






'''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       snake_case__	( unittest.TestCase):




					def 						A	(   self   :						Optional[Any]	)  ->				Any:
								UpperCAmelCase_	:    Optional[int]	  =    "laion/clap-htsat-unfused"
								UpperCAmelCase_	:    Optional[int]	  =    tempfile.mkdtemp()




					def 						A	(   self   :						List[Any]							,      **_A   :						Dict	)  ->				Any:
								return RobertaTokenizer.from_pretrained(self.checkpoint							,      **_a	)




					def 						A	(   self   :						str							,      **_A   :						List[Any]	)  ->				str:
								return ClapFeatureExtractor.from_pretrained(self.checkpoint							,      **_a	)




					def 						A	(   self   :						Tuple	)  ->				Union[str, Any]:
								shutil.rmtree(self.tmpdirname	)




					def 						A	(   self   :						Union[str, Any]	)  ->				str:
								UpperCAmelCase_	:    Optional[int]	  =    self.get_tokenizer()
								UpperCAmelCase_	:    List[Any]	  =    self.get_feature_extractor()

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

								processor.save_pretrained(self.tmpdirname	)
								UpperCAmelCase_	:    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 						A	(   self   :						int	)  ->				List[Any]:
								UpperCAmelCase_	:    Tuple	  =    ClapProcessor(tokenizer=self.get_tokenizer()							,      feature_extractor=self.get_feature_extractor()	)
								processor.save_pretrained(self.tmpdirname	)

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

								UpperCAmelCase_	:    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 						A	(   self   :						Optional[Any]	)  ->				int:
								UpperCAmelCase_	:    List[Any]	  =    self.get_feature_extractor()
								UpperCAmelCase_	:    List[str]	  =    self.get_tokenizer()

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

								UpperCAmelCase_	:    Dict	  =    floats_list((3, 10_00)	)

								UpperCAmelCase_	:    str	  =    feature_extractor(_a							,      return_tensors='''np'''	)
								UpperCAmelCase_	:    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 						A	(   self   :						str	)  ->				Tuple:
								UpperCAmelCase_	:    Dict	  =    self.get_feature_extractor()
								UpperCAmelCase_	:    int	  =    self.get_tokenizer()

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

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

								UpperCAmelCase_	:    Any	  =    processor(text=_a	)

								UpperCAmelCase_	:    List[Any]	  =    tokenizer(_a	)

								for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]							,      encoded_processor[key]	)




					def 						A	(   self   :						int	)  ->				int:
								UpperCAmelCase_	:    Optional[int]	  =    self.get_feature_extractor()
								UpperCAmelCase_	:    Any	  =    self.get_tokenizer()

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

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

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

								self.assertListEqual(_a							,      _a	)





					def 						A	(   self   :						str	)  ->				List[str]:
								UpperCAmelCase_	:    List[Any]	  =    self.get_feature_extractor()
								UpperCAmelCase_	:    Union[str, Any]	  =    self.get_tokenizer()

								UpperCAmelCase_	:    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'''							,      )




"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge

@total_ordering
class 		a__						(      a_						):
   def __lt__(			self	,    _a				):
          return self[-1] < other[-1]

   def __eq__(			self	,    _a				):
          return self[-1] == other[-1]


def   __magic_name__	(      __snake_case				:       list  ) ->				list:
       lowercase    :				list[Stack]       		=							[]
       # sort into stacks
       for element in collection:
              lowercase    :				Union[str, Any]       		=							Stack([element]  )
              lowercase    :				Dict       		=							bisect_left(__snake_case     , __snake_case  )
              if i != len(__snake_case  ):
                     stacks[i].append(__snake_case  )
              else:
                     stacks.append(__snake_case  )

    # use a heap-based merge to merge stack efficiently
       lowercase    :				Dict       		=							merge(*(reversed(__snake_case  ) for stack in stacks)  )
       return collection


if __name__ == "__main__":
   _A			:       Optional[int]   		=       input("""Enter numbers separated by a comma:\n""").strip()
   _A			:       Optional[Any]   		=       [int(item) for item in user_input.split(""",""")]
   print(patience_sort(unsorted))


