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


__lowerCamelCase					:	Dict       =  logging.get_logger(__name__)

__lowerCamelCase					:	Union[str, Any]       =  {
    '''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__						(					A__					):
					A	       =							'decision_transformer'
					A	       =							['past_key_values']
					A	       =							{
					    'max_position_embeddings': 'n_positions',
					    'num_attention_heads': 'n_head',
					    'num_hidden_layers': 'n_layer',
					}

					def __init__(    self      :       int,_A      :       Optional[int]=17,_A      :       List[str]=4,_A      :       Any=128,_A      :       Optional[Any]=4096,_A      :       Tuple=True,_A      :       Any=1,_A      :       str=1024,_A      :       int=3,_A      :       Optional[Any]=1,_A      :       int=None,_A      :       List[str]="relu",_A      :       List[str]=0.1,_A      :       Any=0.1,_A      :       Optional[Any]=0.1,_A      :       Union[str, Any]=1E-5,_A      :       Tuple=0.02,_A      :       int=True,_A      :       List[str]=True,_A      :       List[Any]=5_0256,_A      :       Any=5_0256,_A      :       Optional[Any]=False,_A      :       str=False,**_A      :       str,):







							"""simple docstring"""






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

							SCREAMING_SNAKE_CASE_  :		Any				      =    bos_token_id
							SCREAMING_SNAKE_CASE_  :		Tuple				      =    eos_token_id

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

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       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Union[str, Any]	):
		"""simple docstring"""







		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       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    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       _snake_case	(   lowerCAmelCase							:   Tuple   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    TextDatasetReader(lowerCAmelCase   , features=lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




@pytest.mark.parametrize("split"   , [None, NamedSplit("train"	), "train", "test"]	)
def       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   List[str]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    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       _snake_case	(   lowerCAmelCase							:   int   , lowerCAmelCase							:   int   , lowerCAmelCase							:   Optional[int]	):
		"""simple docstring"""







		if issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    text_path
		elif issubclass(lowerCAmelCase   , lowerCAmelCase	):
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    [text_path]
		SCREAMING_SNAKE_CASE_  :		int				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    TextDatasetReader(lowerCAmelCase   , cache_dir=lowerCAmelCase	).read()
		_check_text_dataset(lowerCAmelCase   , lowerCAmelCase	)




def       _snake_case	(   lowerCAmelCase							:   Dict   , lowerCAmelCase							:   Dict   , lowerCAmelCase							:   List[str]=("train",)	):
		"""simple docstring"""







		assert isinstance(lowerCAmelCase   , lowerCAmelCase	)
		for split in splits:
				SCREAMING_SNAKE_CASE_  :		int				      =    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       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   str   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    {"text": "string"}
		with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
				SCREAMING_SNAKE_CASE_  :		List[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       _snake_case	(   lowerCAmelCase							:   Union[str, Any]   , lowerCAmelCase							:   List[Any]   , lowerCAmelCase							:   Tuple	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    tmp_path / "cache"
		# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		Any				      =    features.copy() if features else default_expected_features
		SCREAMING_SNAKE_CASE_  :		Dict				      =    (
		    Features({feature: Value(lowerCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
		)
		SCREAMING_SNAKE_CASE_  :		str				      =    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       _snake_case	(   lowerCAmelCase							:   Optional[Any]   , lowerCAmelCase							:   Any   , lowerCAmelCase							:   Dict	):
		"""simple docstring"""







		if split:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    {split: text_path}
		else:
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    "train"
				SCREAMING_SNAKE_CASE_  :		Tuple				      =    {"train": text_path, "test": text_path}
		SCREAMING_SNAKE_CASE_  :		Any				      =    tmp_path / "cache"
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    {"text": "string"}
		SCREAMING_SNAKE_CASE_  :		str				      =    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()	)
