



"""simple docstring"""






from __future__ import annotations

from typing import Generic, TypeVar

lowerCAmelCase__              =							TypeVar('''T''')






class 			__snake_case			(      Generic[T]):




  def __init__(					self	:		Optional[int]		,   __lowerCAmelCase	:		T						):






         """simple docstring"""






         _lowerCamelCase		:							Dict            =					data
         _lowerCamelCase		:							str            =					self
         _lowerCamelCase		:							str            =					0






class 			__snake_case			(      Generic[T]):




  def __init__(					self	:		Union[str, Any]						):






         """simple docstring"""






         _lowerCamelCase		:							dict[T, DisjointSetTreeNode[T]]            =					{}




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[str]		,   __lowerCAmelCase	:		T						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					DisjointSetTreeNode(__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		int		,   __lowerCAmelCase	:		T						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					self.map[data]
         if elem_ref != elem_ref.parent:
                _lowerCamelCase		:							Any            =					self.find_set(elem_ref.parent.data						)
         return elem_ref.parent




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict		,   __lowerCAmelCase	:		DisjointSetTreeNode[T]		,   __lowerCAmelCase	:		DisjointSetTreeNode[T]						):






         """simple docstring"""






         if nodea.rank > nodea.rank:
                _lowerCamelCase		:							Tuple            =					nodea
         else:
                _lowerCamelCase		:							Optional[int]            =					nodea
                if nodea.rank == nodea.rank:
                       nodea.rank += 1




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict		,   __lowerCAmelCase	:		T		,   __lowerCAmelCase	:		T						):






         """simple docstring"""






         self.link(self.find_set(__lowerCAmelCase						)		,   self.find_set(__lowerCAmelCase						)						)






class 			__snake_case			(      Generic[T]):




  def __init__(					self	:		int						):






         """simple docstring"""






         _lowerCamelCase		:							dict[T, dict[T, int]]            =					{}




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]		,   __lowerCAmelCase	:		T						):






         """simple docstring"""






         if node not in self.connections:
                _lowerCamelCase		:							List[Any]            =					{}




  def 		SCREAMING_SNAKE_CASE							(					self	:		Dict		,   __lowerCAmelCase	:		T		,   __lowerCAmelCase	:		T		,   __lowerCAmelCase	:		int						):






         """simple docstring"""






         self.add_node(__lowerCAmelCase						)
         self.add_node(__lowerCAmelCase						)
         _lowerCamelCase		:							List[Any]            =					weight
         _lowerCamelCase		:							int            =					weight




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Any            =					[]
         _lowerCamelCase		:							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 __lowerCAmelCase						: x[2]						)

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

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





"""simple docstring"""
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_A							=  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CpmAntForCausalLM""",
        """CpmAntModel""",
        """CpmAntPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

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

