



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





		def __init__(   self       ,				__UpperCAmelCase=17       ,				__UpperCAmelCase=4       ,				__UpperCAmelCase=128       ,				__UpperCAmelCase=4_096       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=1       ,				__UpperCAmelCase=1_024       ,				__UpperCAmelCase=3       ,				__UpperCAmelCase=1       ,				__UpperCAmelCase=None       ,				__UpperCAmelCase="relu"       ,				__UpperCAmelCase=0.1       ,				__UpperCAmelCase=0.1       ,				__UpperCAmelCase=0.1       ,				__UpperCAmelCase=1E-5       ,				__UpperCAmelCase=0.02       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=True       ,				__UpperCAmelCase=50_256       ,				__UpperCAmelCase=50_256       ,				__UpperCAmelCase=False       ,				__UpperCAmelCase=False       ,				**__UpperCAmelCase       ,				)				->   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()      )



