"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
	import PIL


_a  :  Dict=				logging.get_logger(__name__)

class 	UpperCamelCase    (					UpperCAmelCase__		):
				UpperCAmelCase       :   Tuple					=	['pixel_values']



				def __init__(self       :   str			,						_A       :   bool = True			,						_A       :   Dict[str, int] = None			,						_A       :   PILImageResampling = PIL.Image.BICUBIC			,						_A       :   bool = True			,						_A       :   Dict[str, int] = None			,						_A       :   Union[int, float] = 1 / 2_55			,						_A       :   bool = True			,						_A       :   bool = True			,						_A       :   Optional[Union[float, List[float]]] = None			,						_A       :   Optional[Union[float, List[float]]] = None			,						**_A       :   Optional[int]			,						)					->			None:
					super().__init__(**_A)
					__snake_case    :							str   	=				size if size is not None else {"""height""": 2_56, """width""": 2_56}
					__snake_case    :							Union[str, Any]   	=				get_size_dict(_A)
					__snake_case    :							List[Any]   	=				crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
					__snake_case    :							Union[str, Any]   	=				get_size_dict(_A			,						param_name='crop_size')

					__snake_case    :							List[str]   	=				do_resize
					__snake_case    :							List[str]   	=				size
					__snake_case    :							Optional[int]   	=				resample
					__snake_case    :							Any   	=				do_center_crop
					__snake_case    :							Optional[Any]   	=				crop_size
					__snake_case    :							Any   	=				do_rescale
					__snake_case    :							Union[str, Any]   	=				rescale_factor
					__snake_case    :							List[str]   	=				do_normalize
					__snake_case    :							Tuple   	=				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__snake_case    :							Union[str, Any]   	=				image_std if image_std is not None else IMAGENET_STANDARD_STD



				def       _lowercase  (self       :   List[str]			,						_A       :   np.ndarray			,						_A       :   Dict[str, int]			,						_A       :   PILImageResampling = PIL.Image.BICUBIC			,						_A       :   Optional[Union[str, ChannelDimension]] = None			,						**_A       :   Tuple			,						)					->			np.ndarray:
					__snake_case    :							Any   	=				get_size_dict(_A)
					if "height" not in size or "width" not in size:
						raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}")
					return resize(
					    _A			,						size=(size['height'], size['width'])			,						resample=_A			,						data_format=_A			,						**_A)



				def       _lowercase  (self       :   Optional[Any]			,						_A       :   np.ndarray			,						_A       :   Dict[str, int]			,						_A       :   Optional[Union[str, ChannelDimension]] = None			,						**_A       :   Tuple			,						)					->			np.ndarray:
					__snake_case    :							List[Any]   	=				get_size_dict(_A)
					if "height" not in size or "width" not in size:
						raise ValueError(f"The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}")
					return center_crop(_A			,						size=(size['height'], size['width'])			,						data_format=_A			,						**_A)



				def       _lowercase  (self       :   Dict			,						_A       :   np.ndarray			,						_A       :   Union[int, float]			,						_A       :   Optional[Union[str, ChannelDimension]] = None			,						**_A       :   Tuple			,						)					->			Tuple:
					return rescale(_A			,						scale=_A			,						data_format=_A			,						**_A)



				def       _lowercase  (self       :   List[Any]			,						_A       :   np.ndarray			,						_A       :   Union[float, List[float]]			,						_A       :   Union[float, List[float]]			,						_A       :   Optional[Union[str, ChannelDimension]] = None			,						**_A       :   Tuple			,						)					->			np.ndarray:
					return normalize(_A			,						mean=_A			,						std=_A			,						data_format=_A			,						**_A)






				def       _lowercase  (self       :   Dict			,						_A       :   ImageInput			,						_A       :   bool = None			,						_A       :   Dict[str, int] = None			,						_A       :   Optional[int]=None			,						_A       :   bool = None			,						_A       :   Dict[str, int] = None			,						_A       :   bool = None			,						_A       :   float = None			,						_A       :   bool = None			,						_A       :   Optional[Union[float, List[float]]] = None			,						_A       :   Optional[Union[float, List[float]]] = None			,						_A       :   Optional[Union[str, TensorType]] = None			,						_A       :   ChannelDimension = ChannelDimension.FIRST			,						**_A       :   int			,						)					->			PIL.Image.Image:
					__snake_case    :							Any   	=				do_resize if do_resize is not None else self.do_resize
					__snake_case    :							Optional[Any]   	=				resample if resample is not None else self.resample
					__snake_case    :							Optional[int]   	=				do_center_crop if do_center_crop is not None else self.do_center_crop
					__snake_case    :							Dict   	=				do_rescale if do_rescale is not None else self.do_rescale
					__snake_case    :							List[Any]   	=				rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case    :							Dict   	=				do_normalize if do_normalize is not None else self.do_normalize
					__snake_case    :							Optional[Any]   	=				image_mean if image_mean is not None else self.image_mean
					__snake_case    :							Union[str, Any]   	=				image_std if image_std is not None else self.image_std

					__snake_case    :							Union[str, Any]   	=				size if size is not None else self.size
					__snake_case    :							List[str]   	=				get_size_dict(_A)
					__snake_case    :							List[str]   	=				crop_size if crop_size is not None else self.crop_size
					__snake_case    :							List[Any]   	=				get_size_dict(_A			,						param_name='crop_size')

					__snake_case    :							int   	=				make_list_of_images(_A)

					if not valid_images(_A):
						raise ValueError(
						    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
						    'torch.Tensor, tf.Tensor or jax.ndarray.')

					if do_resize and size is None or resample is None:
						raise ValueError('Size and resample must be specified if do_resize is True.')

					if do_center_crop and crop_size is None:
						raise ValueError('Crop size must be specified if do_center_crop is True.')

					if do_rescale and rescale_factor is None:
						raise ValueError('Rescale factor must be specified if do_rescale is True.')

					if do_normalize and (image_mean is None or image_std is None):
						raise ValueError('Image mean and std must be specified if do_normalize is True.')

