



'''simple docstring'''






from __future__ import annotations

from typing import Generic, TypeVar

UpperCamelCase__:     str										=    TypeVar("T")

class        SCREAMING_SNAKE_CASE(							Generic[T]    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  T	)    ->	None:
								UpperCAmelCase				: Any											=	data
								UpperCAmelCase				: int											=	self
								UpperCAmelCase				: Union[str, Any]											=	0

class        SCREAMING_SNAKE_CASE(							Generic[T]    ):







	"""simple docstring"""






	def __init__(		self    :  Union[str, Any]	)    ->	None:
								# map from node name to the node object
								UpperCAmelCase				: dict[T, DisjointSetTreeNode[T]]											=	{}







	def   A			(		self    :  Dict	,    __snake_case    :  T	)    ->	None:
								# create a new set with x as its member
								UpperCAmelCase				: Optional[Any]											=	DisjointSetTreeNode(__snake_case	)







	def   A			(		self    :  Dict	,    __snake_case    :  T	)    ->	DisjointSetTreeNode[T]:
								# find the set x belongs to (with path-compression)
								UpperCAmelCase				: int											=	self.map[data]
								if elem_ref != elem_ref.parent:
															UpperCAmelCase				: Any											=	self.find_set(elem_ref.parent.data	)
								return elem_ref.parent







	def   A			(		self    :  Optional[int]	,    __snake_case    :  DisjointSetTreeNode[T]	,    __snake_case    :  DisjointSetTreeNode[T]	)    ->	None:
								# helper function for union operation
								if nodea.rank > nodea.rank:
															UpperCAmelCase				: int											=	nodea
								else:
															UpperCAmelCase				: List[Any]											=	nodea
															if nodea.rank == nodea.rank:
																						nodea.rank += 1







	def   A			(		self    :  int	,    __snake_case    :  T	,    __snake_case    :  T	)    ->	None:
								# merge 2 disjoint sets
								self.link(self.find_set(__snake_case	)	,    self.find_set(__snake_case	)	)

class        SCREAMING_SNAKE_CASE(							Generic[T]    ):







	"""simple docstring"""






	def __init__(		self    :  Any	)    ->	None:
								# connections: map from the node to the neighbouring nodes (with weights)
								UpperCAmelCase				: dict[T, dict[T, int]]											=	{}







	def   A			(		self    :  int	,    __snake_case    :  T	)    ->	None:
								# add a node ONLY if its not present in the graph
								if node not in self.connections:
															UpperCAmelCase				: List[str]											=	{}







	def   A			(		self    :  Optional[int]	,    __snake_case    :  T	,    __snake_case    :  T	,    __snake_case    :  int	)    ->	None:
								# add an edge with the given weight
								self.add_node(__snake_case	)
								self.add_node(__snake_case	)
								UpperCAmelCase				: Union[str, Any]											=	weight
								UpperCAmelCase				: Union[str, Any]											=	weight







	def   A			(		self    :  Optional[int]	)    ->	GraphUndirectedWeighted[T]:
								UpperCAmelCase				: List[Any]											=	[]
								UpperCAmelCase				: 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 __snake_case	: x[2]	)

								# creating the disjoint set
								UpperCAmelCase				: Optional[int]											=	DisjointSetTree[T]()
								for node in self.connections:
															disjoint_set.make_set(__snake_case	)

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










'''simple docstring'''






import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
						import torch

@require_torch
@require_optimum
@slow
class        SCREAMING_SNAKE_CASE(							unittest.TestCase    ):







	"""simple docstring"""






	def   A			(		self    :  Union[str, Any]	)    ->	Any:
								UpperCAmelCase				: Optional[int]											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: int											=	AutoTokenizer.from_pretrained(__snake_case	)
								UpperCAmelCase				: Tuple											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: Optional[Any]											=	tokenizer('''This is me'''	,    return_tensors='''pt'''	)

								UpperCAmelCase				: Union[str, Any]											=	model.to_bettertransformer()

								self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								UpperCAmelCase				: Dict											=	model.generate(**__snake_case	)

								UpperCAmelCase				: Union[str, Any]											=	model.reverse_bettertransformer()

								self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()	)	)

								with tempfile.TemporaryDirectory() as tmpdirname:
															model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

															self.assertFalse(
															    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()	)	)

															UpperCAmelCase				: Dict											=	model_reloaded.generate(**__snake_case	)
															self.assertTrue(torch.allclose(__snake_case	,    __snake_case	)	)







	def   A			(		self    :  Optional[int]	)    ->	List[Any]:
								UpperCAmelCase				: Dict											=	'''hf-internal-testing/tiny-random-t5'''
								UpperCAmelCase				: Optional[int]											=	AutoModelForSeqaSeqLM.from_pretrained(__snake_case	)

								UpperCAmelCase				: List[Any]											=	model.to_bettertransformer()

								with tempfile.TemporaryDirectory() as tmpdirname:
															with self.assertRaises(__snake_case	):
																						model.save_pretrained(__snake_case	)

															UpperCAmelCase				: Any											=	model.reverse_bettertransformer()
															model.save_pretrained(__snake_case	)





