'''simple docstring'''




from __future__ import annotations

from typing import Generic, TypeVar

_lowercase						: Optional[int]						=						TypeVar("T")





class     __magic_name__						(    Generic[T]):







			def __init__(	self				:  Tuple				,			lowercase_				:  T    ):
										lowercase_					:			List[str]										=       data
										lowercase_					:			Tuple										=       self
										lowercase_					:			List[str]										=       0





class     __magic_name__						(    Generic[T]):







			def __init__(	self				:  Optional[int]    ):
										# map from node name to the node object
										lowercase_					:			dict[T, DisjointSetTreeNode[T]]										=       {}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]				,			lowercase_				:  T    ):
										# create a new set with x as its member
										lowercase_					:			Optional[Any]										=       DisjointSetTreeNode(lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  T    ):
										# find the set x belongs to (with path-compression)
										lowercase_					:			Dict										=       self.map[data]
										if elem_ref != elem_ref.parent:
																	lowercase_					:			Optional[Any]										=       self.find_set(elem_ref.parent.data    )
										return elem_ref.parent







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  DisjointSetTreeNode[T]				,			lowercase_				:  DisjointSetTreeNode[T]    ):
										# helper function for union operation
										if nodea.rank > nodea.rank:
																	lowercase_					:			Optional[int]										=       nodea
										else:
																	lowercase_					:			Union[str, Any]										=       nodea
																	if nodea.rank == nodea.rank:
																								nodea.rank += 1







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  T				,			lowercase_				:  T    ):
										# merge 2 disjoint sets
										self.link(self.find_set(lowercase_    )				,			self.find_set(lowercase_    )    )





class     __magic_name__						(    Generic[T]):







			def __init__(	self				:  Optional[int]    ):
										# connections: map from the node to the neighbouring nodes (with weights)
										lowercase_					:			dict[T, dict[T, int]]										=       {}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict				,			lowercase_				:  T    ):
										# add a node ONLY if its not present in the graph
										if node not in self.connections:
																	lowercase_					:			Union[str, Any]										=       {}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  T				,			lowercase_				:  T				,			lowercase_				:  int    ):
										# add an edge with the given weight
										self.add_node(lowercase_    )
										self.add_node(lowercase_    )
										lowercase_					:			Optional[Any]										=       weight
										lowercase_					:			Union[str, Any]										=       weight







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[Any]										=       []
										lowercase_					:			str										=       set()
										for start in self.connections:
																	for end in self.connections[start]:
																								if (start, end) not in seen:
																															seen.add((end, start)    )
																															edges.append((start, end, self.connections[start][end])    )
										edges.sort(key=lambda lowercase_    : x[2]    )

										# creating the disjoint set
										lowercase_					:			List[Any]										=       DisjointSetTree[T]()
										for node in self.connections:
																	disjoint_set.make_set(lowercase_    )

										# MST generation
										lowercase_					:			List[str]										=       0
										lowercase_					:			Union[str, Any]										=       0
										lowercase_					:			Any										=       GraphUndirectedWeighted[T]()
										while num_edges < len(self.connections    ) - 1:
																	lowercase_						,						lowercase_						,						lowercase_					:			str										=       edges[index]
																	index += 1
																	lowercase_					:			Optional[Any]										=       disjoint_set.find_set(lowercase_    )
																	lowercase_					:			Any										=       disjoint_set.find_set(lowercase_    )
																	if parent_u != parent_v:
																								num_edges += 1
																								graph.add_edge(lowercase_				,			lowercase_				,			lowercase_    )
																								disjoint_set.union(lowercase_				,			lowercase_    )
										return graph






'''simple docstring'''




import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
						import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin





@require_torch
class     __magic_name__						(    unittest.TestCase,     _UpperCAmelCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			List[str]										=       load_tool("""text-to-speech"""    )
										self.tool.setup()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			List[str]										=       self.tool("""hey"""    )
										lowercase_					:			Optional[int]										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										# SpeechT5 isn't deterministic
										torch.manual_seed(0    )
										lowercase_					:			Union[str, Any]										=       self.tool("""hey"""    )
										lowercase_					:			int										=       result.to_raw()
										self.assertTrue(
										    torch.allclose(
										        resulting_tensor[:3]				,			torch.tensor([-0.0_00_59_66_66_88_32_11_58_29, -0.0_00_36_57_64_01_90_79_50_64, -0.00_01_34_39_50_27_99_88_34_85]    )				,			)    )





