


import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


SCREAMING_SNAKE_CASE__     					=			logging.get_logger(__name__)



class 						_UpperCAmelCase      (     lowercase							):






		def __init__(						self				:     Dict   ,  *UpperCAmelCase				:     Optional[int]   ,  **UpperCAmelCase				:     List[Any]):
				warnings.warn(
				    "The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use DonutImageProcessor instead."   ,  UpperCAmelCase   ,  )
				super().__init__(*UpperCAmelCase   ,  **UpperCAmelCase)










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 						_UpperCAmelCase      (     unittest.TestCase							):






		@slow
		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoImageProcessor.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				SCREAMING_SNAKE_CASE_				:int				     =						AutoModelForImageClassification.from_pretrained("microsoft/dit-base-finetuned-rvlcdip")
				model.to(UpperCAmelCase)

				from datasets import load_dataset

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						load_dataset("nielsr/rvlcdip-demo")

				SCREAMING_SNAKE_CASE_				:Dict				     =						dataset["train"][0]["image"].convert("RGB")

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						image_processor(UpperCAmelCase   ,  return_tensors="pt").to(UpperCAmelCase)

				# forward pass
				with torch.no_grad():
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						model(**UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:List[str]				     =						outputs.logits

				SCREAMING_SNAKE_CASE_				:int				     =						torch.Size((1, 16))
				self.assertEqual(logits.shape   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						torch.tensor(
				    [-0.4158, -0.4092, -0.4347]   ,  device=UpperCAmelCase   ,  dtype=torch.float   ,  )
				self.assertTrue(torch.allclose(logits[0, :3]   ,  UpperCAmelCase   ,  atol=1E-4))






