


"""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):


					"""simple docstring"""

					def 		a__						(	self:						List[Any]	)->    Optional[int]:
											lowerCamelCase					:					Union[str, Any]        =	"""hf-internal-testing/tiny-random-t5"""
											lowerCamelCase					:					int        =	AutoTokenizer.from_pretrained(UpperCamelCase__	)
											lowerCamelCase					:					Optional[int]        =	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__	)

											lowerCamelCase					:					Tuple        =	tokenizer("""This is me"""     ,		return_tensors="""pt"""	)

											lowerCamelCase					:					Union[str, Any]        =	model.to_bettertransformer()

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

											lowerCamelCase					:					Dict        =	model.generate(**UpperCamelCase__	)

											lowerCamelCase					:					Dict        =	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(UpperCamelCase__	)

																	lowerCamelCase					:					Union[str, Any]        =	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__	)

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

																	lowerCamelCase					:					str        =	model_reloaded.generate(**UpperCamelCase__	)
																	self.assertTrue(torch.allclose(UpperCamelCase__     ,		UpperCamelCase__	)	)







					def 		a__						(	self:						int	)->    int:
											lowerCamelCase					:					Union[str, Any]        =	"""hf-internal-testing/tiny-random-t5"""
											lowerCamelCase					:					Union[str, Any]        =	AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__	)

											lowerCamelCase					:					Optional[Any]        =	model.to_bettertransformer()

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

																	lowerCamelCase					:					Optional[Any]        =	model.reverse_bettertransformer()
																	model.save_pretrained(UpperCamelCase__	)








"""simple docstring"""


from __future__ import annotations

__lowerCamelCase      :int	    =		10




def   snake_case		(     UpperCamelCase__       :						list[int]    )   ->      list[int]:
      lowerCamelCase					:					int        =	1
      lowerCamelCase					:					Union[str, Any]        =	max(UpperCamelCase__    )
      while placement <= max_digit:
            # declare and initialize empty buckets
            lowerCamelCase					:					list[list]        =	[[] for _ in range(UpperCamelCase__    )]
            # split list_of_ints between the buckets
            for i in list_of_ints:
                  lowerCamelCase					:					Any        =	int((i / placement) % RADIX    )
                  buckets[tmp].append(UpperCamelCase__    )
            # put each buckets' contents into list_of_ints
            lowerCamelCase					:					Dict        =	0
            for b in range(UpperCamelCase__    ):
                  for i in buckets[b]:
                        lowerCamelCase					:					List[str]        =	i
                        a += 1
        # move to next
            placement *= RADIX
      return list_of_ints


if __name__ == "__main__":
    import doctest

    doctest.testmod()




