



"""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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Dict:
											snake_case_ :str						=			"""hf-internal-testing/tiny-random-t5"""
											snake_case_ :str						=			AutoTokenizer.from_pretrained(snake_case						)
											snake_case_ :Tuple						=			AutoModelForSeqaSeqLM.from_pretrained(snake_case						)

											snake_case_ :Optional[int]						=			tokenizer("""This is me"""						,     return_tensors="""pt"""						)

											snake_case_ :int						=			model.to_bettertransformer()

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

											snake_case_ :Optional[int]						=			model.generate(**snake_case						)

											snake_case_ :List[str]						=			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(snake_case						)

																	snake_case_ :Union[str, Any]						=			AutoModelForSeqaSeqLM.from_pretrained(snake_case						)

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

																	snake_case_ :int						=			model_reloaded.generate(**snake_case						)
																	self.assertTrue(torch.allclose(snake_case						,     snake_case						)						)






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

											snake_case_ :Dict						=			model.to_bettertransformer()

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

																	snake_case_ :Union[str, Any]						=			model.reverse_bettertransformer()
																	model.save_pretrained(snake_case						)




def 				UpperCamelCase_(      lowerCamelCase_ )    ->						int:
    if not numbers:
        return 0

    if not isinstance(lowerCamelCase_							, (list, tuple) ) or not all(
        isinstance(lowerCamelCase_							, lowerCamelCase_ ) for number in numbers ):
        raise ValueError('numbers must be an iterable of integers' )

    _lowercase       :							int      		=		numbers[0]

    for i in range(1							, len(lowerCamelCase_ ) ):
        # update the maximum and minimum subarray products
        _lowercase       :							Union[str, Any]      		=		numbers[i]
        if number < 0:
            _lowercase		,				_lowercase       :							Any      		=		min_till_now, max_till_now
        _lowercase       :							Union[str, Any]      		=		max(lowerCamelCase_							, max_till_now * number )
        _lowercase       :							Union[str, Any]      		=		min(lowerCamelCase_							, min_till_now * number )

        # update the maximum product found till now
        _lowercase       :							Optional[Any]      		=		max(lowerCamelCase_							, lowerCamelCase_ )

    return max_prod


