




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 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							def    _UpperCAmelCase     (					self       :	int			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""hf-internal-testing/tiny-random-t5"""
										SCREAMING_SNAKE_CASE           =					AutoTokenizer.from_pretrained(a			)
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(a			)

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

										SCREAMING_SNAKE_CASE           =					model.to_bettertransformer()

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

										SCREAMING_SNAKE_CASE           =					model.generate(**a			)

										SCREAMING_SNAKE_CASE           =					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(a			)

													SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(a			)

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

													SCREAMING_SNAKE_CASE           =					model_reloaded.generate(**a			)
													self.assertTrue(torch.allclose(a       ,     a			)			)







							def    _UpperCAmelCase     (					self       :	Union[str, Any]			)			->					Dict:
										SCREAMING_SNAKE_CASE           =					"""hf-internal-testing/tiny-random-t5"""
										SCREAMING_SNAKE_CASE           =					AutoModelForSeqaSeqLM.from_pretrained(a			)

										SCREAMING_SNAKE_CASE           =					model.to_bettertransformer()

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

													SCREAMING_SNAKE_CASE           =					model.reverse_bettertransformer()
													model.save_pretrained(a			)












from __future__ import annotations
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			create_state_space_tree(SCREAMING_SNAKE_CASE					,	[]					,	0					,	[0 for i in range(len(SCREAMING_SNAKE_CASE   )   )]   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	):
			'''simple docstring'''

			if index == len(SCREAMING_SNAKE_CASE   ):
						print(SCREAMING_SNAKE_CASE   )
						return

			for i in range(len(SCREAMING_SNAKE_CASE   )   ):
						if not index_used[i]:
									current_sequence.append(sequence[i]   )
									SCREAMING_SNAKE_CASE           =					True
									create_state_space_tree(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	index + 1					,	SCREAMING_SNAKE_CASE   )
									current_sequence.pop()
									SCREAMING_SNAKE_CASE           =					False
__A						:							list[int | str]							=  [3, 1, 2, 4]
generate_all_permutations(sequence)

__A						:							list[int | str]							=  ["A", "B", "C"]




generate_all_permutations(sequence_a)






