



from __future__ import annotations

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





@total_ordering
class 		UpperCAmelCase						(						snake_case_				):





    def __lt__(	self       ,     _lowerCAmelCase			):
          return self[-1] < other[-1]





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






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

    # use a heap-based merge to merge stack efficiently
      _lowerCAmelCase 			=  merge(*(reversed(_SCREAMING_SNAKE_CASE				) 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))




from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch

from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch





@require_torch
@require_tf
class 		UpperCAmelCase						(						snake_case_				):





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  SMALL_MODEL_IDENTIFIER
         _lowerCAmelCase 			=  '''pt'''
         _lowerCAmelCase 			=  '''tf'''





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  AutoModel.from_pretrained(self.test_model			)
         model_pt.save_pretrained(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self       ,     _lowerCAmelCase			):
         _lowerCAmelCase 			=  TFAutoModel.from_pretrained(self.test_model       ,     from_pt=_lowerCAmelCase			)
         model_tf.save_pretrained(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  '''mock_framework'''

         # Framework provided - return whatever the user provides
         _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model       ,     _lowerCAmelCase			)
         self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         # Local checkpoint and framework provided - return provided framework
         # PyTorch checkpoint
         with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)

         # TensorFlow checkpoint
         with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase       ,     _lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     _lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         # PyTorch checkpoint
         with TemporaryDirectory() as local_pt_ckpt:
               self._setup_pt_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # TensorFlow checkpoint
         with TemporaryDirectory() as local_tf_ckpt:
               self._setup_tf_ckpt(_lowerCAmelCase			)
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_tf			)

         # Invalid local checkpoint
         with TemporaryDirectory() as local_invalid_ckpt:
               with self.assertRaises(_lowerCAmelCase			):
                     _lowerCAmelCase 			=  FeaturesManager.determine_framework(_lowerCAmelCase			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # PyTorch not in environment -> use TensorFlow
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_tf			)

         # Both in environment -> use PyTorch
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			), patch(
             '''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)
               self.assertEqual(_lowerCAmelCase       ,     self.framework_pt			)

         # Both not in environment -> raise error
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         _lowerCAmelCase 			=  MagicMock(return_value=_lowerCAmelCase			)
         with patch('''transformers.onnx.features.is_tf_available'''       ,     _lowerCAmelCase			), patch(
             '''transformers.onnx.features.is_torch_available'''       ,     _lowerCAmelCase			):
               with self.assertRaises(_lowerCAmelCase			):
                     _lowerCAmelCase 			=  FeaturesManager.determine_framework(self.test_model			)