





'''simple docstring'''






from __future__ import annotations

from typing import Generic, TypeVar

A__				:   List[str]      =TypeVar('''T''')




class  UpperCAmelCase     (			Generic[T]   ):




      def __init__(			self			: List[str]			,			__snake_case			: T  )				->      None:
            _lowerCAmelCase							     =							data
            _lowerCAmelCase							     =							self
            _lowerCAmelCase							     =							0




class  UpperCAmelCase     (			Generic[T]   ):




      def __init__(			self			: List[Any]  )				->      None:
            # map from node name to the node object
            _lowerCAmelCase							     =							{}




      def 				lowercase__    (			self			: Any			,			__snake_case			: T  )				->      None:
            # create a new set with x as its member
            _lowerCAmelCase							     =							DisjointSetTreeNode(__snake_case  )




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




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




      def 				lowercase__    (			self			: List[Any]			,			__snake_case			: T			,			__snake_case			: T  )				->      None:
            # merge 2 disjoint sets
            self.link(self.find_set(__snake_case  )			,			self.find_set(__snake_case  )  )




class  UpperCAmelCase     (			Generic[T]   ):




      def __init__(			self			: Tuple  )				->      None:
            # connections: map from the node to the neighbouring nodes (with weights)
            _lowerCAmelCase							     =							{}




      def 				lowercase__    (			self			: Any			,			__snake_case			: T  )				->      None:
            # add a node ONLY if its not present in the graph
            if node not in self.connections:
                  _lowerCAmelCase							     =							{}




      def 				lowercase__    (			self			: List[Any]			,			__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  )
            _lowerCAmelCase							     =							weight
            _lowerCAmelCase							     =							weight




      def 				lowercase__    (			self			: List[Any]  )				->      GraphUndirectedWeighted[T]:
            _lowerCAmelCase							     =							[]
            _lowerCAmelCase							     =							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
            _lowerCAmelCase							     =							DisjointSetTree[T]()
            for node in self.connections:
                  disjoint_set.make_set(__snake_case  )

            # MST generation
            _lowerCAmelCase							     =							0
            _lowerCAmelCase							     =							0
            _lowerCAmelCase							     =							GraphUndirectedWeighted[T]()
            while num_edges < len(self.connections  ) - 1:
                  _lowerCAmelCase     ,							_lowerCAmelCase     ,							_lowerCAmelCase							     =							edges[index]
                  index += 1
                  _lowerCAmelCase							     =							disjoint_set.find_set(__snake_case  )
                  _lowerCAmelCase							     =							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 shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list

@require_torchaudio
@require_sentencepiece
class 		a__						(      unittest.TestCase						):
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							"laion/clap-htsat-unfused"
          lowercase    :				Optional[int]       		=							tempfile.mkdtemp()
   def 			__magic_name__			(			self	,    **_a				):
          return RobertaTokenizer.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self	,    **_a				):
          return ClapFeatureExtractor.from_pretrained(self.checkpoint	,    **_a				)
   def 			__magic_name__			(			self				):
          shutil.rmtree(self.tmpdirname				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_tokenizer()
          lowercase    :				List[Any]       		=							self.get_feature_extractor()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          processor.save_pretrained(self.tmpdirname				)
          lowercase    :				int       		=							ClapProcessor.from_pretrained(self.tmpdirname				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							ClapProcessor(tokenizer=self.get_tokenizer()	,    feature_extractor=self.get_feature_extractor()				)
          processor.save_pretrained(self.tmpdirname				)

          lowercase    :				Union[str, Any]       		=							self.get_tokenizer(bos_token="(BOS)"	,    eos_token="(EOS)"				)
          lowercase    :				Optional[int]       		=							self.get_feature_extractor(do_normalize=_a	,    padding_value=1.0				)

          lowercase    :				Dict       		=							ClapProcessor.from_pretrained(
              self.tmpdirname	,    bos_token="(BOS)"	,    eos_token="(EOS)"	,    do_normalize=_a	,    padding_value=1.0				)

          self.assertEqual(processor.tokenizer.get_vocab()	,    tokenizer_add_kwargs.get_vocab()				)
          self.assertIsInstance(processor.tokenizer	,    _a				)

          self.assertEqual(processor.feature_extractor.to_json_string()	,    feature_extractor_add_kwargs.to_json_string()				)
          self.assertIsInstance(processor.feature_extractor	,    _a				)
   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				List[str]       		=							self.get_tokenizer()

          lowercase    :				int       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Dict       		=							floats_list((3, 1_000)				)

          lowercase    :				str       		=							feature_extractor(_a	,    return_tensors="np"				)
          lowercase    :				Dict       		=							processor(audios=_a	,    return_tensors="np"				)

          for key in input_feat_extract.keys():
                 self.assertAlmostEqual(input_feat_extract[key].sum()	,    input_processor[key].sum()	,    delta=1E-2				)
   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							self.get_feature_extractor()
          lowercase    :				int       		=							self.get_tokenizer()

          lowercase    :				Dict       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Optional[Any]       		=							"This is a test string"

          lowercase    :				Any       		=							processor(text=_a				)

          lowercase    :				List[Any]       		=							tokenizer(_a				)

          for key in encoded_tok.keys():
                 self.assertListEqual(encoded_tok[key]	,    encoded_processor[key]				)
   def 			__magic_name__			(			self				):
          lowercase    :				Optional[int]       		=							self.get_feature_extractor()
          lowercase    :				Any       		=							self.get_tokenizer()

          lowercase    :				Union[str, Any]       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          lowercase    :				Union[str, Any]       		=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          lowercase    :				str       		=							processor.batch_decode(_a				)
          lowercase    :				Optional[int]       		=							tokenizer.batch_decode(_a				)

          self.assertListEqual(_a	,    _a				)

   def 			__magic_name__			(			self				):
          lowercase    :				List[Any]       		=							self.get_feature_extractor()
          lowercase    :				Union[str, Any]       		=							self.get_tokenizer()

          lowercase    :				Any       		=							ClapProcessor(tokenizer=_a	,    feature_extractor=_a				)

          self.assertListEqual(
              processor.model_input_names[2:]	,    feature_extractor.model_input_names	,    msg="`processor` and `feature_extractor` model input names do not match"	,    )


