






'''simple docstring'''



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    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	Dict    )  ->			List[Any]:

   """simple docstring"""
   return EnvironmentCommand()



class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):




      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase      )							->Any:



         '''simple docstring'''
         __a				     =    parser.add_parser('env'      )
         download_parser.set_defaults(func=lowerCamelCase      )




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    huggingface_hub.__version__

         __a				     =    'not installed'
         __a				     =    'NA'
         if is_torch_available():
            import torch

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

         __a				     =    'not installed'
         if is_transformers_available():
            import transformers

            __a				     =    transformers.__version__

         __a				     =    'not installed'
         if is_accelerate_available():
            import accelerate

            __a				     =    accelerate.__version__

         __a				     =    'not installed'
         if is_xformers_available():
            import xformers

            __a				     =    xformers.__version__

         __a				     =    {
             '`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(lowerCamelCase      )      )

         return info






      @staticmethod
      def  __UpperCamelCase							(  lowerCamelCase      )							->Optional[int]:



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







'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase      :       Dict      			=      logging.get_logger(__name__)

__UpperCamelCase      :       Any      			=      {
    """google/vivit-b-16x2-kinetics400""": (
        """https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"""
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}
class 							__SCREAMING_SNAKE_CASE       (							_lowerCAmelCase     ):
      __a  					="vivit"




      def __init__(  self ,   lowerCamelCase=224 ,   lowerCamelCase=32 ,   lowerCamelCase=[2, 16, 16] ,   lowerCamelCase=3 ,   lowerCamelCase=768 ,   lowerCamelCase=12 ,   lowerCamelCase=12 ,   lowerCamelCase=3072 ,   lowerCamelCase="gelu_fast" ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.0 ,   lowerCamelCase=0.02 ,   lowerCamelCase=1e-06 ,   lowerCamelCase=True ,   **lowerCamelCase ,   )							->Tuple:



         '''simple docstring'''
         __a				     =    hidden_size
         __a				     =    num_hidden_layers
         __a				     =    num_attention_heads
         __a				     =    intermediate_size
         __a				     =    hidden_act
         __a				     =    hidden_dropout_prob
         __a				     =    attention_probs_dropout_prob
         __a				     =    initializer_range
         __a				     =    layer_norm_eps

         __a				     =    image_size
         __a				     =    num_frames
         __a				     =    tubelet_size
         __a				     =    num_channels
         __a				     =    qkv_bias

         super().__init__(**lowerCamelCase      )