
"""simple docstring"""





from typing import TYPE_CHECKING

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


__magic_name__       =			{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			["FunnelTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			[
							    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "FunnelBaseModel",
							    "FunnelForMaskedLM",
							    "FunnelForMultipleChoice",
							    "FunnelForPreTraining",
							    "FunnelForQuestionAnswering",
							    "FunnelForSequenceClassification",
							    "FunnelForTokenClassification",
							    "FunnelModel",
							    "FunnelPreTrainedModel",
							    "load_tf_weights_in_funnel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__magic_name__       =			[
							    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFFunnelBaseModel",
							    "TFFunnelForMaskedLM",
							    "TFFunnelForMultipleChoice",
							    "TFFunnelForPreTraining",
							    "TFFunnelForQuestionAnswering",
							    "TFFunnelForSequenceClassification",
							    "TFFunnelForTokenClassification",
							    "TFFunnelModel",
							    "TFFunnelPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
							from .tokenization_funnel import FunnelTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_funnel_fast import FunnelTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_funnel import (
														    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FunnelBaseModel,
														    FunnelForMaskedLM,
														    FunnelForMultipleChoice,
														    FunnelForPreTraining,
														    FunnelForQuestionAnswering,
														    FunnelForSequenceClassification,
														    FunnelForTokenClassification,
														    FunnelModel,
														    FunnelPreTrainedModel,
														    load_tf_weights_in_funnel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_funnel import (
														    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFunnelBaseModel,
														    TFFunnelForMaskedLM,
														    TFFunnelForMultipleChoice,
														    TFFunnelForPreTraining,
														    TFFunnelForQuestionAnswering,
														    TFFunnelForSequenceClassification,
														    TFFunnelForTokenClassification,
														    TFFunnelModel,
														    TFFunnelPreTrainedModel,
														)

else:
							import sys

							__magic_name__       =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

snake_case__					:							Union[str, Any]													=     (3, 9, -11, 0, 7, 5, 1, -1)
snake_case__					:							int													=     (4, 6, 2, 0, 8, 10, 3, -2)





@dataclass
class A_					:
   lowerCAmelCase__        = 42
   lowerCAmelCase__        = 42





class A_					:




   def __init__(self	:Dict    ,   _UpperCamelCase	:Iterable[int] )->					None:
         __A          =				None
         for i in sorted(_UpperCamelCase    ,   reverse=_UpperCamelCase ):
               __A          =				Node(_UpperCamelCase    ,   self.head )




   def __iter__(self	:List[str] )->					Iterator[int]:
         __A          =				self.head
         while node:
               yield node.data
               __A          =				node.next_node




   def __len__(self	:Union[str, Any] )->					int:
         return sum(1 for _ in self )






   def __str__(self	:List[Any] )->					str:
         return " -> ".join([str(_UpperCamelCase ) for node in self] )





def _a				(	lowerCamelCase:   SortedLinkedList					,				lowerCamelCase:   SortedLinkedList )	->						SortedLinkedList:







      '''simple docstring'''






      return SortedLinkedList(list(lowerCamelCase ) + list(lowerCamelCase ) )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       snake_case__					:							Any													=     SortedLinkedList
       print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))





