



"""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 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def 		lowerCAmelCase__				(self			)					->      Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					[[1, 2, 4], [1, 2, 3, 4]]
						lowerCAmelCase__					:    Tuple												=					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 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						lowerCAmelCase__					:    List[Any]												=					[[1, 2], [1, 2, 3, 4]]

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



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Any												=					[[1, 2, 3], [1, 2, 4]]

						lowerCAmelCase__					:    Dict												=					DisjunctiveConstraint(__lowerCamelCase			)

						lowerCAmelCase__					:    List[str]												=					dc.update(1			)
						lowerCAmelCase__					:    str												=					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]			)

						lowerCAmelCase__					:    Any												=					dc.update(2			)
						lowerCAmelCase__					:    Union[str, 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]			)

						lowerCAmelCase__					:    List[Any]												=					dc.update(3			)
						lowerCAmelCase__					:    List[str]												=					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 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[Any]												=					[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						lowerCAmelCase__					:    Any												=					DisjunctiveConstraint(__lowerCamelCase			)

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

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

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

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

						dc.reset()

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

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

						lowerCAmelCase__					:    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_torch_available, is_vision_available


__snake_case    :		str  ={
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Tuple  =['ConditionalDetrFeatureExtractor']
							__snake_case    :		Union[str, Any]  =['ConditionalDetrImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'ConditionalDetrForObjectDetection',
							    'ConditionalDetrForSegmentation',
							    'ConditionalDetrModel',
							    'ConditionalDetrPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_conditional_detr import (
							    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    ConditionalDetrConfig,
							    ConditionalDetrOnnxConfig,
							)

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
														from .image_processing_conditional_detr import ConditionalDetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_conditional_detr import (
														    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ConditionalDetrForObjectDetection,
														    ConditionalDetrForSegmentation,
														    ConditionalDetrModel,
														    ConditionalDetrPreTrainedModel,
														)

else:
							import sys

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

