
"""simple docstring"""



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
 from transformers import TvltImageProcessor

if is_speech_available():
 from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       '''ZinengTang/tvlt-base'''
       _snake_case					     =       tempfile.mkdtemp()







     def      lowercase	(							self			:			Any    ,      **_lowerCamelCase			:			Tuple			):
       return TvltImageProcessor.from_pretrained(self.checkpoint    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]    ,      **_lowerCamelCase			:			Tuple			):
       return TvltFeatureExtractor.from_pretrained(self.checkpoint    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       shutil.rmtree(self.tmpdirname			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_feature_extractor()

       _snake_case					     =       TvltProcessor(image_processor=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase			)
       processor.save_pretrained(self.tmpdirname			)
       _snake_case					     =       TvltProcessor.from_pretrained(self.tmpdirname			)

       self.assertIsInstance(processor.feature_extractor    ,      _lowerCamelCase			)
       self.assertIsInstance(processor.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_feature_extractor()

       _snake_case					     =       TvltProcessor(image_processor=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase			)

       _snake_case					     =       np.ones([12000]			)

       _snake_case					     =       feature_extractor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(audio=_lowerCamelCase    ,      return_tensors='''np'''			)

       for key in audio_dict.keys():
         self.assertAlmostEqual(audio_dict[key].sum()    ,      input_processor[key].sum()    ,      delta=1e-2			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_feature_extractor()

       _snake_case					     =       TvltProcessor(image_processor=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase			)

       _snake_case					     =       np.ones([3, 224, 224]			)

       _snake_case					     =       image_processor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(images=_lowerCamelCase    ,      return_tensors='''np'''			)

       for key in image_dict.keys():
         self.assertAlmostEqual(image_dict[key].sum()    ,      input_processor[key].sum()    ,      delta=1e-2			)







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_feature_extractor()

       _snake_case					     =       TvltProcessor(image_processor=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase			)

       _snake_case					     =       np.ones([12000]			)
       _snake_case					     =       np.ones([3, 224, 224]			)

       _snake_case					     =       processor(audio=_lowerCamelCase    ,      images=_lowerCamelCase			)

       self.assertListEqual(list(inputs.keys()			)    ,      ['''audio_values''', '''audio_mask''', '''pixel_values''', '''pixel_mask''']			)

       # test if it raises when no input is passed
       with pytest.raises(_lowerCamelCase			):
         processor()







     def      lowercase	(							self			:			str			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_feature_extractor()

       _snake_case					     =       TvltProcessor(image_processor=_lowerCamelCase    ,      feature_extractor=_lowerCamelCase			)

       self.assertListEqual(
           processor.model_input_names    ,      image_processor.model_input_names + feature_extractor.model_input_names    ,      msg='''`processor` and `image_processor`+`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       lowerCAmelCase__				(						A_	):







     def __lt__(							self			:			Any    ,      _lowerCamelCase			:			int			):
       return self[-1] < other[-1]







     def __eq__(							self			:			int    ,      _lowerCamelCase			:			Optional[Any]			):
       return self[-1] == other[-1]






def 					_UpperCAmelCase      ( __lowerCamelCase   :       list		)     ->					list:
  _snake_case					     =       []
  # sort into stacks
  for element in collection:
    _snake_case					     =       Stack([element]		)
    _snake_case					     =       bisect_left(__lowerCamelCase ,					__lowerCamelCase		)
    if i != len(__lowerCamelCase		):
      stacks[i].append(__lowerCamelCase		)
    else:
      stacks.append(__lowerCamelCase		)

    # use a heap-based merge to merge stack efficiently
  _snake_case					     =       merge(*(reversed(__lowerCamelCase		) for stack in stacks)		)
  return collection


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



