



"""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       UpperCamelCase__			(   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__=False						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]						,	SCREAMING_SNAKE_CASE__=[0.5, 0.5, 0.5]						,	)      ->	Any:
				A__											=					parent
				A__											=					batch_size
				A__											=					num_channels
				A__											=					image_size
				A__											=					min_resolution
				A__											=					max_resolution
				A__											=					do_resize
				A__											=					size if size is not None else {"height": 18, "width": 20}
				A__											=					do_thumbnail
				A__											=					do_align_axis
				A__											=					do_pad
				A__											=					do_normalize
				A__											=					image_mean
				A__											=					image_std







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				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       UpperCamelCase__			(   _lowerCAmelCase       ,		unittest.TestCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     DonutImageProcessor if is_vision_available() else None


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					DonutImageProcessingTester(self		)


			@property
			def   snake_case__	(      self		)      ->	List[str]:
				return self.image_processor_tester.prepare_image_processor_dict()


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					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_thumbnail"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_align_long_axis"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_pad"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"do_normalize"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"image_mean"		)		)
				self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"image_std"		)		)


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size						,	{"height": 18, "width": 20}		)

				A__											=					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
				A__											=					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		)      ->	Any:
				pass


			@is_flaky()
			def   snake_case__	(      self		)      ->	Tuple:
				# Initialize image_processing
				A__											=					self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				A__											=					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
				A__											=					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
				A__											=					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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)


			@is_flaky()
			def   snake_case__	(      self		)      ->	Any:
				# Initialize image_processing
				A__											=					self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				A__											=					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
				A__											=					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
				A__											=					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.size["height"],
				        self.image_processor_tester.size["width"],
				    )						,	)







			@is_flaky()
			def   snake_case__	(      self		)      ->	Dict:
				# Initialize image_processing
				A__											=					self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				A__											=					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
				A__											=					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
				A__											=					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.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


UpperCamelCase     =							logging.get_logger(__name__)

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






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					int							  =     "layoutlmv3"


			def __init__(      self						,	SCREAMING_SNAKE_CASE__=50265						,	SCREAMING_SNAKE_CASE__=768						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=12						,	SCREAMING_SNAKE_CASE__=3072						,	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__=0.0_2						,	SCREAMING_SNAKE_CASE__=1e-5						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1024						,	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__						,	)      ->	Any:
				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__						,	)
				A__											=					max_ad_position_embeddings
				A__											=					coordinate_size
				A__											=					shape_size
				A__											=					has_relative_attention_bias
				A__											=					rel_pos_bins
				A__											=					max_rel_pos
				A__											=					has_spatial_attention_bias
				A__											=					rel_ad_pos_bins
				A__											=					max_rel_ad_pos
				A__											=					text_embed
				A__											=					visual_embed
				A__											=					input_size
				A__											=					num_channels
				A__											=					patch_size
				A__											=					classifier_dropout






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Union[str, Any]							  =     version.parse("1.12"      )


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


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







			def   snake_case__	(      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
				A__											=					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
				A__											=					processor.tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__		)
				A__											=					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
				A__											=					[[" ".join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

				# Generate dummy bounding boxes
				A__											=					[[[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)
				A__											=					self._generate_dummy_images(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)

				A__											=					dict(
				    processor(
				        SCREAMING_SNAKE_CASE__						,	text=SCREAMING_SNAKE_CASE__						,	boxes=SCREAMING_SNAKE_CASE__						,	return_tensors=SCREAMING_SNAKE_CASE__						,	)		)

				return inputs




