"""simple docstring"""


import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
    import torch


@require_torch
@require_optimum
@slow
class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      """hf-internal-testing/tiny-random-t5"""
      _UpperCAmelCase						 =      AutoTokenizer.from_pretrained(__lowerCAmelCase					)
      _UpperCAmelCase						 =      AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase					)

      _UpperCAmelCase						 =      tokenizer("""This is me"""   ,				return_tensors="""pt"""					)

      _UpperCAmelCase						 =      model.to_bettertransformer()

      self.assertTrue(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()					)					)

      _UpperCAmelCase						 =      model.generate(**__lowerCAmelCase					)

      _UpperCAmelCase						 =      model.reverse_bettertransformer()

      self.assertFalse(any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model.named_modules()					)					)

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(__lowerCAmelCase					)

        _UpperCAmelCase						 =      AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase					)

        self.assertFalse(
            any("""BetterTransformer""" in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()					)					)

        _UpperCAmelCase						 =      model_reloaded.generate(**__lowerCAmelCase					)
        self.assertTrue(torch.allclose(__lowerCAmelCase   ,				__lowerCAmelCase					)					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      """hf-internal-testing/tiny-random-t5"""
      _UpperCAmelCase						 =      AutoModelForSeqaSeqLM.from_pretrained(__lowerCAmelCase					)

      _UpperCAmelCase						 =      model.to_bettertransformer()

      with tempfile.TemporaryDirectory() as tmpdirname:
        with self.assertRaises(__lowerCAmelCase					):
          model.save_pretrained(__lowerCAmelCase					)

        _UpperCAmelCase						 =      model.reverse_bettertransformer()
        model.save_pretrained(__lowerCAmelCase					)



"""simple docstring"""


from __future__ import annotations

from random import choice






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  return choice(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      random_pivot(lowercase		)

  # partition based on pivot
  # linear time
  _UpperCAmelCase						 =      [e for e in lst if e < pivot]
  _UpperCAmelCase						 =      [e for e in lst if e > pivot]

  # if we get lucky, pivot might be the element we want.
  # we can easily see this:
  # small (elements smaller than k)
  # + pivot (kth element)
  # + big (elements larger than k)
  if len(lowercase		) == k - 1:
    return pivot
  # pivot is in elements bigger than k
  elif len(lowercase		) < k - 1:
    return kth_number(lowercase   ,k - len(lowercase		) - 1		)
  # pivot is in elements smaller than k
  else:
    return kth_number(lowercase   ,lowercase		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
