

'''simple docstring'''



import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					List[Any]     =					'''Speech2TextFeatureExtractor'''
      SCREAMING_SNAKE_CASE__			:					Any     =					'''Speech2TextTokenizer'''




      def __init__(       self			:		int  ,       snake_case			:		List[Any]  ,       snake_case			:		int ):



          """simple docstring"""





          super().__init__(_lowercase  ,       _lowercase )
          _snake_case			:			Tuple				       =		self.feature_extractor
          _snake_case			:			int				       =		False




      def __call__(       self			:		List[Any]  ,       *snake_case			:		List[Any]  ,       **snake_case			:		List[Any] ):



          """simple docstring"""





          if self._in_target_context_manager:
              return self.current_processor(*_lowercase  ,       **_lowercase )

          if "raw_speech" in kwargs:
              warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.' )
              _snake_case			:			int				       =		kwargs.pop('raw_speech' )
          else:
              _snake_case			:			Union[str, Any]				       =		kwargs.pop('audio'  ,       _lowercase )
          _snake_case			:			int				       =		kwargs.pop('sampling_rate'  ,       _lowercase )
          _snake_case			:			Dict				       =		kwargs.pop('text'  ,       _lowercase )
          if len(_lowercase ) > 0:
              _snake_case			:			Tuple				       =		args[0]
              _snake_case			:			Union[str, Any]				       =		args[1:]

          if audio is None and text is None:
              raise ValueError('You need to specify either an `audio` or `text` input to process.' )

          if audio is not None:
              _snake_case			:			str				       =		self.feature_extractor(_lowercase  ,       *_lowercase  ,       sampling_rate=_lowercase  ,       **_lowercase )
          if text is not None:
              _snake_case			:			List[str]				       =		self.tokenizer(_lowercase  ,       **_lowercase )

          if text is None:
              return inputs
          elif audio is None:
              return encodings
          else:
              _snake_case			:			List[str]				       =		encodings['input_ids']
              return inputs




      def 						__UpperCAmelCase      (       self			:		Any  ,       *snake_case			:		Union[str, Any]  ,       **snake_case			:		List[Any] ):



          """simple docstring"""





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




      def 						__UpperCAmelCase      (       self			:		List[Any]  ,       *snake_case			:		Dict  ,       **snake_case			:		List[str] ):



          """simple docstring"""





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




      @contextmanager
      def 						__UpperCAmelCase      (       self			:		int ):



          """simple docstring"""





          warnings.warn(
              '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
              'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
              'your audio inputs, or in a separate call.' )
          _snake_case			:			int				       =		True
          _snake_case			:			Tuple				       =		self.tokenizer
          yield
          _snake_case			:			Optional[Any]				       =		self.feature_extractor
          _snake_case			:			Optional[int]				       =		False





'''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     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE					):






						'''simple docstring'''






						_lowercase						: List[str]										=  '''decision_transformer'''
						_lowercase						: Optional[Any]										=  ['''past_key_values''']
						_lowercase						: str										=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}




						def __init__(   self       ,  _lowercase=17       ,  _lowercase=4       ,  _lowercase=128       ,  _lowercase=4_096       ,  _lowercase=True       ,  _lowercase=1       ,  _lowercase=1_024       ,  _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=50_256       ,  _lowercase=50_256       ,  _lowercase=False       ,  _lowercase=False       ,  **_lowercase       ,  ):

												"""simple docstring"""



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

												_lowerCAmelCase    		=					bos_token_id
												_lowerCAmelCase    		=					eos_token_id

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