




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import MobileNetVaImageProcessor

class   __a(					unittest.TestCase       ):






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=18       ,_SCREAMING_SNAKE_CASE=30       ,_SCREAMING_SNAKE_CASE=400       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=None       ,)			->		Optional[Any]:
         UpperCAmelCase_      :							str       =      size if size is not None else {'''shortest_edge''': 20}
         UpperCAmelCase_      :							int       =      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
         UpperCAmelCase_      :							Any       =      parent
         UpperCAmelCase_      :							Any       =      batch_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							str       =      image_size
         UpperCAmelCase_      :							Tuple       =      min_resolution
         UpperCAmelCase_      :							Dict       =      max_resolution
         UpperCAmelCase_      :							Union[str, Any]       =      do_resize
         UpperCAmelCase_      :							Optional[int]       =      size
         UpperCAmelCase_      :							int       =      do_center_crop
         UpperCAmelCase_      :							str       =      crop_size





    def 							a__				(				self  )			->		str:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
         }


@require_torch
@require_vision
class   __a(					_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				MobileNetVaImageProcessor if is_vision_available() else None





    def 							a__				(				self  )			->		Optional[int]:
         UpperCAmelCase_      :							Tuple       =      MobileNetVaImageProcessingTester(self  )





    @property
    def 							a__				(				self  )			->		Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 							a__				(				self  )			->		Optional[Any]:
         UpperCAmelCase_      :							Any       =      self.image_processing_class(**self.image_processor_dict  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''do_resize'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''size'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''do_center_crop'''  )  )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE       ,'''crop_size'''  )  )





    def 							a__				(				self  )			->		str:
         UpperCAmelCase_      :							List[str]       =      self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size       ,{'''shortest_edge''': 20}  )
         self.assertEqual(image_processor.crop_size       ,{'''height''': 18, '''width''': 18}  )

         UpperCAmelCase_      :							List[str]       =      self.image_processing_class.from_dict(self.image_processor_dict       ,size=42       ,crop_size=84  )
         self.assertEqual(image_processor.size       ,{'''shortest_edge''': 42}  )
         self.assertEqual(image_processor.crop_size       ,{'''height''': 84, '''width''': 84}  )





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





    def 							a__				(				self  )			->		Any:
         # Initialize image_processing
         UpperCAmelCase_      :							Dict       =      self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         UpperCAmelCase_      :							str       =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,Image.Image  )

         # Test not batched input
         UpperCAmelCase_      :							Union[str, Any]       =      image_processing(image_inputs[0]       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)

         # Test batched
         UpperCAmelCase_      :							Dict       =      image_processing(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)





    def 							a__				(				self  )			->		Tuple:
         # Initialize image_processing
         UpperCAmelCase_      :							Tuple       =      self.image_processing_class(**self.image_processor_dict  )
         # create random numpy tensors
         UpperCAmelCase_      :							List[str]       =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_SCREAMING_SNAKE_CASE       ,numpify=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,np.ndarray  )

         # Test not batched input
         UpperCAmelCase_      :							List[Any]       =      image_processing(image_inputs[0]       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)

         # Test batched
         UpperCAmelCase_      :							Tuple       =      image_processing(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)





    def 							a__				(				self  )			->		Tuple:
         # Initialize image_processing
         UpperCAmelCase_      :							Union[str, Any]       =      self.image_processing_class(**self.image_processor_dict  )
         # create random PyTorch tensors
         UpperCAmelCase_      :							Optional[int]       =      prepare_image_inputs(self.image_processor_tester       ,equal_resolution=_SCREAMING_SNAKE_CASE       ,torchify=_SCREAMING_SNAKE_CASE  )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE       ,torch.Tensor  )

         # Test not batched input
         UpperCAmelCase_      :							Optional[Any]       =      image_processing(image_inputs[0]       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)

         # Test batched
         UpperCAmelCase_      :							Union[str, Any]       =      image_processing(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).pixel_values
         self.assertEqual(
             encoded_images.shape       ,(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,)





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
     from ...processing_utils import ProcessorMixin
     from ...utils import TensorType


