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    __lowercase     (						lowerCamelCase					:     Tuple	):
 return EnvironmentCommand()






class 		_lowercase						(					snake_case_       ):

 @staticmethod
 def 						SCREAMING_SNAKE_CASE__   ( snake_case	:							ArgumentParser			)   ->						Optional[Any]:

  """simple docstring"""





  UpperCamelCase_					:						Optional[Any] =					parser.add_parser('env'			)
  download_parser.set_defaults(func=snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						int =					huggingface_hub.__version__

  UpperCamelCase_					:						Optional[int] =					'not installed'
  UpperCamelCase_					:						Dict =					'NA'
  if is_torch_available():
   import torch

   UpperCamelCase_					:						Union[str, Any] =					torch.__version__
   UpperCamelCase_					:						Any =					torch.cuda.is_available()

  UpperCamelCase_					:						List[str] =					'not installed'
  if is_transformers_available():
   import transformers

   UpperCamelCase_					:						Union[str, Any] =					transformers.__version__

  UpperCamelCase_					:						str =					'not installed'
  if is_accelerate_available():
   import accelerate

   UpperCamelCase_					:						str =					accelerate.__version__

  UpperCamelCase_					:						Tuple =					'not installed'
  if is_xformers_available():
   import xformers

   UpperCamelCase_					:						Union[str, Any] =					xformers.__version__

  UpperCamelCase_					:						Dict =					{
      '`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(snake_case			)			)

  return info


 @staticmethod
 def 						SCREAMING_SNAKE_CASE__   ( snake_case	:							str			)   ->						Any:

  """simple docstring"""





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




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



