





import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand


def _A       (   __magic_name__	):
   return EnvironmentCommand()







class  lowerCAmelCase							(       lowercase_ ):







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :ArgumentParser ):






     '''simple docstring'''





     lowercase__			       =  parser.add_parser("env" )
     download_parser.set_defaults(func=_lowercase )







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






     '''simple docstring'''





     lowercase__			       =  huggingface_hub.__version__

     lowercase__			       =  "not installed"
     lowercase__			       =  "NA"
     if is_torch_available():
        import torch

        lowercase__			       =  torch.__version__
        lowercase__			       =  torch.cuda.is_available()

     lowercase__			       =  "not installed"
     if is_transformers_available():
        import transformers

        lowercase__			       =  transformers.__version__

     lowercase__			       =  "not installed"
     if is_accelerate_available():
        import accelerate

        lowercase__			       =  accelerate.__version__

     lowercase__			       =  "not installed"
     if is_xformers_available():
        import xformers

        lowercase__			       =  xformers.__version__

     lowercase__			       =  {
         "`diffusers` version": version,
         "Platform": platform.platform(),
         "Python version": platform.python_version(),
         "PyTorch version (GPU?)": f'''{pt_version} ({pt_cuda_available})''',
         "Huggingface_hub version": hub_version,
         "Transformers version": transformers_version,
         "Accelerate version": accelerate_version,
         "xFormers version": xformers_version,
         "Using GPU in script?": "<fill in>",
         "Using distributed or parallel set-up in script?": "<fill in>",
     }

     print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n" )
     print(self.format_dict(_lowercase ) )

     return info







  @staticmethod
  def   UpperCAmelCase					(      _lowercase  :List[str] ):






     '''simple docstring'''





     return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"













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






