


from typing import TYPE_CHECKING

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


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

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A							:					Optional[int]     						=     ['RemBertTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A							:					str     						=     ['RemBertTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A							:					Union[str, Any]     						=     [
	    '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:
	A							:					Optional[Any]     						=     [
	    '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

	A							:					Tuple     						=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









from __future__ import annotations

from collections.abc import Iterator






class     UpperCamelCase:


					def __init__(						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	None:




											'''simple docstring'''

											__snake_case				=		value
											__snake_case				=		None
											__snake_case				=		None






class     UpperCamelCase:


					def __init__(						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Node						)			->	None:




											'''simple docstring'''

											__snake_case				=		tree


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Node | None						)			->	int:




											'''simple docstring'''

											if node is None:
																	return 0
											return node.value + (
											    self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
											)






					def __iter__(						self	:		int						)			->	Iterator[int]:




											'''simple docstring'''

											yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()





