"""simple docstring"""
from ...processing_utils import ProcessorMixin




class    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""SpeechT5FeatureExtractor"""
      a__        =		"""SpeechT5Tokenizer"""



      def __init__(						self    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        super().__init__(lowercase    ,			lowercase)



      def __call__(						self    ,			*lowercase    ,			**lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     str										=	kwargs.pop('audio'    ,			lowercase)
        a__:     Dict										=	kwargs.pop('text'    ,			lowercase)
        a__:     Tuple										=	kwargs.pop('text_target'    ,			lowercase)
        a__:     Union[str, Any]										=	kwargs.pop('audio_target'    ,			lowercase)
        a__:     List[str]										=	kwargs.pop('sampling_rate'    ,			lowercase)

        if audio is not None and text is not None:
          raise ValueError(
              'Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?')
        if audio_target is not None and text_target is not None:
          raise ValueError(
              'Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?')
        if audio is None and audio_target is None and text is None and text_target is None:
          raise ValueError(
              'You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.')

        if audio is not None:
          a__:     int										=	self.feature_extractor(lowercase    ,			*lowercase    ,			sampling_rate=lowercase    ,			**lowercase)
        elif text is not None:
          a__:     str										=	self.tokenizer(lowercase    ,			**lowercase)
        else:
          a__:     int										=	None

        if audio_target is not None:
          a__:     Tuple										=	self.feature_extractor(audio_target=lowercase    ,			*lowercase    ,			sampling_rate=lowercase    ,			**lowercase)
          a__:     Any										=	targets['input_values']
        elif text_target is not None:
          a__:     str										=	self.tokenizer(lowercase    ,			**lowercase)
          a__:     Tuple										=	targets['input_ids']
        else:
          a__:     Tuple										=	None

        if inputs is None:
          return targets

        if targets is not None:
          a__:     Dict										=	labels

          a__:     Optional[int]										=	targets.get('attention_mask')
          if decoder_attention_mask is not None:
            a__:     List[str]										=	decoder_attention_mask

        return inputs



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			int:





        '''simple docstring'''





        a__:     int										=	kwargs.pop('input_values'    ,			lowercase)
        a__:     Optional[int]										=	kwargs.pop('input_ids'    ,			lowercase)
        a__:     int										=	kwargs.pop('labels'    ,			lowercase)

        if input_values is not None and input_ids is not None:
          raise ValueError('Cannot process both `input_values` and `input_ids` inputs.')
        if input_values is None and input_ids is None and labels is None:
          raise ValueError(
              'You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.')

        if input_values is not None:
          a__:     str										=	self.feature_extractor.pad(lowercase    ,			*lowercase    ,			**lowercase)
        elif input_ids is not None:
          a__:     Optional[int]										=	self.tokenizer.pad(lowercase    ,			**lowercase)
        else:
          a__:     str										=	None

        if labels is not None:
          if "input_ids" in labels or (isinstance(lowercase    ,			lowercase) and "input_ids" in labels[0]):
            a__:     Optional[int]										=	self.tokenizer.pad(lowercase    ,			**lowercase)
            a__:     int										=	targets['input_ids']
          else:
            a__:     Dict										=	self.feature_extractor.feature_size
            a__:     str										=	self.feature_extractor.num_mel_bins
            a__:     int										=	self.feature_extractor.pad(lowercase    ,			*lowercase    ,			**lowercase)
            a__:     Optional[Any]										=	feature_size_hack
            a__:     Any										=	targets['input_values']
        else:
          a__:     Union[str, Any]										=	None

        if inputs is None:
          return targets

        if targets is not None:
          a__:     Any										=	labels

          a__:     Optional[int]										=	targets.get('attention_mask')
          if decoder_attention_mask is not None:
            a__:     Any										=	decoder_attention_mask

        return inputs



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self    ,			*lowercase    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





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



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					       =	logging.get_logger(__name__)

lowercase__					       =	{
    '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    __snake_case       (     __lowerCAmelCase				):
      a__        =		"""decision_transformer"""
      a__        =		["""past_key_values"""]
      a__        =		{
          """max_position_embeddings""": """n_positions""",
          """num_attention_heads""": """n_head""",
          """num_hidden_layers""": """n_layer""",
      }



      def __init__(						self    ,			lowercase=17    ,			lowercase=4    ,			lowercase=1_28    ,			lowercase=40_96    ,			lowercase=True    ,			lowercase=1    ,			lowercase=10_24    ,			lowercase=3    ,			lowercase=1    ,			lowercase=None    ,			lowercase="relu"    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=0.1    ,			lowercase=1e-5    ,			lowercase=0.02    ,			lowercase=True    ,			lowercase=True    ,			lowercase=5_02_56    ,			lowercase=5_02_56    ,			lowercase=False    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        a__:     List[str]										=	state_dim
        a__:     int										=	act_dim
        a__:     List[Any]										=	hidden_size
        a__:     List[str]										=	max_ep_len
        a__:     List[Any]										=	action_tanh
        a__:     Optional[Any]										=	vocab_size
        a__:     Tuple										=	n_positions
        a__:     Dict										=	n_layer
        a__:     Optional[int]										=	n_head
        a__:     Optional[int]										=	n_inner
        a__:     Any										=	activation_function
        a__:     Union[str, Any]										=	resid_pdrop
        a__:     Any										=	embd_pdrop
        a__:     Any										=	attn_pdrop
        a__:     List[Any]										=	layer_norm_epsilon
        a__:     Optional[Any]										=	initializer_range
        a__:     Any										=	scale_attn_weights
        a__:     Dict										=	use_cache
        a__:     Optional[int]										=	scale_attn_by_inverse_layer_idx
        a__:     List[str]										=	reorder_and_upcast_attn

        a__:     Any										=	bos_token_id
        a__:     int										=	eos_token_id

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


