



"""simple docstring"""





import warnings

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




class  _snake_case		(    a__ ):







				"""simple docstring"""






				a			=						["""image_processor""", """tokenizer"""]
				a			=						"""CLIPImageProcessor"""
				a			=						("""XLMRobertaTokenizer""", """XLMRobertaTokenizerFast""")





				def __init__(      self							:							Optional[Any]      ,    _A							:							Union[str, Any]=None      ,    _A							:							int=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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__      ,    )
																		_SCREAMING_SNAKE_CASE		:						int			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						int			  =    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							:							Optional[int]      ,    _A							:							Optional[int]=None      ,    _A							:							Optional[Any]=None      ,    _A							:							Union[str, Any]=None      ,    **_A							:							str):



											"""simple docstring"""







											if text is None and images is None:
																		raise ValueError("""You have to specify either text or images. Both cannot be none.""")

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						str			  =    self.image_processor(lowerCAmelCase__      ,    return_tensors=lowerCAmelCase__      ,    **lowerCAmelCase__)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**lowerCAmelCase__)      ,    tensor_type=lowerCAmelCase__)





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							Any      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Tuple      ,    *_A							:							int      ,    **_A							:							int):



											"""simple docstring"""







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





				@property
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))



"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
			import torch

			from transformers import AutoModelForImageClassification

if is_vision_available():
			from transformers import AutoImageProcessor




@require_torch
@require_vision
class  _snake_case		(    unittest.TestCase ):







				"""simple docstring"""






				@slow
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											_SCREAMING_SNAKE_CASE		:						str			  =    AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""")
											model.to(_A)

											from datasets import load_dataset

											_SCREAMING_SNAKE_CASE		:						Any			  =    load_dataset("""nielsr/rvlcdip-demo""")

											_SCREAMING_SNAKE_CASE		:						Any			  =    dataset["""train"""][0]["""image"""].convert("""RGB""")

											_SCREAMING_SNAKE_CASE		:						str			  =    image_processor(_A      ,    return_tensors="""pt""").to(_A)

											# forward pass
											with torch.no_grad():
																		_SCREAMING_SNAKE_CASE		:						Any			  =    model(**_A)
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    outputs.logits

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    torch.Size((1, 1_6))
											self.assertEqual(logits.shape      ,    _A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    torch.tensor(
											    [-0.4_158, -0.4_092, -0.4_347]      ,    device=_A      ,    dtype=torch.float      ,    )
											self.assertTrue(torch.allclose(logits[0, :3]      ,    _A      ,    atol=1e-4))


