





from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__UpperCamelCase	: Dict    		=  {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: Any    		=  ["""GPTNeoXTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase	: str    		=  [
					    """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GPTNeoXForCausalLM""",
					    """GPTNeoXForQuestionAnswering""",
					    """GPTNeoXForSequenceClassification""",
					    """GPTNeoXForTokenClassification""",
					    """GPTNeoXLayer""",
					    """GPTNeoXModel""",
					    """GPTNeoXPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_gpt_neox import (
										    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GPTNeoXForCausalLM,
										    GPTNeoXForQuestionAnswering,
										    GPTNeoXForSequenceClassification,
										    GPTNeoXForTokenClassification,
										    GPTNeoXLayer,
										    GPTNeoXModel,
										    GPTNeoXPreTrainedModel,
										)


else:
					import sys

					__UpperCamelCase	: str    		=  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from __future__ import annotations

from typing import Any





class       lowercase__		(     UpperCamelCase_):
						pass






class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							data
												SCREAMING_SNAKE_CASE							:					Node | None           =							None


						def __iter__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self
												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(UpperCamelCase__		)
																		yield node.data
																		SCREAMING_SNAKE_CASE							:					Dict           =							node.next_node



						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												try:
																		list(self		)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  Node(1)
					__UpperCamelCase	: str    		=  Node(2)
					__UpperCamelCase	: Dict    		=  Node(3)
					__UpperCamelCase	: List[Any]    		=  Node(4)
					print(root_node.has_loop)  # False
					__UpperCamelCase	: int    		=  root_node.next_node
					print(root_node.has_loop)  # True

					__UpperCamelCase	: Union[str, Any]    		=  Node(5)
					__UpperCamelCase	: Union[str, Any]    		=  Node(6)
					__UpperCamelCase	: List[Any]    		=  Node(5)
					__UpperCamelCase	: List[str]    		=  Node(6)
					print(root_node.has_loop)  # False

					__UpperCamelCase	: List[Any]    		=  Node(1)
					print(root_node.has_loop)  # False