__a          =			logging.get_logger(__name__)

__a          =			{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''layoutlmv3'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE=50_265       ,_SCREAMING_SNAKE_CASE=768       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=12       ,_SCREAMING_SNAKE_CASE=3_072       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=512       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=1e-5       ,_SCREAMING_SNAKE_CASE=1       ,_SCREAMING_SNAKE_CASE=0       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=1_024       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=128       ,_SCREAMING_SNAKE_CASE=64       ,_SCREAMING_SNAKE_CASE=256       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=224       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=None       ,**_SCREAMING_SNAKE_CASE       ,)			->		Dict:
         super().__init__(
             vocab_size=_SCREAMING_SNAKE_CASE       ,hidden_size=_SCREAMING_SNAKE_CASE       ,num_hidden_layers=_SCREAMING_SNAKE_CASE       ,num_attention_heads=_SCREAMING_SNAKE_CASE       ,intermediate_size=_SCREAMING_SNAKE_CASE       ,hidden_act=_SCREAMING_SNAKE_CASE       ,hidden_dropout_prob=_SCREAMING_SNAKE_CASE       ,attention_probs_dropout_prob=_SCREAMING_SNAKE_CASE       ,max_position_embeddings=_SCREAMING_SNAKE_CASE       ,type_vocab_size=_SCREAMING_SNAKE_CASE       ,initializer_range=_SCREAMING_SNAKE_CASE       ,layer_norm_eps=_SCREAMING_SNAKE_CASE       ,pad_token_id=_SCREAMING_SNAKE_CASE       ,bos_token_id=_SCREAMING_SNAKE_CASE       ,eos_token_id=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE       ,)
         UpperCAmelCase_      :							Dict       =      max_ad_position_embeddings
         UpperCAmelCase_      :							Any       =      coordinate_size
         UpperCAmelCase_      :							Tuple       =      shape_size
         UpperCAmelCase_      :							Optional[int]       =      has_relative_attention_bias
         UpperCAmelCase_      :							Union[str, Any]       =      rel_pos_bins
         UpperCAmelCase_      :							Dict       =      max_rel_pos
         UpperCAmelCase_      :							Union[str, Any]       =      has_spatial_attention_bias
         UpperCAmelCase_      :							Any       =      rel_ad_pos_bins
         UpperCAmelCase_      :							Tuple       =      max_rel_ad_pos
         UpperCAmelCase_      :							List[str]       =      text_embed
         UpperCAmelCase_      :							int       =      visual_embed
         UpperCAmelCase_      :							int       =      input_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							int       =      patch_size
         UpperCAmelCase_      :							Dict       =      classifier_dropout


class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				version.parse('''1.12'''       )





    @property
    def 							a__				(				self  )			->		Mapping[str, Mapping[int, str]]:
         # The order of inputs is different for question answering and sequence classification
         if self.task in ["question-answering", "sequence-classification"]:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ]  )
         else:
              return OrderedDict(
                  [
                      ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                      ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                      ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
                  ]  )





    @property
    def 							a__				(				self  )			->		float:
         return 1e-5





    @property
    def 							a__				(				self  )			->		int:
         return 12





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = -1       ,_SCREAMING_SNAKE_CASE = False       ,_SCREAMING_SNAKE_CASE = None       ,_SCREAMING_SNAKE_CASE = 3       ,_SCREAMING_SNAKE_CASE = 40       ,_SCREAMING_SNAKE_CASE = 40       ,)			->		Mapping[str, Any]:
         setattr(processor.image_processor       ,'''apply_ocr'''       ,_SCREAMING_SNAKE_CASE  )

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         UpperCAmelCase_      :							List[str]       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_batch       ,num_token_to_add=0  )
         # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
         UpperCAmelCase_      :							Optional[Any]       =      processor.tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      compute_effective_axis_dimension(
             _SCREAMING_SNAKE_CASE       ,fixed_dimension=OnnxConfig.default_fixed_sequence       ,num_token_to_add=_SCREAMING_SNAKE_CASE  )
         # Generate dummy inputs according to compute batch and sequence
         UpperCAmelCase_      :							Optional[Any]       =      [[''' '''.join([processor.tokenizer.unk_token]  ) * seq_length]] * batch_size

         # Generate dummy bounding boxes
         UpperCAmelCase_      :							Tuple       =      [[[48, 84, 73, 128]]] * batch_size

         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         # batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
         UpperCAmelCase_      :							Union[str, Any]       =      self._generate_dummy_images(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      dict(
             processor(
                 _SCREAMING_SNAKE_CASE       ,text=_SCREAMING_SNAKE_CASE       ,boxes=_SCREAMING_SNAKE_CASE       ,return_tensors=_SCREAMING_SNAKE_CASE       ,)  )

         return inputs