



'''simple docstring'''

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class __snake_case(				__SCREAMING_SNAKE_CASE ):


      '''simple docstring'''



      UpperCAmelCase							:   str									=		['image_processor', 'tokenizer']
      UpperCAmelCase							:   str									=		'LayoutLMv3ImageProcessor'
      UpperCAmelCase							:   Optional[int]									=		('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')


      def __init__( self     ,	A_=None     ,	A_=None     ,	**A_				)     ->     str:
       lowerCAmelCase   =     None
       if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""     ,	_a     ,	)
        lowerCAmelCase   =     kwargs.pop("""feature_extractor"""				)

       lowerCAmelCase   =     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__(_a     ,	_a				)


      def __call__( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = True     ,	A_ = False     ,	A_ = None     ,	A_ = None     ,	A_ = 0     ,	A_ = None     ,	A_ = None     ,	A_ = None     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = False     ,	A_ = True     ,	A_ = None     ,	**A_     ,	)     ->     Tuple:
       # verify input
       if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""				)

       if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""				)

       # first, apply the image processor
       lowerCAmelCase   =     self.image_processor(images=_a     ,	return_tensors=_a				)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(_a     ,	_a				):
         lowerCAmelCase   =     [text]  # add batch dimension (as the image processor always adds a batch dimension)
        lowerCAmelCase   =     features["""words"""]

       lowerCAmelCase   =     self.tokenizer(
           text=text if text is not None else features["""words"""]     ,	text_pair=text_pair if text_pair is not None else None     ,	boxes=boxes if boxes is not None else features["""boxes"""]     ,	word_labels=_a     ,	add_special_tokens=_a     ,	padding=_a     ,	truncation=_a     ,	max_length=_a     ,	stride=_a     ,	pad_to_multiple_of=_a     ,	return_token_type_ids=_a     ,	return_attention_mask=_a     ,	return_overflowing_tokens=_a     ,	return_special_tokens_mask=_a     ,	return_offsets_mapping=_a     ,	return_length=_a     ,	verbose=_a     ,	return_tensors=_a     ,	**_a     ,	)

       # add pixel values
       lowerCAmelCase   =     features.pop("""pixel_values"""				)
       if return_overflowing_tokens is True:
        lowerCAmelCase   =     self.get_overflowing_images(_a     ,	encoded_inputs["""overflow_to_sample_mapping"""]				)
       lowerCAmelCase   =     images

       return encoded_inputs


      def     __snake_case   ( self     ,	A_     ,	A_				)     ->     str:
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       lowerCAmelCase   =     []
       for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]				)

       if len(_a				) != len(_a				):
        raise ValueError(
            """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
            f' {len(_a				)} and {len(_a				)}'				)

       return images_with_overflow


      def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Optional[int]:
       return self.tokenizer.batch_decode(*_a     ,	**_a				)


      def     __snake_case   ( self     ,	*A_     ,	**A_				)     ->     Tuple:
       return self.tokenizer.decode(*_a     ,	**_a				)


      @property
      def     __snake_case   ( self				)     ->     int:
       return ["input_ids", "bbox", "attention_mask", "pixel_values"]


      @property
      def     __snake_case   ( self				)     ->     str:
       warnings.warn(
           """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,	_a     ,	)
       return self.image_processor_class




      @property
      def     __snake_case   ( self				)     ->     Tuple:
       warnings.warn(
           """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,	_a     ,	)
       return self.image_processor




'''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     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)