


"""simple docstring"""


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


A	:  List[Any]            =     logging.get_logger(__name__)

A	:  Optional[int]            =     {
    "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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 ="""trajectory_transformer"""
							__UpperCAmelCase		:       int							 =["""past_key_values"""]
							__UpperCAmelCase		:       Any							 ={
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}

							def __init__(	self       ,  __a=1_00       ,  __a=5       ,  __a=1       ,  __a=1       ,  __a=2_49       ,  __a=6       ,  __a=17       ,  __a=25       ,  __a=4       ,  __a=4       ,  __a=1_28       ,  __a=0.1       ,  __a=0.1       ,  __a=0.1       ,  __a=0.0_0_0_6       ,  __a=5_12       ,  __a=0.0_2       ,  __a=1e-1_2       ,  __a=1       ,  __a=True       ,  __a=1       ,  __a=5_02_56       ,  __a=5_02_56       ,  **__a       ,  ):
										__lowerCAmelCase							       =     vocab_size
										__lowerCAmelCase							       =     action_weight
										__lowerCAmelCase							       =     reward_weight
										__lowerCAmelCase							       =     value_weight
										__lowerCAmelCase							       =     max_position_embeddings
										__lowerCAmelCase							       =     block_size
										__lowerCAmelCase							       =     action_dim
										__lowerCAmelCase							       =     observation_dim
										__lowerCAmelCase							       =     transition_dim
										__lowerCAmelCase							       =     learning_rate
										__lowerCAmelCase							       =     n_layer
										__lowerCAmelCase							       =     n_head
										__lowerCAmelCase							       =     n_embd
										__lowerCAmelCase							       =     embd_pdrop
										__lowerCAmelCase							       =     attn_pdrop
										__lowerCAmelCase							       =     resid_pdrop
										__lowerCAmelCase							       =     initializer_range
										__lowerCAmelCase							       =     layer_norm_eps
										__lowerCAmelCase							       =     kaiming_initializer_range
										__lowerCAmelCase							       =     use_cache
										super().__init__(pad_token_id=__a       ,  bos_token_id=__a       ,  eos_token_id=__a       ,  **__a      )








"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       List[str]							 ="""Speech2TextFeatureExtractor"""
							__UpperCAmelCase		:       Union[str, Any]							 ="""Speech2TextTokenizer"""

							def __init__(	self       ,  __a       ,  __a      ):
										super().__init__(__a       ,  __a      )
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False

							def __call__(	self       ,  *__a       ,  **__a      ):
										# For backward compatibility
										if self._in_target_context_manager:
													return self.current_processor(*__a       ,  **__a      )

										if "raw_speech" in kwargs:
													warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."      )
													__lowerCAmelCase							       =     kwargs.pop("raw_speech"      )
										else:
													__lowerCAmelCase							       =     kwargs.pop("audio"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("sampling_rate"       ,  __a      )
										__lowerCAmelCase							       =     kwargs.pop("text"       ,  __a      )
										if len(__a      ) > 0:
													__lowerCAmelCase							       =     args[0]
													__lowerCAmelCase							       =     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:
													__lowerCAmelCase							       =     self.feature_extractor(__a       ,  *__a       ,  sampling_rate=__a       ,  **__a      )
										if text is not None:
													__lowerCAmelCase							       =     self.tokenizer(__a       ,  **__a      )

										if text is None:
													return inputs
										elif audio is None:
													return encodings
										else:
													__lowerCAmelCase							       =     encodings["input_ids"]
													return inputs

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.batch_decode(*__a       ,  **__a      )

							def      snake_case							(	self       ,  *__a       ,  **__a      ):
										return self.tokenizer.decode(*__a       ,  **__a      )



							@contextmanager
							def      snake_case							(	self      ):
										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."      )
										__lowerCAmelCase							       =     True
										__lowerCAmelCase							       =     self.tokenizer
										yield
										__lowerCAmelCase							       =     self.feature_extractor
										__lowerCAmelCase							       =     False




