


'''simple docstring'''



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


__A		=logging.get_logger(__name__)

__A		={
    "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 						_snake_case   (       _UpperCAmelCase		):
					lowerCAmelCase :Any						=   '''decision_transformer'''
					lowerCAmelCase :Union[str, Any]						=   ['''past_key_values''']
					lowerCAmelCase :Tuple						=   {
					    '''max_position_embeddings''': '''n_positions''',
					    '''num_attention_heads''': '''n_head''',
					    '''num_hidden_layers''': '''n_layer''',
					}




					def __init__( self							,							_lowerCamelCase=17							,							_lowerCamelCase=4							,							_lowerCamelCase=128							,							_lowerCamelCase=4096							,							_lowerCamelCase=True							,							_lowerCamelCase=1							,							_lowerCamelCase=1024							,							_lowerCamelCase=3							,							_lowerCamelCase=1							,							_lowerCamelCase=None							,							_lowerCamelCase="relu"							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=0.1							,							_lowerCamelCase=1e-5							,							_lowerCamelCase=0.02							,							_lowerCamelCase=True							,							_lowerCamelCase=True							,							_lowerCamelCase=5_0256							,							_lowerCamelCase=5_0256							,							_lowerCamelCase=False							,							_lowerCamelCase=False							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   Tuple       				=       state_dim
								UpperCAmelCase__					:   Optional[int]       				=       act_dim
								UpperCAmelCase__					:   str       				=       hidden_size
								UpperCAmelCase__					:   Dict       				=       max_ep_len
								UpperCAmelCase__					:   Any       				=       action_tanh
								UpperCAmelCase__					:   Union[str, Any]       				=       vocab_size
								UpperCAmelCase__					:   List[str]       				=       n_positions
								UpperCAmelCase__					:   Optional[int]       				=       n_layer
								UpperCAmelCase__					:   Tuple       				=       n_head
								UpperCAmelCase__					:   Any       				=       n_inner
								UpperCAmelCase__					:   Optional[Any]       				=       activation_function
								UpperCAmelCase__					:   List[str]       				=       resid_pdrop
								UpperCAmelCase__					:   str       				=       embd_pdrop
								UpperCAmelCase__					:   Dict       				=       attn_pdrop
								UpperCAmelCase__					:   List[Any]       				=       layer_norm_epsilon
								UpperCAmelCase__					:   Dict       				=       initializer_range
								UpperCAmelCase__					:   Tuple       				=       scale_attn_weights
								UpperCAmelCase__					:   Optional[Any]       				=       use_cache
								UpperCAmelCase__					:   Optional[int]       				=       scale_attn_by_inverse_layer_idx
								UpperCAmelCase__					:   Any       				=       reorder_and_upcast_attn

								UpperCAmelCase__					:   Optional[Any]       				=       bos_token_id
								UpperCAmelCase__					:   Optional[Any]       				=       eos_token_id

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A		={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TableTransformerForObjectDetection',
		    'TableTransformerModel',
		    'TableTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_table_transformer import (
		    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TableTransformerConfig,
		    TableTransformerOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_table_transformer import (
				    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TableTransformerForObjectDetection,
				    TableTransformerModel,
				    TableTransformerPreTrainedModel,
				)

else:
		import sys

		__A		=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)