
class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int=None	)	->					List[str]:
					lowercase_					  = data
					lowercase_					  = None




	def __repr__(	self  :      str	)	->					Any:
					lowercase_					  = ""
					lowercase_					  = self
					while temp:
									string_rep += f'''<{temp.data}> ---> '''
									lowercase_					  = temp.next
					string_rep += "<END>"
					return string_rep




def     a						(							snake_case__: List[Any] ):


				'''simple docstring'''




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

				# Set first element as Head
				lowercase_					  = Node(elements_list[0] )
				lowercase_					  = head
				# Loop through elements from position 1
				for data in elements_list[1:]:
								lowercase_					  = Node(_UpperCamelCase )
								lowercase_					  = current.next
				return head


__a        = [1, 3, 5, 3_2, 4_4, 1_2, 4_3]
print(f"List: {list_data}")
print('Creating Linked List from List.')
__a        = make_linked_list(list_data)
print('Linked List:')


print(linked_list)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


__a        = {
    'configuration_speech_to_text_2': ['SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2Text2Config'],
    'processing_speech_to_text_2': ['Speech2Text2Processor'],
    'tokenization_speech_to_text_2': ['Speech2Text2Tokenizer'],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'Speech2Text2ForCausalLM',
						    'Speech2Text2PreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_speech_to_text_a import SPEECH_TO_TEXT_2_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextaConfig
						from .processing_speech_to_text_a import SpeechaTextaProcessor
						from .tokenization_speech_to_text_a import SpeechaTextaTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_speech_to_text_a import (
												    SPEECH_TO_TEXT_2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SpeechaTextaForCausalLM,
												    SpeechaTextaPreTrainedModel,
												)

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




