'''simple docstring'''

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


UpperCamelCase__     :	Any				=			logging.get_logger(__name__)

UpperCamelCase__     :	Tuple				=			{
    '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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


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




      def __init__(				self					,lowerCamelCase_=100					,lowerCamelCase_=5					,lowerCamelCase_=1					,lowerCamelCase_=1					,lowerCamelCase_=249					,lowerCamelCase_=6					,lowerCamelCase_=17					,lowerCamelCase_=25					,lowerCamelCase_=4					,lowerCamelCase_=4					,lowerCamelCase_=128					,lowerCamelCase_=0.1					,lowerCamelCase_=0.1					,lowerCamelCase_=0.1					,lowerCamelCase_=0.0006					,lowerCamelCase_=512					,lowerCamelCase_=0.02					,lowerCamelCase_=1e-12					,lowerCamelCase_=1					,lowerCamelCase_=True					,lowerCamelCase_=1					,lowerCamelCase_=50256					,lowerCamelCase_=50256					,**lowerCamelCase_					,)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  vocab_size
         UpperCAmelCase__							:				List[Any]   				=  action_weight
         UpperCAmelCase__							:				Union[str, Any]   				=  reward_weight
         UpperCAmelCase__							:				Union[str, Any]   				=  value_weight
         UpperCAmelCase__							:				Tuple   				=  max_position_embeddings
         UpperCAmelCase__							:				Tuple   				=  block_size
         UpperCAmelCase__							:				Optional[Any]   				=  action_dim
         UpperCAmelCase__							:				Tuple   				=  observation_dim
         UpperCAmelCase__							:				Tuple   				=  transition_dim
         UpperCAmelCase__							:				Optional[Any]   				=  learning_rate
         UpperCAmelCase__							:				List[Any]   				=  n_layer
         UpperCAmelCase__							:				str   				=  n_head
         UpperCAmelCase__							:				Optional[Any]   				=  n_embd
         UpperCAmelCase__							:				List[str]   				=  embd_pdrop
         UpperCAmelCase__							:				Tuple   				=  attn_pdrop
         UpperCAmelCase__							:				Optional[Any]   				=  resid_pdrop
         UpperCAmelCase__							:				Tuple   				=  initializer_range
         UpperCAmelCase__							:				List[str]   				=  layer_norm_eps
         UpperCAmelCase__							:				int   				=  kaiming_initializer_range
         UpperCAmelCase__							:				Dict   				=  use_cache
         super().__init__(pad_token_id=lowerCamelCase_					,bos_token_id=lowerCamelCase_					,eos_token_id=lowerCamelCase_					,**lowerCamelCase_							)





'''simple docstring'''

import warnings

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


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      Tuple      							=   ['''image_processor''', '''tokenizer''']
      UpperCAmelCase_      :      str      							=   '''CLIPImageProcessor'''
      UpperCAmelCase_      :      Dict      							=   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			Any:



         '''simple docstring'''



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

         UpperCAmelCase__							:				Optional[Any]   				=  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__(lowerCamelCase_					,lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=None					,**lowerCamelCase_							)			->			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:
            UpperCAmelCase__							:				Tuple   				=  self.tokenizer(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

         if images is not None:
            UpperCAmelCase__							:				Optional[int]   				=  self.image_processor(lowerCamelCase_					,return_tensors=lowerCamelCase_					,**lowerCamelCase_							)

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




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



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




      def 		lowerCAmelCase__					(				self					,*lowerCamelCase_					,**lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



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




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.tokenizer.model_input_names
         UpperCAmelCase__							:				Optional[Any]   				=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[Any]:



         '''simple docstring'''



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




      @property
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



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




