


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase     :						int	=     {
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						Tuple	=     ['RemBertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						Union[str, Any]	=     ['RemBertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						str	=     [
	    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'RemBertForCausalLM',
	    'RemBertForMaskedLM',
	    'RemBertForMultipleChoice',
	    'RemBertForQuestionAnswering',
	    'RemBertForSequenceClassification',
	    'RemBertForTokenClassification',
	    'RemBertLayer',
	    'RemBertModel',
	    'RemBertPreTrainedModel',
	    'load_tf_weights_in_rembert',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase     :						str	=     [
	    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFRemBertForCausalLM',
	    'TFRemBertForMaskedLM',
	    'TFRemBertForMultipleChoice',
	    'TFRemBertForQuestionAnswering',
	    'TFRemBertForSequenceClassification',
	    'TFRemBertForTokenClassification',
	    'TFRemBertLayer',
	    'TFRemBertModel',
	    'TFRemBertPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_rembert import RemBertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_rembert_fast import RemBertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_rembert import (
		    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RemBertForCausalLM,
		    RemBertForMaskedLM,
		    RemBertForMultipleChoice,
		    RemBertForQuestionAnswering,
		    RemBertForSequenceClassification,
		    RemBertForTokenClassification,
		    RemBertLayer,
		    RemBertModel,
		    RemBertPreTrainedModel,
		    load_tf_weights_in_rembert,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_rembert import (
		    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRemBertForCausalLM,
		    TFRemBertForMaskedLM,
		    TFRemBertForMultipleChoice,
		    TFRemBertForQuestionAnswering,
		    TFRemBertForSequenceClassification,
		    TFRemBertForTokenClassification,
		    TFRemBertLayer,
		    TFRemBertModel,
		    TFRemBertPreTrainedModel,
		)


else:
	import sys

	__lowercase     :						Optional[int]	=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''



from __future__ import annotations

from dataclasses import dataclass





@dataclass
class 							__UpperCamelCase    :
	A_           =   42
	A_           =   None
	A_           =   None






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       TreeNode | None       ):

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

										if not isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															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(_SCREAMING_SNAKE_CASE       ):
										raise ValueError(
										    'Each node should be type of TreeNode and data should be float.'       )

					def is_binary_search_tree_recursive_check(
					    _SCREAMING_SNAKE_CASE				:       TreeNode | None						,				_SCREAMING_SNAKE_CASE				:       float						,				_SCREAMING_SNAKE_CASE				:       float       ) -> bool:

										if node is None:
															return True

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

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


if __name__ == "__main__":
	import doctest

	doctest.testmod()



