


"""simple docstring"""


from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a_       							=    TypeVar("T")
class snake_case      (  Generic[T]):







		def __init__(					self      :       Dict    ,   a__      :       bool = True		)       ->						None:





						'''simple docstring'''







						_A					  =			{}  # dictionary of lists
						_A					  =			directed







		def  a_ (					self      :       int    ,   a__      :       T    ,   a__      :       T		)       ->						GraphAdjacencyList[T]:





						'''simple docstring'''







						if not self.directed:  # For undirected graphs
										# if both source vertex and destination vertex are both present in the
										# adjacency list, add destination vertex to source vertex list of adjacent
										# vertices and add source vertex to destination vertex list of adjacent
										# vertices.
										if source_vertex in self.adj_list and destination_vertex in self.adj_list:
														self.adj_list[source_vertex].append(a__		)
														self.adj_list[destination_vertex].append(a__		)
										# if only source vertex is present in adjacency list, add destination vertex
										# to source vertex list of adjacent vertices, then create a new vertex with
										# destination vertex as key and assign a list containing the source vertex
										# as it's first adjacent vertex.
										elif source_vertex in self.adj_list:
														self.adj_list[source_vertex].append(a__		)
														_A					  =			[source_vertex]
										# if only destination vertex is present in adjacency list, add source vertex
										# to destination vertex list of adjacent vertices, then create a new vertex
										# with source vertex as key and assign a list containing the source vertex
										# as it's first adjacent vertex.
										elif destination_vertex in self.adj_list:
														self.adj_list[destination_vertex].append(a__		)
														_A					  =			[destination_vertex]
										# if both source vertex and destination vertex are not present in adjacency
										# list, create a new vertex with source vertex as key and assign a list
										# containing the destination vertex as it's first adjacent vertex also
										# create a new vertex with destination vertex as key and assign a list
										# containing the source vertex as it's first adjacent vertex.
										else:
														_A					  =			[destination_vertex]
														_A					  =			[source_vertex]
						else:  # For directed graphs
										# if both source vertex and destination vertex are present in adjacency
										# list, add destination vertex to source vertex list of adjacent vertices.
										if source_vertex in self.adj_list and destination_vertex in self.adj_list:
														self.adj_list[source_vertex].append(a__		)
										# if only source vertex is present in adjacency list, add destination
										# vertex to source vertex list of adjacent vertices and create a new vertex
										# with destination vertex as key, which has no adjacent vertex
										elif source_vertex in self.adj_list:
														self.adj_list[source_vertex].append(a__		)
														_A					  =			[]
										# if only destination vertex is present in adjacency list, create a new
										# vertex with source vertex as key and assign a list containing destination
										# vertex as first adjacent vertex
										elif destination_vertex in self.adj_list:
														_A					  =			[destination_vertex]
										# if both source vertex and destination vertex are not present in adjacency
										# list, create a new vertex with source vertex as key and a list containing
										# destination vertex as it's first adjacent vertex. Then create a new vertex
										# with destination vertex as key, which has no adjacent vertex
										else:
														_A					  =			[destination_vertex]
														_A					  =			[]

						return self







		def __repr__(					self      :       int		)       ->						str:





						'''simple docstring'''







						return pformat(self.adj_list		)



"""simple docstring"""


from typing import TYPE_CHECKING

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


a_       							=    {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "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:
		a_       							=    [
		    "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:
		a_       							=    [
		    "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

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