


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


lowerCamelCase__							:						Union[str, Any]							     =   logging.get_logger(__name__)

lowerCamelCase__							:						str							     =   {
    """CarlCochet/trajectory-transformer-halfcheetah-medium-v2""": (
        """https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"""
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							Optional[int]	   =       'trajectory_transformer'
     __lowercase				:							List[Any]	   =       ['past_key_values']
     __lowercase				:							Dict	   =       {
         'hidden_size': 'n_embd',
         'num_attention_heads': 'n_head',
         'num_hidden_layers': 'n_layer',
     }

     def __init__(					self:List[Any]     ,		_a:Any=1_00     ,		_a:List[str]=5     ,		_a:Optional[Any]=1     ,		_a:List[Any]=1     ,		_a:Optional[int]=2_49     ,		_a:Dict=6     ,		_a:int=17     ,		_a:Union[str, Any]=25     ,		_a:Optional[Any]=4     ,		_a:Union[str, Any]=4     ,		_a:int=1_28     ,		_a:Optional[Any]=0.1     ,		_a:Tuple=0.1     ,		_a:List[Any]=0.1     ,		_a:List[str]=0.0006     ,		_a:List[str]=5_12     ,		_a:Tuple=0.02     ,		_a:Optional[Any]=1e-12     ,		_a:Tuple=1     ,		_a:Dict=True     ,		_a:str=1     ,		_a:Union[str, Any]=5_02_56     ,		_a:List[str]=5_02_56     ,		**_a:List[str]     ,		):
       snake_case__						= vocab_size
       snake_case__						= action_weight
       snake_case__						= reward_weight
       snake_case__						= value_weight
       snake_case__						= max_position_embeddings
       snake_case__						= block_size
       snake_case__						= action_dim
       snake_case__						= observation_dim
       snake_case__						= transition_dim
       snake_case__						= learning_rate
       snake_case__						= n_layer
       snake_case__						= n_head
       snake_case__						= n_embd
       snake_case__						= embd_pdrop
       snake_case__						= attn_pdrop
       snake_case__						= resid_pdrop
       snake_case__						= initializer_range
       snake_case__						= layer_norm_eps
       snake_case__						= kaiming_initializer_range
       snake_case__						= use_cache
       super().__init__(pad_token_id=_a     ,		bos_token_id=_a     ,		eos_token_id=_a     ,		**_a   )




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =["image_processor", "tokenizer"]
    __UpperCamelCase    ="CLIPImageProcessor"
    __UpperCamelCase    =("CLIPTokenizer", "CLIPTokenizerFast")

    def __init__(      self			:   int				,     snake_case__			:   List[Any]=None				,     snake_case__			:   Any=None				,     **snake_case__			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      None
     if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'				,     snake_case__				,     )
      SCREAMING_SNAKE_CASE		    =      kwargs.pop('feature_extractor'     )

     SCREAMING_SNAKE_CASE		    =      image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'     )
     if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'     )

     super().__init__(snake_case__				,     snake_case__     )

    def __call__(      self			:   List[Any]				,     snake_case__			:   Tuple=None				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   List[str]=None				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     if text is None and images is None:
      raise ValueError('You have to specify either text or images. Both cannot be none.'     )

     if text is not None:
      SCREAMING_SNAKE_CASE		    =      self.tokenizer(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if images is not None:
      SCREAMING_SNAKE_CASE		    =      self.image_processor(snake_case__				,     return_tensors=snake_case__				,     **snake_case__     )

     if text is not None and images is not None:
      SCREAMING_SNAKE_CASE		    =      image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case__     )				,     tensor_type=snake_case__     )

    def UpperCamelCase      (      self			:   Union[str, Any]				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Optional[int]     ):

     """simple docstring"""






     return self.tokenizer.batch_decode(*snake_case__				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Dict				,     *snake_case__			:   Optional[Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     return self.tokenizer.decode(*snake_case__				,     **snake_case__     )

    @property
    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer.model_input_names
     SCREAMING_SNAKE_CASE		    =      self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )

    @property
    def UpperCamelCase      (      self			:   int     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'				,     snake_case__				,     )
     return self.image_processor_class




    @property
    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     warnings.warn(
         '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'				,     snake_case__				,     )
     return self.image_processor

