



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


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :Tuple			       ={
    '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        lowerCAmelCase__					(  __A       ):
				A_   :     Any								=     'decision_transformer'
				A_   :     Union[str, Any]								=     ['past_key_values']
				A_   :     Optional[int]								=     {
				    'max_position_embeddings': 'n_positions',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  Tuple						, __UpperCamelCase      :  Optional[Any]=17						, __UpperCamelCase      :  Optional[Any]=4						, __UpperCamelCase      :  Tuple=128						, __UpperCamelCase      :  Optional[int]=4_096						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Dict=1						, __UpperCamelCase      :  Optional[Any]=1_024						, __UpperCamelCase      :  Optional[int]=3						, __UpperCamelCase      :  List[str]=1						, __UpperCamelCase      :  List[str]=None						, __UpperCamelCase      :  Union[str, Any]="relu"						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Any=0.1						, __UpperCamelCase      :  int=0.1						, __UpperCamelCase      :  Tuple=1e-5						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  Union[str, Any]=True						, __UpperCamelCase      :  Union[str, Any]=True						, __UpperCamelCase      :  List[str]=50_256						, __UpperCamelCase      :  List[str]=50_256						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  str=False						, **__UpperCamelCase      :  Union[str, Any]						, )		->     int:
											A      						=    state_dim
											A      						=    act_dim
											A      						=    hidden_size
											A      						=    max_ep_len
											A      						=    action_tanh
											A      						=    vocab_size
											A      						=    n_positions
											A      						=    n_layer
											A      						=    n_head
											A      						=    n_inner
											A      						=    activation_function
											A      						=    resid_pdrop
											A      						=    embd_pdrop
											A      						=    attn_pdrop
											A      						=    layer_norm_epsilon
											A      						=    initializer_range
											A      						=    scale_attn_weights
											A      						=    use_cache
											A      						=    scale_attn_by_inverse_layer_idx
											A      						=    reorder_and_upcast_attn

											A      						=    bos_token_id
											A      						=    eos_token_id

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







'''simple docstring'''



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

import numpy as np
import pyarrow as pa

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


if TYPE_CHECKING:
      import torch
class UpperCAmelCase_       (     TensorFormatter[Mapping, '''torch.Tensor''', Mapping]					):




      """simple docstring"""




      def __init__(			self      :		Dict  ,   UpperCAmelCase      :		str=None  ,   **UpperCAmelCase      :		List[Any]		)	->	int:


       '''simple docstring'''

       super().__init__(features=UpperCAmelCase		)
       lowercase       : List[Any]						=torch_tensor_kwargs
       import torch  # noqa import torch at initialization






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		Dict		)	->	List[str]:


       '''simple docstring'''

       import torch

       if isinstance(UpperCAmelCase  ,   UpperCAmelCase		) and column:
        if all(
            isinstance(UpperCAmelCase  ,   torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
            for x in column		):
         return torch.stack(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		List[str]  ,   UpperCAmelCase      :		Optional[int]		)	->	Optional[Any]:


       '''simple docstring'''

       import torch

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

       lowercase       : Any						={}

       if isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.integer		):
        lowercase       : Tuple						={'''dtype''': torch.intaa}
       elif isinstance(UpperCAmelCase  ,   (np.number, np.ndarray)		) and np.issubdtype(value.dtype  ,   np.floating		):
        lowercase       : int						={'''dtype''': torch.floataa}
       elif config.PIL_AVAILABLE and "PIL" in sys.modules:
        import PIL.Image

        if isinstance(UpperCAmelCase  ,   PIL.Image.Image		):
         lowercase       : List[Any]						=np.asarray(UpperCAmelCase		)
       return torch.tensor(UpperCAmelCase  ,   **{**default_dtype, **self.torch_tensor_kwargs}		)






      def 					A__   (			self      :		List[Any]  ,   UpperCAmelCase      :		Dict		)	->	Tuple:


       '''simple docstring'''

       import torch

       # support for torch, tf, jax etc.
       if hasattr(UpperCAmelCase  ,   '''__array__'''		) and not isinstance(UpperCAmelCase  ,   torch.Tensor		):
        lowercase       : List[Any]						=data_struct.__array__()
       # support for nested types like struct of list of struct
       if isinstance(UpperCAmelCase  ,   np.ndarray		):
        if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
         return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       elif isinstance(UpperCAmelCase  ,   (list, tuple)		):
        return self._consolidate([self.recursive_tensorize(UpperCAmelCase		) for substruct in data_struct]		)
       return self._tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		dict		)	->	Any:


       '''simple docstring'''

       return map_nested(self._recursive_tensorize  ,   UpperCAmelCase  ,   map_list=UpperCAmelCase		)






      def 					A__   (			self      :		Union[str, Any]  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : List[str]						=self.numpy_arrow_extractor().extract_row(UpperCAmelCase		)
       lowercase       : Any						=self.python_features_decoder.decode_row(UpperCAmelCase		)
       return self.recursive_tensorize(UpperCAmelCase		)






      def 					A__   (			self      :		Any  ,   UpperCAmelCase      :		pa.Table		)	->	"torch.Tensor":


       '''simple docstring'''

       lowercase       : int						=self.numpy_arrow_extractor().extract_column(UpperCAmelCase		)
       lowercase       : Tuple						=self.python_features_decoder.decode_column(UpperCAmelCase  ,   pa_table.column_names[0]		)
       lowercase       : Optional[Any]						=self.recursive_tensorize(UpperCAmelCase		)
       lowercase       : Any						=self._consolidate(UpperCAmelCase		)
       return column






      def 					A__   (			self      :		str  ,   UpperCAmelCase      :		pa.Table		)	->	Mapping:


       '''simple docstring'''

       lowercase       : Tuple						=self.numpy_arrow_extractor().extract_batch(UpperCAmelCase		)
       lowercase       : List[str]						=self.python_features_decoder.decode_batch(UpperCAmelCase		)
       lowercase       : Dict						=self.recursive_tensorize(UpperCAmelCase		)
       for column_name in batch:
        lowercase       : str						=self._consolidate(batch[column_name]		)
       return batch



