



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
							import torch

							from transformers.generation import DisjunctiveConstraint







@require_torch
class 			__lowerCAmelCase    (			unittest.TestCase						):



			def     _lowerCamelCase       (						self   :  str)   ->    str:



						"""simple docstring"""






						_UpperCAmelCase		       =   [[1, 2, 4], [1, 2, 3, 4]]
						_UpperCAmelCase		       =   DisjunctiveConstraint(_SCREAMING_SNAKE_CASE)
						self.assertTrue(isinstance(dc.token_ids ,		_SCREAMING_SNAKE_CASE))

						with self.assertRaises(_SCREAMING_SNAKE_CASE):
									DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

						with self.assertRaises(_SCREAMING_SNAKE_CASE):
									DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



			def     _lowerCamelCase       (						self   :  Optional[Any])   ->    Any:



						"""simple docstring"""






						_UpperCAmelCase		       =   [[1, 2], [1, 2, 3, 4]]

						with self.assertRaises(_SCREAMING_SNAKE_CASE):
									DisjunctiveConstraint(_SCREAMING_SNAKE_CASE)  # fails here



			def     _lowerCamelCase       (						self   :  Dict)   ->    List[str]:



						"""simple docstring"""






						_UpperCAmelCase		       =   [[1, 2, 3], [1, 2, 4]]

						_UpperCAmelCase		       =   DisjunctiveConstraint(_SCREAMING_SNAKE_CASE)

						_UpperCAmelCase		       =   dc.update(1)
						_UpperCAmelCase		       =   stepped is True and completed is False and reset is False
						self.assertTrue(_SCREAMING_SNAKE_CASE)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.current_seq == [1])

						_UpperCAmelCase		       =   dc.update(2)
						_UpperCAmelCase		       =   stepped is True and completed is False and reset is False
						self.assertTrue(_SCREAMING_SNAKE_CASE)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.current_seq == [1, 2])

						_UpperCAmelCase		       =   dc.update(3)
						_UpperCAmelCase		       =   stepped is True and completed is True and reset is False
						self.assertTrue(_SCREAMING_SNAKE_CASE)
						self.assertTrue(dc.completed)  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 3])



			def     _lowerCamelCase       (						self   :  str)   ->    List[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						_UpperCAmelCase		       =   DisjunctiveConstraint(_SCREAMING_SNAKE_CASE)

						_UpperCAmelCase		       =   dc.update(1)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.current_seq == [1])

						_UpperCAmelCase		       =   dc.update(2)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.current_seq == [1, 2])

						_UpperCAmelCase		       =   dc.update(4)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.current_seq == [1, 2, 4])

						_UpperCAmelCase		       =   dc.update(5)
						self.assertTrue(dc.completed)  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 4, 5])

						dc.reset()

						_UpperCAmelCase		       =   dc.update(1)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.remaining() == 3)
						self.assertTrue(dc.current_seq == [1])

						_UpperCAmelCase		       =   dc.update(2)
						self.assertTrue(not dc.completed)
						self.assertTrue(dc.remaining() == 2)
						self.assertTrue(dc.current_seq == [1, 2])

						_UpperCAmelCase		       =   dc.update(5)
						self.assertTrue(dc.completed)  # Completed!
						self.assertTrue(dc.remaining() == 0)
						self.assertTrue(dc.current_seq == [1, 2, 5])




from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




