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				):





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							'hf-internal-testing/tiny-random-t5'
										_lowerCAmelCase							=							AutoTokenizer.from_pretrained(lowercase__						)
										_lowerCAmelCase							=							AutoModelForSeqaSeqLM.from_pretrained(lowercase__						)

										_lowerCAmelCase							=							tokenizer('This is me'  ,  return_tensors='pt'						)

										_lowerCAmelCase							=							model.to_bettertransformer()

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

										_lowerCAmelCase							=							model.generate(**lowercase__						)

										_lowerCAmelCase							=							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(lowercase__						)

																	_lowerCAmelCase							=							AutoModelForSeqaSeqLM.from_pretrained(lowercase__						)

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

																	_lowerCAmelCase							=							model_reloaded.generate(**lowercase__						)
																	self.assertTrue(torch.allclose(lowercase__  ,  lowercase__						)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							'hf-internal-testing/tiny-random-t5'
										_lowerCAmelCase							=							AutoModelForSeqaSeqLM.from_pretrained(lowercase__						)

										_lowerCAmelCase							=							model.to_bettertransformer()

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

																	_lowerCAmelCase							=							model.reverse_bettertransformer()
																	model.save_pretrained(lowercase__						)







from math import ceil, sqrt






def _lowerCamelCase  (		snake_case = 1_000_000  ):
							_lowerCAmelCase							=							0

							for outer_width in range(3     , (limit // 4) + 2  ):
														if outer_width**2 > limit:
																					_lowerCAmelCase							=							max(ceil(sqrt(outer_width**2 - limit  )  )     , 1  )
														else:
																					_lowerCAmelCase							=							1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


if __name__ == "__main__":
				print(f"""{solution() = }""")