					# All transformations expect numpy arrays.
					__snake_case    :							Optional[Any]   	=				[to_numpy_array(_A) for image in images]

					if do_resize:
						__snake_case    :							List[str]   	=				[self.resize(image=_A			,						size=_A			,						resample=_A) for image in images]

					if do_center_crop:
						__snake_case    :							Optional[Any]   	=				[self.center_crop(image=_A			,						size=_A) for image in images]

					if do_rescale:
						__snake_case    :							Tuple   	=				[self.rescale(image=_A			,						scale=_A) for image in images]

					if do_normalize:
						__snake_case    :							str   	=				[self.normalize(image=_A			,						mean=_A			,						std=_A) for image in images]

					__snake_case    :							List[str]   	=				[to_channel_dimension_format(_A			,						_A) for image in images]

					__snake_case    :							List[Any]   	=				{"""pixel_values""": images}
					return BatchFeature(data=_A			,						tensor_type=_A)






"""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


_a  :  Optional[int]=				logging.get_logger(__name__)

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

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Dict					=	"""layoutlmv3"""



				def __init__(self       :   List[str]			,						_A       :   Optional[int]=5_02_65			,						_A       :   List[str]=7_68			,						_A       :   List[Any]=12			,						_A       :   List[str]=12			,						_A       :   Optional[int]=30_72			,						_A       :   str="gelu"			,						_A       :   int=0.1			,						_A       :   Tuple=0.1			,						_A       :   List[Any]=5_12			,						_A       :   List[str]=2			,						_A       :   List[Any]=0.02			,						_A       :   Tuple=1E-5			,						_A       :   Dict=1			,						_A       :   str=0			,						_A       :   str=2			,						_A       :   List[str]=10_24			,						_A       :   Optional[Any]=1_28			,						_A       :   Union[str, Any]=1_28			,						_A       :   Union[str, Any]=True			,						_A       :   Union[str, Any]=32			,						_A       :   Any=1_28			,						_A       :   Optional[Any]=64			,						_A       :   List[Any]=2_56			,						_A       :   str=True			,						_A       :   List[Any]=True			,						_A       :   Tuple=True			,						_A       :   Tuple=2_24			,						_A       :   Tuple=3			,						_A       :   Optional[Any]=16			,						_A       :   Tuple=None			,						**_A       :   Optional[int]			,						)					->			Optional[int]:
					super().__init__(
					    vocab_size=_A			,						hidden_size=_A			,						num_hidden_layers=_A			,						num_attention_heads=_A			,						intermediate_size=_A			,						hidden_act=_A			,						hidden_dropout_prob=_A			,						attention_probs_dropout_prob=_A			,						max_position_embeddings=_A			,						type_vocab_size=_A			,						initializer_range=_A			,						layer_norm_eps=_A			,						pad_token_id=_A			,						bos_token_id=_A			,						eos_token_id=_A			,						**_A			,						)
					__snake_case    :							List[Any]   	=				max_ad_position_embeddings
					__snake_case    :							List[str]   	=				coordinate_size
					__snake_case    :							int   	=				shape_size
					__snake_case    :							List[str]   	=				has_relative_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_pos_bins
					__snake_case    :							Tuple   	=				max_rel_pos
					__snake_case    :							Optional[Any]   	=				has_spatial_attention_bias
					__snake_case    :							Union[str, Any]   	=				rel_ad_pos_bins
					__snake_case    :							Any   	=				max_rel_ad_pos
					__snake_case    :							Tuple   	=				text_embed
					__snake_case    :							str   	=				visual_embed
					__snake_case    :							List[str]   	=				input_size
					__snake_case    :							List[Any]   	=				num_channels
					__snake_case    :							Union[str, Any]   	=				patch_size
					__snake_case    :							str   	=				classifier_dropout







class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Optional[int]					=	version.parse("""1.12"""		)



				@property
				def       _lowercase  (self       :   Optional[int])					->			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       _lowercase  (self       :   Tuple)					->			float:
					return 1E-5



				@property
				def       _lowercase  (self       :   Optional[int])					->			int:
					return 12






				def       _lowercase  (self       :   str			,						_A       :   "ProcessorMixin"			,						_A       :   int = -1			,						_A       :   int = -1			,						_A       :   bool = False			,						_A       :   Optional["TensorType"] = None			,						_A       :   int = 3			,						_A       :   int = 40			,						_A       :   int = 40			,						)					->			Mapping[str, Any]:
					setattr(processor.image_processor			,						'apply_ocr'			,						_A)

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

					# Generate dummy bounding boxes
					__snake_case    :							Dict   	=				[[[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    :							Optional[Any]   	=				self._generate_dummy_images(_A			,						_A			,						_A			,						_A)

					__snake_case    :							Dict   	=				dict(
					    processor(
					        _A			,						text=_A			,						boxes=_A			,						return_tensors=_A			,						))

					return inputs





