

"""simple docstring"""





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 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					def        __lowerCAmelCase (       self			:				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.

						lowerCAmelCase__	:						List[str]  				=	[[1, 2, 4], [1, 2, 3, 4]]
						lowerCAmelCase__	:						Optional[int]  				=	DisjunctiveConstraint(lowercase_		)
						self.assertTrue(isinstance(dc.token_ids   ,lowercase_		)		)

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

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







					def        __lowerCAmelCase (       self			:				Tuple		):
						# 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).
						lowerCAmelCase__	:						List[str]  				=	[[1, 2], [1, 2, 3, 4]]

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







					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						str  				=	[[1, 2, 3], [1, 2, 4]]

						lowerCAmelCase__	:						Optional[int]  				=	DisjunctiveConstraint(lowercase_		)

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

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

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Optional[int]  				=	[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						lowerCAmelCase__	:						List[Any]  				=	DisjunctiveConstraint(lowercase_		)

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

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

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

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

						dc.reset()

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

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

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







"""simple docstring"""





from typing import TYPE_CHECKING

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


__UpperCamelCase  :      str							      =		{
    '''configuration_longt5''': ['''LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LongT5Config''', '''LongT5OnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      str							      =		[
							    '''LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''LongT5EncoderModel''',
							    '''LongT5ForConditionalGeneration''',
							    '''LongT5Model''',
							    '''LongT5PreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		[
							    '''FlaxLongT5ForConditionalGeneration''',
							    '''FlaxLongT5Model''',
							    '''FlaxLongT5PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_longta import (
														    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LongTaEncoderModel,
														    LongTaForConditionalGeneration,
														    LongTaModel,
														    LongTaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_longta import (
														    FlaxLongTaForConditionalGeneration,
														    FlaxLongTaModel,
														    FlaxLongTaPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      int							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




