






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


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''decision_transformer'''
      __snake_case       =  ['''past_key_values''']
      __snake_case       =  {
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }







      def __init__(      self      :						Any ,					__UpperCAmelCase      :						List[Any]=17 ,					__UpperCAmelCase      :						int=4 ,					__UpperCAmelCase      :						Optional[int]=128 ,					__UpperCAmelCase      :						Union[str, Any]=4_096 ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						Optional[Any]=1 ,					__UpperCAmelCase      :						Optional[Any]=1_024 ,					__UpperCAmelCase      :						Any=3 ,					__UpperCAmelCase      :						Dict=1 ,					__UpperCAmelCase      :						List[str]=None ,					__UpperCAmelCase      :						List[str]="relu" ,					__UpperCAmelCase      :						Tuple=0.1 ,					__UpperCAmelCase      :						Any=0.1 ,					__UpperCAmelCase      :						Dict=0.1 ,					__UpperCAmelCase      :						Tuple=1e-5 ,					__UpperCAmelCase      :						str=0.02 ,					__UpperCAmelCase      :						Any=True ,					__UpperCAmelCase      :						Union[str, Any]=True ,					__UpperCAmelCase      :						Optional[int]=50_256 ,					__UpperCAmelCase      :						int=50_256 ,					__UpperCAmelCase      :						Optional[Any]=False ,					__UpperCAmelCase      :						Tuple=False ,					**__UpperCAmelCase      :						List[str] ,					)    ->str:




       """simple docstring"""


       a		  =      state_dim
       a		  =      act_dim
       a		  =      hidden_size
       a		  =      max_ep_len
       a		  =      action_tanh
       a		  =      vocab_size
       a		  =      n_positions
       a		  =      n_layer
       a		  =      n_head
       a		  =      n_inner
       a		  =      activation_function
       a		  =      resid_pdrop
       a		  =      embd_pdrop
       a		  =      attn_pdrop
       a		  =      layer_norm_epsilon
       a		  =      initializer_range
       a		  =      scale_attn_weights
       a		  =      use_cache
       a		  =      scale_attn_by_inverse_layer_idx
       a		  =      reorder_and_upcast_attn

       a		  =      bos_token_id
       a		  =      eos_token_id

       super().__init__(bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase  )









import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.Resampling.BILINEAR,
	    '''bilinear''': PIL.Image.Resampling.BILINEAR,
	    '''bicubic''': PIL.Image.Resampling.BICUBIC,
	    '''lanczos''': PIL.Image.Resampling.LANCZOS,
	    '''nearest''': PIL.Image.Resampling.NEAREST,
	}
else:
	lowerCAmelCase_    =				{
	    '''linear''': PIL.Image.LINEAR,
	    '''bilinear''': PIL.Image.BILINEAR,
	    '''bicubic''': PIL.Image.BICUBIC,
	    '''lanczos''': PIL.Image.LANCZOS,
	    '''nearest''': PIL.Image.NEAREST,
	}






def      lowerCamelCase_						(				_UpperCamelCase )		->						Optional[int]:






				"""simple docstring"""


				snake_case_						:    Dict				=  (images / 2 + 0.5).clamp(0 ,				1 )
				snake_case_						:    Dict				=  images.cpu().permute(0 ,				2 ,				3 ,				1 ).float().numpy()
				snake_case_						:    int				=  numpy_to_pil(_UpperCamelCase )
				return images






def      lowerCamelCase_						(				_UpperCamelCase )		->						List[Any]:






				"""simple docstring"""


				if images.ndim == 3:
								snake_case_						:    Optional[Any]				=  images[None, ...]
				snake_case_						:    Any				=  (images * 255).round().astype('''uint8''' )
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								snake_case_						:    str				=  [Image.fromarray(image.squeeze() ,				mode='''L''' ) for image in images]
				else:
								snake_case_						:    List[Any]				=  [Image.fromarray(_UpperCamelCase ) for image in images]

				return pil_images
