



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


__lowerCamelCase     :							Union[str, Any]  							=				logging.get_logger(__name__)

__lowerCamelCase     :							Tuple  							=				{
    "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  __magic_name__   (							A__						):
		lowercase      :       int          ='''trajectory_transformer'''
		lowercase      :       Optional[int]          =['''past_key_values''']
		lowercase      :       str          ={
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}






		def __init__(						self      :     List[str]				,		UpperCamelCase__      :     Optional[Any]=1_00				,		UpperCamelCase__      :     Any=5				,		UpperCamelCase__      :     Any=1				,		UpperCamelCase__      :     int=1				,		UpperCamelCase__      :     Optional[int]=2_49				,		UpperCamelCase__      :     Optional[int]=6				,		UpperCamelCase__      :     List[Any]=17				,		UpperCamelCase__      :     Any=25				,		UpperCamelCase__      :     Tuple=4				,		UpperCamelCase__      :     Optional[int]=4				,		UpperCamelCase__      :     Tuple=1_28				,		UpperCamelCase__      :     List[str]=0.1				,		UpperCamelCase__      :     List[str]=0.1				,		UpperCamelCase__      :     List[Any]=0.1				,		UpperCamelCase__      :     Any=0.00_06				,		UpperCamelCase__      :     int=5_12				,		UpperCamelCase__      :     Tuple=0.02				,		UpperCamelCase__      :     Dict=1e-1_2				,		UpperCamelCase__      :     int=1				,		UpperCamelCase__      :     Tuple=True				,		UpperCamelCase__      :     Dict=1				,		UpperCamelCase__      :     str=5_02_56				,		UpperCamelCase__      :     Union[str, Any]=5_02_56				,		**UpperCamelCase__      :     List[Any]				,		)				->					Any:
								'''simple docstring'''







								UpperCAmelCase         =     vocab_size
								UpperCAmelCase         =     action_weight
								UpperCAmelCase         =     reward_weight
								UpperCAmelCase         =     value_weight
								UpperCAmelCase         =     max_position_embeddings
								UpperCAmelCase         =     block_size
								UpperCAmelCase         =     action_dim
								UpperCAmelCase         =     observation_dim
								UpperCAmelCase         =     transition_dim
								UpperCAmelCase         =     learning_rate
								UpperCAmelCase         =     n_layer
								UpperCAmelCase         =     n_head
								UpperCAmelCase         =     n_embd
								UpperCAmelCase         =     embd_pdrop
								UpperCAmelCase         =     attn_pdrop
								UpperCAmelCase         =     resid_pdrop
								UpperCAmelCase         =     initializer_range
								UpperCAmelCase         =     layer_norm_eps
								UpperCAmelCase         =     kaiming_initializer_range
								UpperCAmelCase         =     use_cache
								super().__init__(pad_token_id=UpperCamelCase__				,		bos_token_id=UpperCamelCase__				,		eos_token_id=UpperCamelCase__				,		**UpperCamelCase__      )








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__lowerCamelCase     :							Any  							=				{
    "configuration_mega": ["MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP", "MegaConfig", "MegaOnnxConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase     :							Optional[int]  							=				[
		    "MEGA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "MegaForCausalLM",
		    "MegaForMaskedLM",
		    "MegaForMultipleChoice",
		    "MegaForQuestionAnswering",
		    "MegaForSequenceClassification",
		    "MegaForTokenClassification",
		    "MegaModel",
		    "MegaPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		__lowerCamelCase     :							int  							=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



