




from typing import TYPE_CHECKING

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


__UpperCamelCase					:       Optional[int]    					=      {
    "configuration_roberta": ["ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "RobertaConfig", "RobertaOnnxConfig"],
    "tokenization_roberta": ["RobertaTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Optional[int]    					=      ["RobertaTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Dict    					=      [
		    "ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "RobertaForCausalLM",
		    "RobertaForMaskedLM",
		    "RobertaForMultipleChoice",
		    "RobertaForQuestionAnswering",
		    "RobertaForSequenceClassification",
		    "RobertaForTokenClassification",
		    "RobertaModel",
		    "RobertaPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Dict    					=      [
		    "TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFRobertaForCausalLM",
		    "TFRobertaForMaskedLM",
		    "TFRobertaForMultipleChoice",
		    "TFRobertaForQuestionAnswering",
		    "TFRobertaForSequenceClassification",
		    "TFRobertaForTokenClassification",
		    "TFRobertaMainLayer",
		    "TFRobertaModel",
		    "TFRobertaPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       int    					=      [
		    "FlaxRobertaForCausalLM",
		    "FlaxRobertaForMaskedLM",
		    "FlaxRobertaForMultipleChoice",
		    "FlaxRobertaForQuestionAnswering",
		    "FlaxRobertaForSequenceClassification",
		    "FlaxRobertaForTokenClassification",
		    "FlaxRobertaModel",
		    "FlaxRobertaPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

		__UpperCamelCase					:       Any    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







from __future__ import annotations

from collections.abc import Iterator

class 		__lowerCAmelCase	:
							def __init__(						self						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							value
								a										=							None
								a										=							None







class 		__lowerCAmelCase	:
							def __init__(						self						:str		,      __magic_name__						:Node				):







								'''simple docstring'''







								a										=							tree
							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:Node | None				):







								'''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						:Tuple				):







								'''simple docstring'''







								yield self.depth_first_search(self.tree				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

