




from __future__ import annotations

from collections.abc import Iterator
from typing import Generic, TypeVar

UpperCamelCase__    			=			TypeVar("""T""")
class 			a__    (					Generic[T]      ):






						def __init__(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       data
											__lowerCAmelCase				   =       None






						def __str__(   self			):




											"""simple docstring"""



											return f"""{self.data}"""






class 			a__    (					Generic[T]      ):






						def __init__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None






						def __iter__(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.top
											while node:
																yield node.data
																__lowerCAmelCase				   =       node.next






						def __str__(   self			):




											"""simple docstring"""



											return "->".join([str(_A			) for item in self]			)






						def __len__(   self			):




											"""simple docstring"""



											return len(tuple(iter(self			)			)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return self.top is None






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       Node(_A			)
											if not self.is_empty():
																__lowerCAmelCase				   =       self.top
											__lowerCAmelCase				   =       node






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if self.is_empty():
																raise IndexError("pop from empty stack"			)
											assert isinstance(self.top		,			_A			)
											__lowerCAmelCase				   =       self.top
											__lowerCAmelCase				   =       self.top.next
											return pop_node.data






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											if self.is_empty():
																raise IndexError("peek from empty stack"			)

											assert self.top is not None
											return self.top.data






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None


if __name__ == "__main__":
					from doctest import testmod

					testmod()










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case										=       logging.get_logger(__name__)

_snake_case										=       {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}







class 						lowercase							(   UpperCamelCase__		):
     _a							 =						"xmod"

     def __init__(  self		,     _a=3_0522		,     _a=768		,     _a=12		,     _a=12		,     _a=3072		,     _a="gelu"		,     _a=0.1		,     _a=0.1		,     _a=512		,     _a=2		,     _a=0.02		,     _a=1e-12		,     _a=1		,     _a=0		,     _a=2		,     _a="absolute"		,     _a=True		,     _a=None		,     _a=False		,     _a=2		,     _a=False		,     _a=True		,     _a=True		,     _a=("en_XX",)		,     _a=None		,     **_a		,     )						->							str:
           super().__init__(pad_token_id=_a		,     bos_token_id=_a		,     eos_token_id=_a		,     **_a				)

           _A							:     Tuple			=						vocab_size
           _A							:     Union[str, Any]			=						hidden_size
           _A							:     Dict			=						num_hidden_layers
           _A							:     Dict			=						num_attention_heads
           _A							:     List[Any]			=						hidden_act
           _A							:     Optional[Any]			=						intermediate_size
           _A							:     Any			=						hidden_dropout_prob
           _A							:     str			=						attention_probs_dropout_prob
           _A							:     Dict			=						max_position_embeddings
           _A							:     Any			=						type_vocab_size
           _A							:     List[Any]			=						initializer_range
           _A							:     int			=						layer_norm_eps
           _A							:     int			=						position_embedding_type
           _A							:     Any			=						use_cache
           _A							:     int			=						classifier_dropout
           _A							:     int			=						pre_norm
           _A							:     Optional[Any]			=						adapter_reduction_factor
           _A							:     List[Any]			=						adapter_layer_norm
           _A							:     Optional[int]			=						adapter_reuse_layer_norm
           _A							:     Any			=						ln_before_adapter
           _A							:     Union[str, Any]			=						list(_a				)
           _A							:     List[Any]			=						default_language







class 						lowercase							(   UpperCamelCase__		):

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

