
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 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					str       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			3
    __magic_name__								=			250

    __magic_name__								=			ids_tensor((batch_size, length)     ,				__lowerCamelCase       )
    __magic_name__								=			torch.ones((batch_size, length)     ,				device=__lowerCamelCase     ,				dtype=torch.float       ) / length
    return input_ids, scores
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Any:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self._get_tensors(5       )

    __magic_name__								=			StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10       ),
            MaxTimeCriteria(max_time=0.1       ),
        ]       )

    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(9       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(10       )
    self.assertTrue(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			MaxLengthCriteria(max_length=10       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(5       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(9       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(10       )
    self.assertTrue(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    __magic_name__								=			MaxNewTokensCriteria(start_length=5     ,				max_new_tokens=5       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(5       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(9       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__	,       __magic_name__								=			self._get_tensors(10       )
    self.assertTrue(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__								=			StoppingCriteriaList([criteria]       )
    self.assertEqual(criteria_list.max_length     ,				10       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__	,       __magic_name__								=			self._get_tensors(5       )

    __magic_name__								=			MaxTimeCriteria(max_time=0.1       )
    self.assertFalse(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )

    __magic_name__								=			MaxTimeCriteria(max_time=0.1     ,				initial_timestamp=time.time() - 0.2       )
    self.assertTrue(criteria(__lowerCamelCase     ,				__lowerCamelCase       )       )





  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10       )]       )     ,				10       )

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

    __magic_name__								=			validate_stopping_criteria(StoppingCriteriaList()     ,				11       )

    self.assertEqual(len(__lowerCamelCase       )     ,				1       )




from __future__ import annotations





def 	__a     (      A__			:  list[int | str]       ):
    create_state_space_tree(A__			,  []			,  0			,  [0 for i in range(len(A__       )       )]       )





def 	__a     (      A__			:  list[int | str]			,  A__			:  list[int | str]			,  A__			:  int			,  A__			:  list[int]			,  ):

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

    for i in range(len(A__       )       ):
        if not index_used[i]:
            current_sequence.append(sequence[i]       )
            SCREAMING_SNAKE_CASE										=   True
            create_state_space_tree(A__			,  A__			,  index + 1			,  A__       )
            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)