

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
	import torch

	from transformers.generation import (
	    MaxLengthCriteria,
	    MaxNewTokensCriteria,
	    MaxTimeCriteria,
	    StoppingCriteriaList,
	    validate_stopping_criteria,
	)



@require_torch
class        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						3
						A__				:       Tuple	  =						250

						A__				:       int	  =						ids_tensor((batch_size, length)       ,			A__     )
						A__				:       List[Any]	  =						torch.ones((batch_size, length)       ,			device=A__       ,			dtype=torch.float     ) / length
						return input_ids, scores






					def     __A     (					self     ):
						A__  ,  A__				:       Tuple	  =						self._get_tensors(5     )

						A__				:       Any	  =						StoppingCriteriaList(
						    [
						        MaxLengthCriteria(max_length=10     ),
						        MaxTimeCriteria(max_time=0.1     ),
						    ]     )

						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       Tuple	  =						self._get_tensors(9     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       List[Any]	  =						self._get_tensors(10     )
						self.assertTrue(criteria(A__       ,			A__     )     )






					def     __A     (					self     ):
						A__				:       List[str]	  =						MaxLengthCriteria(max_length=10     )

						A__  ,  A__				:       Optional[Any]	  =						self._get_tensors(5     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       Optional[int]	  =						self._get_tensors(9     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       List[str]	  =						self._get_tensors(10     )
						self.assertTrue(criteria(A__       ,			A__     )     )






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						MaxNewTokensCriteria(start_length=5       ,			max_new_tokens=5     )

						A__  ,  A__				:       Tuple	  =						self._get_tensors(5     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       Any	  =						self._get_tensors(9     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__  ,  A__				:       Tuple	  =						self._get_tensors(10     )
						self.assertTrue(criteria(A__       ,			A__     )     )

						A__				:       List[Any]	  =						StoppingCriteriaList([criteria]     )
						self.assertEqual(criteria_list.max_length       ,			10     )






					def     __A     (					self     ):
						A__  ,  A__				:       Dict	  =						self._get_tensors(5     )

						A__				:       Any	  =						MaxTimeCriteria(max_time=0.1     )
						self.assertFalse(criteria(A__       ,			A__     )     )

						A__				:       List[Any]	  =						MaxTimeCriteria(max_time=0.1       ,			initial_timestamp=time.time() - 0.2     )
						self.assertTrue(criteria(A__       ,			A__     )     )






					def     __A     (					self     ):
						validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10     )]     )       ,			10     )

						with self.assertWarns(A__     ):
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10     )]     )       ,			11     )

						A__				:       int	  =						validate_stopping_criteria(StoppingCriteriaList()       ,			11     )

						self.assertEqual(len(A__     )       ,			1     )






def 		UpperCamelCase   (lowercase_:					str   )	-> Optional[Any]:
	A__				:       Optional[int]	  =						[]
	A__				:       Optional[int]	  =						set({"""(""", """[""", """{"""}   )
	A__				:       str	  =						set({""")""", """]""", """}"""}   )
	A__				:       Optional[Any]	  =						{"""{""": """}""", """[""": """]""", """(""": """)"""}

	for i in range(len(lowercase_   )   ):
		if s[i] in open_brackets:
			stack.append(s[i]   )

		elif s[i] in closed_brackets and (
		    len(lowercase_   ) == 0 or (len(lowercase_   ) > 0 and open_to_closed[stack.pop()] != s[i])
		):
			return False

	return len(lowercase_   ) == 0

def 		UpperCamelCase   ()	-> List[str]:
	A__				:       Optional[Any]	  =						input("""Enter sequence of brackets: """   )
	if is_balanced(lowercase_   ):
		print(lowercase_       , """is balanced"""   )
	else:
		print(lowercase_       , """is not balanced"""   )


if __name__ == "__main__":
	main()



