



"""simple docstring"""
from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE  :						int    							=      {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      ['''RoFormerTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RoFormerForCausalLM''',
			    '''RoFormerForMaskedLM''',
			    '''RoFormerForMultipleChoice''',
			    '''RoFormerForQuestionAnswering''',
			    '''RoFormerForSequenceClassification''',
			    '''RoFormerForTokenClassification''',
			    '''RoFormerLayer''',
			    '''RoFormerModel''',
			    '''RoFormerPreTrainedModel''',
			    '''load_tf_weights_in_roformer''',
			]


try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Any    							=      [
			    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRoFormerForCausalLM''',
			    '''TFRoFormerForMaskedLM''',
			    '''TFRoFormerForMultipleChoice''',
			    '''TFRoFormerForQuestionAnswering''',
			    '''TFRoFormerForSequenceClassification''',
			    '''TFRoFormerForTokenClassification''',
			    '''TFRoFormerLayer''',
			    '''TFRoFormerModel''',
			    '''TFRoFormerPreTrainedModel''',
			]


try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE  :						Dict    							=      [
			    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FlaxRoFormerForMaskedLM''',
			    '''FlaxRoFormerForMultipleChoice''',
			    '''FlaxRoFormerForQuestionAnswering''',
			    '''FlaxRoFormerForSequenceClassification''',
			    '''FlaxRoFormerForTokenClassification''',
			    '''FlaxRoFormerModel''',
			    '''FlaxRoFormerPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
			from .tokenization_roformer import RoFormerTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_roformer_fast import RoFormerTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roformer import (
						    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RoFormerForCausalLM,
						    RoFormerForMaskedLM,
						    RoFormerForMultipleChoice,
						    RoFormerForQuestionAnswering,
						    RoFormerForSequenceClassification,
						    RoFormerForTokenClassification,
						    RoFormerLayer,
						    RoFormerModel,
						    RoFormerPreTrainedModel,
						    load_tf_weights_in_roformer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roformer import (
						    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerLayer,
						    TFRoFormerModel,
						    TFRoFormerPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roformer import (
						    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						    FlaxRoFormerPreTrainedModel,
						)


else:
			import sys

			__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





"""simple docstring"""
from typing import Any


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				data
									_lowerCamelCase        =				None


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(    self   ):
									_lowerCamelCase        =				None

					def 			snake_case__      (    self   ):
									_lowerCamelCase        =				self.head
									while temp is not None:
													print(temp.data				,       end=''' '''   )
													_lowerCamelCase        =				temp.next
									print()

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				Node(lowerCamelCase__   )
									_lowerCamelCase        =				self.head
									_lowerCamelCase        =				new_node





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									if node_data_a == node_data_a:
													return
									else:
													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													_lowerCamelCase        =				self.head
													while node_a is not None and node_a.data != node_data_a:
																	_lowerCamelCase        =				node_a.next

													if node_a is None or node_a is None:
																	return

													_lowerCamelCase  ,     _lowerCamelCase        =				node_a.data, node_a.data


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						List[Any]    							=      LinkedList()
			for i in range(5, 0, -1):
						ll.push(i)

			ll.print_list()

			ll.swap_nodes(1, 4)
			print('''After swapping''')
			ll.print_list()
