


from __future__ import annotations

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

lowerCAmelCase__			:      Dict								=(3, 9, -11, 0, 7, 5, 1, -1)
lowerCAmelCase__			:      Optional[int]								=(4, 6, 2, 0, 8, 10, 3, -2)







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						42
							_UpperCAmelCase      =						42







class 	__lowercase    :





							"""simple docstring"""







							def __init__(      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Node | None					=					None
										for i in sorted(lowerCAmelCase__			,						reverse=lowerCAmelCase__       ):
													SCREAMING_SNAKE_CASE_		:     Dict					=					Node(lowerCAmelCase__			,						self.head       )






							def __iter__(      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					self.head
										while node:
													yield node.data
													SCREAMING_SNAKE_CASE_		:     List[str]					=					node.next_node






							def __len__(      self       ):





										"""simple docstring"""




										return sum(1 for _ in self       )






							def __str__(      self       ):





										"""simple docstring"""




										return " -> ".join([str(lowerCAmelCase__       ) for node in self]       )







def 						a__			(				A__,      A__				):
			return SortedLinkedList(list(A__				) + list(A__				)				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				lowerCAmelCase__			:      Dict								=SortedLinkedList
				print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))









'''simple docstring'''







from typing import TYPE_CHECKING

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


lowerCAmelCase     :  List[str]											=    {
    """configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ElectraConfig""", """ElectraOnnxConfig"""],
    """tokenization_electra""": ["""ElectraTokenizer"""],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Optional[Any]											=    ["""ElectraTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  str											=    [
           """ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ElectraForCausalLM""",
           """ElectraForMaskedLM""",
           """ElectraForMultipleChoice""",
           """ElectraForPreTraining""",
           """ElectraForQuestionAnswering""",
           """ElectraForSequenceClassification""",
           """ElectraForTokenClassification""",
           """ElectraModel""",
           """ElectraPreTrainedModel""",
           """load_tf_weights_in_electra""",
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Tuple											=    [
           """TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """TFElectraForMaskedLM""",
           """TFElectraForMultipleChoice""",
           """TFElectraForPreTraining""",
           """TFElectraForQuestionAnswering""",
           """TFElectraForSequenceClassification""",
           """TFElectraForTokenClassification""",
           """TFElectraModel""",
           """TFElectraPreTrainedModel""",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase     :  Union[str, Any]											=    [
           """FlaxElectraForCausalLM""",
           """FlaxElectraForMaskedLM""",
           """FlaxElectraForMultipleChoice""",
           """FlaxElectraForPreTraining""",
           """FlaxElectraForQuestionAnswering""",
           """FlaxElectraForSequenceClassification""",
           """FlaxElectraForTokenClassification""",
           """FlaxElectraModel""",
           """FlaxElectraPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
       from .tokenization_electra import ElectraTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_electra_fast import ElectraTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_electra import (
                  ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ElectraForCausalLM,
                  ElectraForMaskedLM,
                  ElectraForMultipleChoice,
                  ElectraForPreTraining,
                  ElectraForQuestionAnswering,
                  ElectraForSequenceClassification,
                  ElectraForTokenClassification,
                  ElectraModel,
                  ElectraPreTrainedModel,
                  load_tf_weights_in_electra,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_electra import (
                  TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFElectraForMaskedLM,
                  TFElectraForMultipleChoice,
                  TFElectraForPreTraining,
                  TFElectraForQuestionAnswering,
                  TFElectraForSequenceClassification,
                  TFElectraForTokenClassification,
                  TFElectraModel,
                  TFElectraPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_electra import (
                  FlaxElectraForCausalLM,
                  FlaxElectraForMaskedLM,
                  FlaxElectraForMultipleChoice,
                  FlaxElectraForPreTraining,
                  FlaxElectraForQuestionAnswering,
                  FlaxElectraForSequenceClassification,
                  FlaxElectraForTokenClassification,
                  FlaxElectraModel,
                  FlaxElectraPreTrainedModel,
              )

else:
       import sys

       lowerCAmelCase     :  int											=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

