






'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A:      Optional[Any]  				=				{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  'albert'
	def __init__(							self  ,      _lowercase=3_0000  ,      _lowercase=128  ,      _lowercase=4096  ,      _lowercase=12  ,      _lowercase=1  ,      _lowercase=64  ,      _lowercase=1_6384  ,      _lowercase=1  ,      _lowercase="gelu_new"  ,      _lowercase=0  ,      _lowercase=0  ,      _lowercase=512  ,      _lowercase=2  ,      _lowercase=0.02  ,      _lowercase=1E-1_2  ,      _lowercase=0.1  ,      _lowercase="absolute"  ,      _lowercase=0  ,      _lowercase=2  ,      _lowercase=3  ,      **_lowercase  ,      )						->  Any:
							super().__init__(pad_token_id=_lowercase  ,      bos_token_id=_lowercase  ,      eos_token_id=_lowercase  ,      **_lowercase       )

							lowercase_      :   Dict        =			vocab_size
							lowercase_      :   List[str]        =			embedding_size
							lowercase_      :   Any        =			hidden_size
							lowercase_      :   Dict        =			num_hidden_layers
							lowercase_      :   Tuple        =			num_hidden_groups
							lowercase_      :   Union[str, Any]        =			num_attention_heads
							lowercase_      :   int        =			inner_group_num
							lowercase_      :   Any        =			hidden_act
							lowercase_      :   Optional[Any]        =			intermediate_size
							lowercase_      :   Tuple        =			hidden_dropout_prob
							lowercase_      :   Optional[int]        =			attention_probs_dropout_prob
							lowercase_      :   Dict        =			max_position_embeddings
							lowercase_      :   List[Any]        =			type_vocab_size
							lowercase_      :   Optional[Any]        =			initializer_range
							lowercase_      :   Union[str, Any]        =			layer_norm_eps
							lowercase_      :   List[str]        =			classifier_dropout_prob
							lowercase_      :   Optional[int]        =			position_embedding_type






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	@property
	def        lowerCamelCase__		(							self       )						->  Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
													lowercase_      :   Tuple        =			{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
													lowercase_      :   int        =			{0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							        ('token_type_ids', dynamic_axis),
							    ]       )








'''simple docstring'''

from typing import TYPE_CHECKING

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


A:      int  				=				{
    "configuration_trajectory_transformer": [
        "TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TrajectoryTransformerConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A:      Union[str, Any]  				=				[
						    "TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TrajectoryTransformerModel",
						    "TrajectoryTransformerPreTrainedModel",
						    "load_tf_weights_in_trajectory_transformer",
						]


if TYPE_CHECKING:
						from .configuration_trajectory_transformer import (
						    TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TrajectoryTransformerConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trajectory_transformer import (
												    TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TrajectoryTransformerModel,
												    TrajectoryTransformerPreTrainedModel,
												    load_tf_weights_in_trajectory_transformer,
												)


else:
						import sys

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