


'''simple docstring'''


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


lowerCamelCase__					=   logging.get_logger(__name__)

lowerCamelCase__					=   {
    '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__	(	UpperCAmelCase__		):
			lowerCAmelCase       :		Optional[int]				      =   "decision_transformer"
			lowerCAmelCase       :		List[str]				      =   ["past_key_values"]
			lowerCAmelCase       :		Union[str, Any]				      =   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__(						self     :   Dict		,     lowerCamelCase__     :   Dict=17		,     lowerCamelCase__     :   Optional[Any]=4		,     lowerCamelCase__     :   Optional[Any]=1_28		,     lowerCamelCase__     :   List[str]=40_96		,     lowerCamelCase__     :   List[Any]=True		,     lowerCamelCase__     :   Dict=1		,     lowerCamelCase__     :   Dict=10_24		,     lowerCamelCase__     :   Dict=3		,     lowerCamelCase__     :   str=1		,     lowerCamelCase__     :   Any=None		,     lowerCamelCase__     :   Any="relu"		,     lowerCamelCase__     :   int=0.1		,     lowerCamelCase__     :   Any=0.1		,     lowerCamelCase__     :   str=0.1		,     lowerCamelCase__     :   Tuple=1E-5		,     lowerCamelCase__     :   str=0.0_2		,     lowerCamelCase__     :   Optional[Any]=True		,     lowerCamelCase__     :   str=True		,     lowerCamelCase__     :   Any=5_02_56		,     lowerCamelCase__     :   List[Any]=5_02_56		,     lowerCamelCase__     :   Optional[int]=False		,     lowerCamelCase__     :   Any=False		,     **lowerCamelCase__     :   int		,     )      ->List[Any]:



					'''simple docstring'''







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

					_UpperCAmelCase						:				Union[str, Any]	    =					bos_token_id
					_UpperCAmelCase						:				Optional[Any]	    =					eos_token_id

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







'''simple docstring'''


import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.num_rows == 4
		assert dataset.num_columns == 1
		assert dataset.column_names == ["text"]
		for feature, expected_dtype in expected_features.items():
				assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Union[str, Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				int	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Any	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[Any]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Union[str, Any]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				Any	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Dict	    =					{"text": "string"}
		_UpperCAmelCase						:				Union[str, Any]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase					,       split=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )
		assert dataset.split == split if split else "train"






@pytest.mark.parametrize("path_type"					,       [str, list]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				Union[str, Any]	    =					text_path
		elif issubclass(__lowerCAmelCase					,       __lowerCAmelCase      ):
				_UpperCAmelCase						:				int	    =					[text_path]
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Union[str, Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				Optional[int]	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_dataset(__lowerCAmelCase					,       __lowerCAmelCase      )






def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase=("train",)      ):
		assert isinstance(__lowerCAmelCase					,       __lowerCAmelCase      )
		for split in splits:
				_UpperCAmelCase						:				List[str]	    =					dataset_dict[split]
				assert dataset.num_rows == 4
				assert dataset.num_columns == 1
				assert dataset.column_names == ["text"]
				for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype






@pytest.mark.parametrize("keep_in_memory"					,       [False, True]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Tuple	    =					{"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				_UpperCAmelCase						:				Any	    =					TextDatasetReader({"train": text_path}					,       cache_dir=__lowerCAmelCase					,       keep_in_memory=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize(
    "features"					,       [
        None,
        {"text": "string"},
        {"text": "int32"},
        {"text": "float32"},
    ]					,       )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		_UpperCAmelCase						:				List[Any]	    =					tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		_UpperCAmelCase						:				List[Any]	    =					{"text": "string"}
		_UpperCAmelCase						:				List[str]	    =					features.copy() if features else default_expected_features
		_UpperCAmelCase						:				Optional[int]	    =					(
		    Features({feature: Value(__lowerCAmelCase      ) for feature, dtype in features.items()}      ) if features is not None else None
		)
		_UpperCAmelCase						:				Tuple	    =					TextDatasetReader({"train": text_path}					,       features=__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase      )






@pytest.mark.parametrize("split"					,       [None, NamedSplit("train"      ), "train", "test"]      )
def        __lowerCAmelCase       (__lowerCAmelCase					,       __lowerCAmelCase					,       __lowerCAmelCase      ):
		if split:
				_UpperCAmelCase						:				int	    =					{split: text_path}
		else:
				_UpperCAmelCase						:				Tuple	    =					"train"
				_UpperCAmelCase						:				List[str]	    =					{"train": text_path, "test": text_path}
		_UpperCAmelCase						:				Optional[Any]	    =					tmp_path / "cache"
		_UpperCAmelCase						:				Optional[int]	    =					{"text": "string"}
		_UpperCAmelCase						:				int	    =					TextDatasetReader(__lowerCAmelCase					,       cache_dir=__lowerCAmelCase      ).read()
		_check_text_datasetdict(__lowerCAmelCase					,       __lowerCAmelCase					,       splits=list(path.keys()      )      )
		assert all(dataset[split].split == split for split in path.keys()      )



