





from collections import OrderedDict
from typing import Mapping

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


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		{
    """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
    """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
    """xlm-roberta-large-finetuned-conll02-dutch""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll02-spanish""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-english""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
    ),
    """xlm-roberta-large-finetuned-conll03-german""": (
        """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
    ),
}
class SCREAMING_SNAKE_CASE_						(					snake_case_					):
						__magic_name__:			Any       =       "xlm-roberta"






						def __init__(      self      :      int		,   _A      :      int=30522		,   _A      :      List[Any]=768		,   _A      :      Optional[Any]=12		,   _A      :      List[str]=12		,   _A      :      int=3072		,   _A      :      int="gelu"		,   _A      :      Optional[int]=0.1		,   _A      :      Tuple=0.1		,   _A      :      List[Any]=512		,   _A      :      Tuple=2		,   _A      :      List[str]=0.0_2		,   _A      :      str=1E-12		,   _A      :      Optional[Any]=1		,   _A      :      List[str]=0		,   _A      :      Dict=2		,   _A      :      Dict="absolute"		,   _A      :      List[str]=True		,   _A      :      Union[str, Any]=None		,   **_A      :      List[str]		,   )							->    Optional[int]:







							"""simple docstring"""


							super().__init__(pad_token_id=_A		,   bos_token_id=_A		,   eos_token_id=_A		,   **_A	)

							snake_case_		:			Dict											=	vocab_size
							snake_case_		:			Optional[int]											=	hidden_size
							snake_case_		:			str											=	num_hidden_layers
							snake_case_		:			Dict											=	num_attention_heads
							snake_case_		:			Dict											=	hidden_act
							snake_case_		:			Optional[Any]											=	intermediate_size
							snake_case_		:			Optional[int]											=	hidden_dropout_prob
							snake_case_		:			str											=	attention_probs_dropout_prob
							snake_case_		:			Any											=	max_position_embeddings
							snake_case_		:			Tuple											=	type_vocab_size
							snake_case_		:			int											=	initializer_range
							snake_case_		:			str											=	layer_norm_eps
							snake_case_		:			Optional[Any]											=	position_embedding_type
							snake_case_		:			str											=	use_cache
							snake_case_		:			List[str]											=	classifier_dropout
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@property
						def UpperCAmelCase_				(      self      :      str	)							->    Mapping[str, Mapping[int, str]]:







							"""simple docstring"""


							if self.task == "multiple-choice":
								snake_case_		:			Optional[int]											=	{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
								snake_case_		:			Union[str, Any]											=	{0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]	)








from __future__ import annotations

from dataclasses import dataclass
@dataclass
class SCREAMING_SNAKE_CASE_						:
						__magic_name__:			float
						__magic_name__:			TreeNode | None       =       None
						__magic_name__:			TreeNode | None       =       None


def 							SCREAMING_SNAKE_CASE__						(    __a   ):

	# Validation
	def is_valid_tree(__a   ) -> bool:
		if node is None:
			return True

		if not isinstance(__a   , __a   ):
			return False

		try:
			float(node.data   )
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left   ) and is_valid_tree(node.right   )

	if not is_valid_tree(__a   ):
		raise ValueError(
		    'Each node should be type of TreeNode and data should be float.'   )

	def is_binary_search_tree_recursive_check(
	    __a   , __a   , __a   ) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left   , __a   , node.data   )
		    and is_binary_search_tree_recursive_check(
		        node.right   , node.data   , __a   )
		)

	return is_binary_search_tree_recursive_check(__a   , -float('inf'   )   , float('inf'   )   )


if __name__ == "__main__":
						import doctest

						doctest.testmod()

