


'''simple docstring'''



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class  UpperCamelCase__(     lowerCAmelCase ):
				__magic_name__       :   Tuple							= ["image_processor", "tokenizer"]
				__magic_name__       :   Any							= "ViTImageProcessor"
				__magic_name__       :   str							= ("CLIPTokenizer", "CLIPTokenizerFast")




				def __init__(	self    :				List[str]   ,					lowerCAmelCase    :				List[Any]=None   ,					lowerCAmelCase    :				List[str]=None   ,					**lowerCAmelCase    :				Optional[int]   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			None
										if "feature_extractor" in kwargs:
																warnings.warn(
																    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
																    ''' instead.'''   ,					lowerCAmelCase   ,					)
																UpperCAmelCase         =			kwargs.pop('''feature_extractor'''   )

										UpperCAmelCase         =			image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
																raise ValueError('''You need to specify an `image_processor`.'''   )
										if tokenizer is None:
																raise ValueError('''You need to specify a `tokenizer`.'''   )

										super().__init__(lowerCAmelCase   ,					lowerCAmelCase   )




				def __call__(	self    :				str   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Optional[Any]=None   ,					lowerCAmelCase    :				Dict=None   ,					lowerCAmelCase    :				str=None   ,					**lowerCAmelCase    :				List[str]   )->  List[Any]:

										"""simple docstring"""




										if text is None and visual_prompt is None and images is None:
																raise ValueError('''You have to specify either text, visual prompt or images.'''   )

										if text is not None and visual_prompt is not None:
																raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''   )

										if text is not None:
																UpperCAmelCase         =			self.tokenizer(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if images is not None:
																UpperCAmelCase         =			self.image_processor(lowerCAmelCase   ,					return_tensors=lowerCAmelCase   ,					**lowerCAmelCase   )

										if visual_prompt is not None and images is not None:
																UpperCAmelCase         =			{
																    '''pixel_values''': image_features.pixel_values,
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										elif text is not None and images is not None:
																UpperCAmelCase         =			image_features.pixel_values
																return encoding
										elif text is not None:
																return encoding
										elif visual_prompt is not None:
																UpperCAmelCase         =			{
																    '''conditional_pixel_values''': prompt_features.pixel_values,
																}
																return encoding
										else:
																return BatchEncoding(data=dict(**lowerCAmelCase   )   ,					tensor_type=lowerCAmelCase   )




				def   a__(	self    :				List[str]   ,					*lowerCAmelCase    :				str   ,					**lowerCAmelCase    :				Optional[int]   )->  Union[str, Any]:

										"""simple docstring"""




										return self.tokenizer.batch_decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				def   a__(	self    :				Dict   ,					*lowerCAmelCase    :				Tuple   ,					**lowerCAmelCase    :				List[str]   )->  Optional[Any]:

										"""simple docstring"""




										return self.tokenizer.decode(*lowerCAmelCase   ,					**lowerCAmelCase   )




				@property
				def   a__(	self    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor_class





				@property
				def   a__(	self    :				Any   )->  List[Any]:

										"""simple docstring"""




										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''   ,					lowerCAmelCase   ,					)
										return self.image_processor









'''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  UpperCamelCase__(     unittest.TestCase ):




				def   a__(	self    :				Optional[int]   )->  str:

										"""simple docstring"""




										UpperCAmelCase         =			[[1, 2, 4], [1, 2, 3, 4]]
										UpperCAmelCase         =			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   a__(	self    :				Union[str, Any]   )->  str:

										"""simple docstring"""




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

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




				def   a__(	self    :				Any   )->  Optional[int]:

										"""simple docstring"""




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

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(1   )
										UpperCAmelCase         =			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]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(2   )
										UpperCAmelCase         =			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]   )

										UpperCAmelCase       ,       UpperCAmelCase       ,       UpperCAmelCase         =			dc.update(3   )
										UpperCAmelCase         =			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   a__(	self    :				int   )->  Dict:

										"""simple docstring"""




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

										UpperCAmelCase         =			DisjunctiveConstraint(lowerCAmelCase   )

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

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

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

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

										dc.reset()

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

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

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





