, 'logits': 1}
  __lowerCamelCase		=      NegativeBinomial







  @classmethod
  def   UpperCAmelCase					(      cls  :Optional[int]   ,							_lowercase  :torch.Tensor   ,							_lowercase  :torch.Tensor ):






     '''simple docstring'''





     lowercase__			       =  cls.squareplus(__UpperCAmelCase )
     return total_count.squeeze(-1 ), logits.squeeze(-1 )







  def   UpperCAmelCase					(      self  :Optional[Any]   ,							_lowercase  :Union[str, Any] ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  distr_args
     if self.dim == 1:
        return self.distribution_class(total_count=__UpperCAmelCase   ,							logits=__UpperCAmelCase )
     else:
        return Independent(self.distribution_class(total_count=__UpperCAmelCase   ,							logits=__UpperCAmelCase )   ,							1 )







  def   UpperCAmelCase					(      self  :Optional[int]   ,							_lowercase  :Tuple   ,							_lowercase  :Optional[torch.Tensor] = None   ,							_lowercase  :Optional[torch.Tensor] = None ):






     '''simple docstring'''





     lowercase__       ,							lowercase__			       =  distr_args

     if scale is not None:
        # See scaling property of Gamma.
        logits += scale.log()

     return self._base_distribution((total_count, logits) )













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


def _A       (   __magic_name__  , __magic_name__ = True	):
   lowercase__			       =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

   lowercase__			       =  is_compiled_module(__magic_name__	)
   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  model._orig_mod

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

   while isinstance(__magic_name__  , __magic_name__	):
      lowercase__			       =  model.module

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

   if is_compiled:
      lowercase__			       =  model
      lowercase__			       =  compiled_model

   return model


def _A       (   ):
   PartialState().wait_for_everyone()


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


@contextmanager
def _A       (   **__magic_name__	):
   for key, value in kwargs.items():
      lowercase__			       =  str(__magic_name__	)

   yield

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


def _A       (   __magic_name__	):
   if not hasattr(__magic_name__  , "__qualname__"	) and not hasattr(__magic_name__  , "__name__"	):
      lowercase__			       =  getattr(__magic_name__  , "__class__"  , __magic_name__	)
   if hasattr(__magic_name__  , "__qualname__"	):
      return obj.__qualname__
   if hasattr(__magic_name__  , "__name__"	):
      return obj.__name__
   return str(__magic_name__	)


def _A       (   __magic_name__  , __magic_name__	):
   for key, value in source.items():
      if isinstance(__magic_name__  , __magic_name__	):
         lowercase__			       =  destination.setdefault(__magic_name__  , {}	)
         merge_dicts(__magic_name__  , __magic_name__	)
      else:
         lowercase__			       =  value

   return destination







def _A       (   __magic_name__ = None	):
   if port is None:
      lowercase__			       =  2_9500
   with socket.socket(socket.AF_INET  , socket.SOCK_STREAM	) as s:
      return s.connect_ex(("localhost", port)	) == 0






