

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   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									# For consistency across different places the DisjunctiveConstraint is called,
									# dc.token_ids is a list of integers. It is also initialized only by integers.

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [[1, 2, 4], [1, 2, 3, 4]]
									SCREAMING_SNAKE_CASE_: Any  		=       DisjunctiveConstraint(lowerCAmelCase__)
									self.assertTrue(isinstance(dc.token_ids   ,						lowerCAmelCase__))

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

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									# We can't have constraints that are complete subsets of another. This leads to a preverse
									# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
									# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
									# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
									# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2], [1, 2, 3, 4]]

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       [[1, 2, 3], [1, 2, 4]]

									SCREAMING_SNAKE_CASE_: Tuple  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: int  		=       dc.update(1)
									SCREAMING_SNAKE_CASE_: Dict  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: str  		=       dc.update(2)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       stepped is True and completed is False and reset is False
									self.assertTrue(lowerCAmelCase__)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1, 2])

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




			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

									SCREAMING_SNAKE_CASE_: List[Any]  		=       DisjunctiveConstraint(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.current_seq == [1])

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

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

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

									dc.reset()

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       dc.update(1)
									self.assertTrue(not dc.completed)
									self.assertTrue(dc.remaining() == 3)
									self.assertTrue(dc.current_seq == [1])

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

									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       dc.update(5)
									self.assertTrue(dc.completed)  # Completed!
									self.assertTrue(dc.remaining() == 0)
									self.assertTrue(dc.current_seq == [1, 2, 5])













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase						:   Any			  =     {
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   int			  =     ['''MvpTokenizerFast''']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase						:   str			  =     [
				    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''MvpForCausalLM''',
				    '''MvpForConditionalGeneration''',
				    '''MvpForQuestionAnswering''',
				    '''MvpForSequenceClassification''',
				    '''MvpModel''',
				    '''MvpPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
				from .tokenization_mvp import MvpTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_mvp_fast import MvpTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mvp import (
								    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MvpForCausalLM,
								    MvpForConditionalGeneration,
								    MvpForQuestionAnswering,
								    MvpForSequenceClassification,
								    MvpModel,
								    MvpPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase						:   List[str]			  =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




