





from __future__ import annotations


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Dict							,	__lowerCAmelCase   :    List[Any]=None   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   data
					A__        =   None







			def __repr__(			self   :    Optional[Any]   )	->	Optional[Any]:







					"""simple docstring"""






					A__        =   []
					A__        =   self
					while temp:
							string_rep.append(f'{temp.data}'   )
							A__        =   temp.next
					return "->".join(lowerCAmelCase__   )







def    __lowerCamelCase					(							__a   :int    )		->				Optional[int]:






		"""simple docstring"""

		if not elements_list:
				raise Exception("""The Elements List is empty"""    )

		A__        =   Node(elements_list[0]    )
		for i in range(1    ,     len(_UpperCAmelCase    )    ):
				A__        =   Node(elements_list[i]    )
				A__        =   current.next
		return head







def    __lowerCamelCase					(							__a   :List[str]    )		->				List[str]:






		"""simple docstring"""

		if head_node is not None and isinstance(_UpperCAmelCase    ,     _UpperCAmelCase    ):
				print_reverse(head_node.next    )
				print(head_node.data    )







def    __lowerCamelCase					(							)		->				int:






		"""simple docstring"""

		from doctest import testmod

		testmod()

		A__        =   make_linked_list([1_4, 5_2, 1_4, 1_2, 4_3]    )
		print("""Linked List:"""    )
		print(_UpperCAmelCase    )
		print("""Elements in Reverse:"""    )
		print_reverse(_UpperCAmelCase    )


if __name__ == "__main__":
					main()






from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
						from ..ta.tokenization_ta import TaTokenizer
else:
						from ...utils.dummy_sentencepiece_objects import TaTokenizer

lowerCAmelCase							:  Any   	=						TaTokenizer

if is_tokenizers_available():
						from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
						from ...utils.dummy_tokenizers_objects import TaTokenizerFast

lowerCAmelCase							:  Dict   	=						TaTokenizerFast

lowerCAmelCase							:  Optional[int]   	=						{"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  int   	=						[
						    """MT5EncoderModel""",
						    """MT5ForConditionalGeneration""",
						    """MT5ForQuestionAnswering""",
						    """MT5Model""",
						    """MT5PreTrainedModel""",
						    """MT5Stack""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  Tuple   	=						["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase							:  List[str]   	=						["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
						from .configuration_mta import MTaConfig, MTaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mta import (
												    MTaEncoderModel,
												    MTaForConditionalGeneration,
												    MTaForQuestionAnswering,
												    MTaModel,
												    MTaPreTrainedModel,
												    MTaStack,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
						import sys

						lowerCAmelCase							:  Optional[Any]   	=						_LazyModule(
						    __name__,
						    globals()["""__file__"""],
						    _import_structure,
						    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
						    module_spec=__spec__,
						)






