



'''simple docstring'''






# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
     import jax
     import jaxlib

lowerCAmelCase:					Tuple						       =						get_logger()

lowerCAmelCase:					List[Any]						       =						None

class 							a__(					TensorFormatter[Mapping, """jax.Array""", Mapping]	):







    def __init__(      self :  Union[str, Any]						,			__snake_case :  Any=None						,			__snake_case :  Dict=None						,			**__snake_case :  int	):
      super().__init__(features=_A	)
      import jax
      from jaxlib.xla_client import Device

      if isinstance(_A						,			_A	):
        raise ValueError(
            F"""Expected {device} to be a `str` not {type(_A	)}, as `jaxlib.xla_extension.Device` """
            'is not serializable neither with `pickle` nor with `dill`. Instead you can surround '
            'the device with `str()` to get its string identifier that will be internally mapped '
            'to the actual `jaxlib.xla_extension.Device`.'	)
      a						:					Optional[int]        =     device if isinstance(_A						,			_A	) else str(jax.devices()[0]	)
      # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
      # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
        a						:					Optional[Any]        =     self._map_devices_to_str()
      if self.device not in list(DEVICE_MAPPING.keys()	):
        logger.warning(
            F"""Device with string identifier {self.device} not listed among the available """
            F"""devices: {list(DEVICE_MAPPING.keys()	)}, so falling back to the default """
            F"""device: {str(jax.devices()[0]	)}."""	)
        a						:					Tuple        =     str(jax.devices()[0]	)
      a						:					Optional[int]        =     jnp_array_kwargs







    @staticmethod
    def 			lowercase_       (      ):
      import jax

      return {str(_A	): device for device in jax.devices()}







    def 			lowercase_       (      self :  Dict						,			__snake_case :  Tuple	):
      import jax
      import jax.numpy as jnp

      if isinstance(_A						,			_A	) and column:
        if all(
            isinstance(_A						,			jax.Array	) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column	):
          return jnp.stack(_A						,			axis=0	)
      return column







    def 			lowercase_       (      self :  Union[str, Any]						,			__snake_case :  Union[str, Any]	):
      import jax
      import jax.numpy as jnp

      if isinstance(_A						,			(str, bytes, type(_A	))	):
        return value
      elif isinstance(_A						,			(np.character, np.ndarray)	) and np.issubdtype(value.dtype						,			np.character	):
        return value.tolist()

      a						:					List[str]        =     {}

      if isinstance(_A						,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype						,			np.integer	):
        # the default int precision depends on the jax config
        # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
        if jax.config.jax_enable_xaa:
          a						:					Union[str, Any]        =     {'dtype': jnp.intaa}
        else:
          a						:					Dict        =     {'dtype': jnp.intaa}
      elif isinstance(_A						,			(np.number, np.ndarray)	) and np.issubdtype(value.dtype						,			np.floating	):
        a						:					Any        =     {'dtype': jnp.floataa}
      elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(_A						,			PIL.Image.Image	):
          a						:					str        =     np.asarray(_A	)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
      global DEVICE_MAPPING
      if DEVICE_MAPPING is None:
        a						:					Tuple        =     self._map_devices_to_str()

      with jax.default_device(DEVICE_MAPPING[self.device]	):
        # calling jnp.array on a np.ndarray does copy the data
        # see https://github.com/google/jax/issues/4486
        return jnp.array(_A						,			**{**default_dtype, **self.jnp_array_kwargs}	)







    def 			lowercase_       (      self :  int						,			__snake_case :  Optional[Any]	):
      import jax

      # support for torch, tf, jax etc.
      if config.TORCH_AVAILABLE and "torch" in sys.modules:
        import torch

        if isinstance(_A						,			torch.Tensor	):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]	)
      if hasattr(_A						,			'__array__'	) and not isinstance(_A						,			jax.Array	):
        a						:					Optional[Any]        =     data_struct.__array__()
      # support for nested types like struct of list of struct
      if isinstance(_A						,			np.ndarray	):
        if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
          return self._consolidate([self.recursive_tensorize(_A	) for substruct in data_struct]	)
      elif isinstance(_A						,			(list, tuple)	):
        return self._consolidate([self.recursive_tensorize(_A	) for substruct in data_struct]	)
      return self._tensorize(_A	)







    def 			lowercase_       (      self :  Dict						,			__snake_case :  List[Any]	):
      return map_nested(self._recursive_tensorize						,			_A						,			map_list=_A	)







    def 			lowercase_       (      self :  Optional[Any]						,			__snake_case :  List[Any]	):
      a						:					Tuple        =     self.numpy_arrow_extractor().extract_row(_A	)
      a						:					int        =     self.python_features_decoder.decode_row(_A	)
      return self.recursive_tensorize(_A	)







    def 			lowercase_       (      self :  Union[str, Any]						,			__snake_case :  Optional[int]	):
      a						:					Optional[int]        =     self.numpy_arrow_extractor().extract_column(_A	)
      a						:					Dict        =     self.python_features_decoder.decode_column(_A						,			pa_table.column_names[0]	)
      a						:					Optional[Any]        =     self.recursive_tensorize(_A	)
      a						:					Optional[int]        =     self._consolidate(_A	)
      return column







    def 			lowercase_       (      self :  List[str]						,			__snake_case :  Union[str, Any]	):
      a						:					str        =     self.numpy_arrow_extractor().extract_batch(_A	)
      a						:					Optional[Any]        =     self.python_features_decoder.decode_batch(_A	)
      a						:					Optional[Any]        =     self.recursive_tensorize(_A	)
      for column_name in batch:
        a						:					int        =     self._consolidate(batch[column_name]	)
      return batch





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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """decision_transformer"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    Dict					=    {
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(   self		,			_A=1_7		,			_A=4		,			_A=1_2_8		,			_A=4_0_9_6		,			_A=True		,			_A=1		,			_A=1_0_2_4		,			_A=3		,			_A=1		,			_A=None		,			_A="relu"		,			_A=0.1		,			_A=0.1		,			_A=0.1		,			_A=1E-5		,			_A=0.02		,			_A=True		,			_A=True		,			_A=5_0_2_5_6		,			_A=5_0_2_5_6		,			_A=False		,			_A=False		,			**_A		,			):




											"""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=_A		,			eos_token_id=_A		,			**_A			)




