





from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
			import PIL

if is_torch_available():
			import torch


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     __UpperCAmelCase    ):
							lowerCamelCase_           =					['''pixel_values''']


							def __init__(							self    :Any							,				__A    :bool = True							,				__A    :Dict[str, int] = None							,				__A    :PILImageResampling = PILImageResampling.BILINEAR							,				__A    :bool = True							,				__A    :Union[int, float] = 1 / 255							,				__A    :bool = True							,				__A    :Dict[str, int] = None							,				__A    :bool = True							,				**__A    :List[Any]							,				)   ->   List[Any]:



									"""simple docstring"""



									super().__init__(**__SCREAMING_SNAKE_CASE		)
									SCREAMING_SNAKE_CASE__			=      size if size is not None else {"""shortest_edge""": 224}
									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE							,				default_to_square=__SCREAMING_SNAKE_CASE		)
									SCREAMING_SNAKE_CASE__			=      crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE							,				param_name="""crop_size"""		)

									SCREAMING_SNAKE_CASE__			=      do_resize
									SCREAMING_SNAKE_CASE__			=      size
									SCREAMING_SNAKE_CASE__			=      resample
									SCREAMING_SNAKE_CASE__			=      do_rescale
									SCREAMING_SNAKE_CASE__			=      rescale_factor
									SCREAMING_SNAKE_CASE__			=      do_center_crop
									SCREAMING_SNAKE_CASE__			=      crop_size
									SCREAMING_SNAKE_CASE__			=      do_flip_channel_order


							def      _snake_case  (							self    :str							,				__A    :np.ndarray							,				__A    :Dict[str, int]							,				__A    :PILImageResampling = PIL.Image.BILINEAR							,				__A    :Optional[Union[str, ChannelDimension]] = None							,				**__A    :Tuple							,				)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE							,				default_to_square=__SCREAMING_SNAKE_CASE		)
									if "shortest_edge" not in size:
											raise ValueError(f'''The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}'''		)
									SCREAMING_SNAKE_CASE__			=      get_resize_output_image_size(__SCREAMING_SNAKE_CASE							,				size=size["""shortest_edge"""]							,				default_to_square=__SCREAMING_SNAKE_CASE		)
									return resize(__SCREAMING_SNAKE_CASE							,				size=__SCREAMING_SNAKE_CASE							,				resample=__SCREAMING_SNAKE_CASE							,				data_format=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE		)


							def      _snake_case  (							self    :Dict							,				__A    :np.ndarray							,				__A    :Dict[str, int]							,				__A    :Optional[Union[str, ChannelDimension]] = None							,				**__A    :Any							,				)   ->   Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE		)
									if "height" not in size or "width" not in size:
											raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''		)
									return center_crop(__SCREAMING_SNAKE_CASE							,				size=(size["""height"""], size["""width"""])							,				data_format=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE		)


							def      _snake_case  (							self    :List[str]							,				__A    :np.ndarray							,				__A    :Union[int, float]							,				__A    :Optional[Union[str, ChannelDimension]] = None							,				**__A    :List[Any]							,				)   ->   Optional[int]:



									"""simple docstring"""



									return rescale(__SCREAMING_SNAKE_CASE							,				scale=__SCREAMING_SNAKE_CASE							,				data_format=__SCREAMING_SNAKE_CASE							,				**__SCREAMING_SNAKE_CASE		)


							def      _snake_case  (							self    :List[str]							,				__A    :np.ndarray							,				__A    :Optional[Union[str, ChannelDimension]] = None		)   ->   List[str]:



									"""simple docstring"""



									return flip_channel_order(__SCREAMING_SNAKE_CASE							,				data_format=__SCREAMING_SNAKE_CASE		)


							def      _snake_case  (							self    :int							,				__A    :ImageInput							,				__A    :bool = None							,				__A    :Dict[str, int] = None							,				__A    :PILImageResampling = None							,				__A    :bool = None							,				__A    :float = None							,				__A    :bool = None							,				__A    :Dict[str, int] = None							,				__A    :bool = None							,				__A    :Optional[Union[str, TensorType]] = None							,				__A    :ChannelDimension = ChannelDimension.FIRST							,				**__A    :Dict							,				)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__			=      resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__			=      do_rescale if do_rescale is not None else self.do_rescale
									SCREAMING_SNAKE_CASE__			=      rescale_factor if rescale_factor is not None else self.rescale_factor
									SCREAMING_SNAKE_CASE__			=      do_center_crop if do_center_crop is not None else self.do_center_crop
									SCREAMING_SNAKE_CASE__			=      (
									    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
									)

									SCREAMING_SNAKE_CASE__			=      size if size is not None else self.size
									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE							,				default_to_square=__SCREAMING_SNAKE_CASE		)
									SCREAMING_SNAKE_CASE__			=      crop_size if crop_size is not None else self.crop_size
									SCREAMING_SNAKE_CASE__			=      get_size_dict(__SCREAMING_SNAKE_CASE							,				param_name="""crop_size"""		)

