





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):




	'''simple docstring'''




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

								__UpperCamelCase			:     Any         =      [[1, 2, 4], [1, 2, 3, 4]]
								__UpperCamelCase			:     str         =      DisjunctiveConstraint(a		)
								self.assertTrue(isinstance(dc.token_ids  ,     a		)		)

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

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







	def      _lowerCamelCase       (      self	:Any		)	->				Any:
								# 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).
								__UpperCamelCase			:     List[str]         =      [[1, 2], [1, 2, 3, 4]]

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







	def      _lowerCamelCase       (      self	:Optional[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Tuple         =      [[1, 2, 3], [1, 2, 4]]

								__UpperCamelCase			:     List[Any]         =      DisjunctiveConstraint(a		)

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     int         =      dc.update(1		)
								__UpperCamelCase			:     Optional[Any]         =      stepped is True and completed is False and reset is False
								self.assertTrue(a		)
								self.assertTrue(not dc.completed		)
								self.assertTrue(dc.current_seq == [1]		)

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

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







	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[int]:
								__UpperCamelCase			:     Dict         =      [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

								__UpperCamelCase			:     Optional[int]         =      DisjunctiveConstraint(a		)

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     str         =      dc.update(1		)
								self.assertTrue(not dc.completed		)
								self.assertTrue(dc.current_seq == [1]		)

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      dc.update(2		)
								self.assertTrue(not dc.completed		)
								self.assertTrue(dc.current_seq == [1, 2]		)

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

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

								dc.reset()

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Optional[int]         =      dc.update(1		)
								self.assertTrue(not dc.completed		)
								self.assertTrue(dc.remaining() == 3		)
								self.assertTrue(dc.current_seq == [1]		)

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      dc.update(2		)
								self.assertTrue(not dc.completed		)
								self.assertTrue(dc.remaining() == 2		)
								self.assertTrue(dc.current_seq == [1, 2]		)

								__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     Tuple         =      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_torch_available


lowercase						:      Tuple									= {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase						:      Union[str, Any]									= [
		    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'Swinv2ForImageClassification',
		    'Swinv2ForMaskedImageModeling',
		    'Swinv2Model',
		    'Swinv2PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_swinva import (
				    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwinvaForImageClassification,
				    SwinvaForMaskedImageModeling,
				    SwinvaModel,
				    SwinvaPreTrainedModel,
				)


else:
		import sys

		lowercase						:      Optional[Any]									= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)