"""simple docstring"""






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


_A   					=    logging.get_logger(__name__)

_A   					=    {
    'CarlCochet/trajectory-transformer-halfcheetah-medium-v2': (
        'https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json'
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					a   	=		"trajectory_transformer"
					a   	=		["past_key_values"]
					a   	=		{
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}



					def __init__(						self			:				List[str]   , _snake_case			:				Tuple=100   , _snake_case			:				List[str]=5   , _snake_case			:				Optional[int]=1   , _snake_case			:				Optional[int]=1   , _snake_case			:				Optional[int]=249   , _snake_case			:				Optional[Any]=6   , _snake_case			:				Optional[int]=17   , _snake_case			:				str=25   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				Union[str, Any]=4   , _snake_case			:				Optional[Any]=128   , _snake_case			:				Any=0.1   , _snake_case			:				Any=0.1   , _snake_case			:				Optional[Any]=0.1   , _snake_case			:				Tuple=0.0006   , _snake_case			:				List[Any]=512   , _snake_case			:				Union[str, Any]=0.02   , _snake_case			:				str=1e-12   , _snake_case			:				Tuple=1   , _snake_case			:				Tuple=True   , _snake_case			:				str=1   , _snake_case			:				Any=50256   , _snake_case			:				str=50256   , **_snake_case			:				Dict   , )							->       Tuple:
									SCREAMING_SNAKE_CASE__		  =				vocab_size
									SCREAMING_SNAKE_CASE__		  =				action_weight
									SCREAMING_SNAKE_CASE__		  =				reward_weight
									SCREAMING_SNAKE_CASE__		  =				value_weight
									SCREAMING_SNAKE_CASE__		  =				max_position_embeddings
									SCREAMING_SNAKE_CASE__		  =				block_size
									SCREAMING_SNAKE_CASE__		  =				action_dim
									SCREAMING_SNAKE_CASE__		  =				observation_dim
									SCREAMING_SNAKE_CASE__		  =				transition_dim
									SCREAMING_SNAKE_CASE__		  =				learning_rate
									SCREAMING_SNAKE_CASE__		  =				n_layer
									SCREAMING_SNAKE_CASE__		  =				n_head
									SCREAMING_SNAKE_CASE__		  =				n_embd
									SCREAMING_SNAKE_CASE__		  =				embd_pdrop
									SCREAMING_SNAKE_CASE__		  =				attn_pdrop
									SCREAMING_SNAKE_CASE__		  =				resid_pdrop
									SCREAMING_SNAKE_CASE__		  =				initializer_range
									SCREAMING_SNAKE_CASE__		  =				layer_norm_eps
									SCREAMING_SNAKE_CASE__		  =				kaiming_initializer_range
									SCREAMING_SNAKE_CASE__		  =				use_cache
									super().__init__(pad_token_id=_snake_case   , bos_token_id=_snake_case   , eos_token_id=_snake_case   , **_snake_case		)





"""simple docstring"""






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				List[str]   , _snake_case			:				Callable   , _snake_case			:				Optional[Features] = None   , _snake_case			:				str = None   , _snake_case			:				bool = False   , _snake_case			:				bool = False   , _snake_case			:				Optional[dict] = None   , _snake_case			:				Optional[int] = None   , **_snake_case			:				Dict   , )							->       Any:
									super().__init__(
									    features=_snake_case   , cache_dir=_snake_case   , keep_in_memory=_snake_case   , streaming=_snake_case   , num_proc=_snake_case   , **_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				Generator(
									    cache_dir=_snake_case   , features=_snake_case   , generator=_snake_case   , gen_kwargs=_snake_case   , **_snake_case   , )



					def 			lowerCAmelCase_					(						self			:				List[str]		)							->       Optional[Any]:
									# Build iterable dataset
									if self.streaming:
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_streaming_dataset(split="train"		)
									# Build regular (map-style) dataset
									else:
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None
													SCREAMING_SNAKE_CASE__		  =				None

													self.builder.download_and_prepare(
													    download_config=_snake_case   , download_mode=_snake_case   , verification_mode=_snake_case   , base_path=_snake_case   , num_proc=self.num_proc   , )
													SCREAMING_SNAKE_CASE__		  =				self.builder.as_dataset(
													    split="train"   , verification_mode=_snake_case   , in_memory=self.keep_in_memory		)
									return dataset