									SCREAMING_SNAKE_CASE__			=      make_list_of_images(__SCREAMING_SNAKE_CASE		)

									if not valid_images(__SCREAMING_SNAKE_CASE		):
											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:
											raise ValueError("""Size must be specified if do_resize is True."""		)

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

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

									# All transformations expect numpy arrays.
									SCREAMING_SNAKE_CASE__			=      [to_numpy_array(__SCREAMING_SNAKE_CASE		) for image in images]

									if do_resize:
											SCREAMING_SNAKE_CASE__			=      [self.resize(image=__SCREAMING_SNAKE_CASE							,				size=__SCREAMING_SNAKE_CASE							,				resample=__SCREAMING_SNAKE_CASE		) for image in images]

									if do_center_crop:
											SCREAMING_SNAKE_CASE__			=      [self.center_crop(image=__SCREAMING_SNAKE_CASE							,				size=__SCREAMING_SNAKE_CASE		) for image in images]

									if do_rescale:
											SCREAMING_SNAKE_CASE__			=      [self.rescale(image=__SCREAMING_SNAKE_CASE							,				scale=__SCREAMING_SNAKE_CASE		) for image in images]

									# the pretrained checkpoints assume images are BGR, not RGB
									if do_flip_channel_order:
											SCREAMING_SNAKE_CASE__			=      [self.flip_channel_order(image=__SCREAMING_SNAKE_CASE		) for image in images]

									SCREAMING_SNAKE_CASE__			=      [to_channel_dimension_format(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE		) for image in images]

									SCREAMING_SNAKE_CASE__			=      {"""pixel_values""": images}
									return BatchFeature(data=__SCREAMING_SNAKE_CASE							,				tensor_type=__SCREAMING_SNAKE_CASE		)


							def      _snake_case  (							self    :Optional[Any]							,				__A    :Dict							,				__A    :List[Tuple] = None		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      outputs.logits

									# Resize logits and compute semantic segmentation maps
									if target_sizes is not None:
											if len(__SCREAMING_SNAKE_CASE		) != len(__SCREAMING_SNAKE_CASE		):
													raise ValueError(
													    """Make sure that you pass in as many target sizes as the batch dimension of the logits"""		)

											if is_torch_tensor(__SCREAMING_SNAKE_CASE		):
													SCREAMING_SNAKE_CASE__			=      target_sizes.numpy()

											SCREAMING_SNAKE_CASE__			=      []

											for idx in range(len(__SCREAMING_SNAKE_CASE		)		):
													SCREAMING_SNAKE_CASE__			=      torch.nn.functional.interpolate(
													    logits[idx].unsqueeze(dim=0		)							,				size=target_sizes[idx]							,				mode="""bilinear"""							,				align_corners=__SCREAMING_SNAKE_CASE		)
													SCREAMING_SNAKE_CASE__			=      resized_logits[0].argmax(dim=0		)
													semantic_segmentation.append(__SCREAMING_SNAKE_CASE		)
									else:
											SCREAMING_SNAKE_CASE__			=      logits.argmax(dim=1		)
											SCREAMING_SNAKE_CASE__			=      [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]		)]

									return semantic_segmentation






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase   				=			{
    'configuration_layoutlmv3': [
        'LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'LayoutLMv3Config',
        'LayoutLMv3OnnxConfig',
    ],
    'processing_layoutlmv3': ['LayoutLMv3Processor'],
    'tokenization_layoutlmv3': ['LayoutLMv3Tokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3TokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'LayoutLMv3ForQuestionAnswering',
	    'LayoutLMv3ForSequenceClassification',
	    'LayoutLMv3ForTokenClassification',
	    'LayoutLMv3Model',
	    'LayoutLMv3PreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			[
	    'TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFLayoutLMv3ForQuestionAnswering',
	    'TFLayoutLMv3ForSequenceClassification',
	    'TFLayoutLMv3ForTokenClassification',
	    'TFLayoutLMv3Model',
	    'TFLayoutLMv3PreTrainedModel',
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase   				=			['LayoutLMv3FeatureExtractor']
	_lowerCamelCase   				=			['LayoutLMv3ImageProcessor']


if TYPE_CHECKING:
	from .configuration_layoutlmva import (
	    LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    LayoutLMvaConfig,
	    LayoutLMvaOnnxConfig,
	)
	from .processing_layoutlmva import LayoutLMvaProcessor
	from .tokenization_layoutlmva import LayoutLMvaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_layoutlmva import (
		    LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		    LayoutLMvaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_layoutlmva import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		    TFLayoutLMvaPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
		from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
	import sys

	_lowerCamelCase   				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)