"""simple docstring"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, 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 DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





@require_torch
@require_vision
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=42    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    Image.Image    )

										# Test not batched input
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    numpify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    np.ndarray    )

										# Test not batched input
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    torchify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    torch.Tensor    )

										# Test not batched input
										snake_case								=			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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    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.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )



"""simple docstring"""
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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)

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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Dict			  =						"""layoutlmv3"""

				def __init__(				self		,    lowerCAmelCase=5_02_65		,    lowerCAmelCase=7_68		,    lowerCAmelCase=12		,    lowerCAmelCase=12		,    lowerCAmelCase=30_72		,    lowerCAmelCase="gelu"		,    lowerCAmelCase=0.1		,    lowerCAmelCase=0.1		,    lowerCAmelCase=5_12		,    lowerCAmelCase=2		,    lowerCAmelCase=0.02		,    lowerCAmelCase=1E-5		,    lowerCAmelCase=1		,    lowerCAmelCase=0		,    lowerCAmelCase=2		,    lowerCAmelCase=10_24		,    lowerCAmelCase=1_28		,    lowerCAmelCase=1_28		,    lowerCAmelCase=True		,    lowerCAmelCase=32		,    lowerCAmelCase=1_28		,    lowerCAmelCase=64		,    lowerCAmelCase=2_56		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=2_24		,    lowerCAmelCase=3		,    lowerCAmelCase=16		,    lowerCAmelCase=None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(
										    vocab_size=lowerCAmelCase		,    hidden_size=lowerCAmelCase		,    num_hidden_layers=lowerCAmelCase		,    num_attention_heads=lowerCAmelCase		,    intermediate_size=lowerCAmelCase		,    hidden_act=lowerCAmelCase		,    hidden_dropout_prob=lowerCAmelCase		,    attention_probs_dropout_prob=lowerCAmelCase		,    max_position_embeddings=lowerCAmelCase		,    type_vocab_size=lowerCAmelCase		,    initializer_range=lowerCAmelCase		,    layer_norm_eps=lowerCAmelCase		,    pad_token_id=lowerCAmelCase		,    bos_token_id=lowerCAmelCase		,    eos_token_id=lowerCAmelCase		,    **lowerCAmelCase		,    )
										snake_case								=			max_ad_position_embeddings
										snake_case								=			coordinate_size
										snake_case								=			shape_size
										snake_case								=			has_relative_attention_bias
										snake_case								=			rel_pos_bins
										snake_case								=			max_rel_pos
										snake_case								=			has_spatial_attention_bias
										snake_case								=			rel_ad_pos_bins
										snake_case								=			max_rel_ad_pos
										snake_case								=			text_embed
										snake_case								=			visual_embed
										snake_case								=			input_size
										snake_case								=			num_channels
										snake_case								=			patch_size
										snake_case								=			classifier_dropout





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						version.parse("""1.12"""					)

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										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 		snake_case (				self    ):






										"""simple docstring"""

										return 1E-5

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return 12

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = -1		,    lowerCAmelCase = -1		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    lowerCAmelCase = 3		,    lowerCAmelCase = 40		,    lowerCAmelCase = 40		,    ):






										"""simple docstring"""

										setattr(processor.image_processor		,    'apply_ocr'		,    lowerCAmelCase    )

										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    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
										snake_case								=			processor.tokenizer.num_special_tokens_to_add(lowerCAmelCase    )
										snake_case								=			compute_effective_axis_dimension(
										    lowerCAmelCase		,    fixed_dimension=OnnxConfig.default_fixed_sequence		,    num_token_to_add=lowerCAmelCase    )
										# Generate dummy inputs according to compute batch and sequence
										snake_case								=			[[' '.join([processor.tokenizer.unk_token]    ) * seq_length]] * batch_size

										# Generate dummy bounding boxes
										snake_case								=			[[[48, 84, 73, 1_28]]] * 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)
										snake_case								=			self._generate_dummy_images(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			dict(
										    processor(
										        lowerCAmelCase		,    text=lowerCAmelCase		,    boxes=lowerCAmelCase		,    return_tensors=lowerCAmelCase		,    )    )

										return inputs


