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


__lowercase										= logging.get_logger(__name__)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Dict      =				["""pixel_values"""]







    def __init__(     self			,			__lowercase = True			,			__lowercase = None			,			__lowercase = PILImageResampling.BILINEAR			,			__lowercase = True			,			__lowercase = 1 / 255			,			__lowercase = True			,			__lowercase = None			,			__lowercase = True			,			**__lowercase			,			)	->     None:
        super().__init__(**__lowercase)
        __UpperCamelCase					:Optional[int]        =		size if size is not None else {'''shortest_edge''': 224}
        __UpperCamelCase					:str        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        __UpperCamelCase					:str        =		crop_size if crop_size is not None else {'''height''': 256, '''width''': 256}
        __UpperCamelCase					:Union[str, Any]        =		get_size_dict(__lowercase			,			param_name='''crop_size''')

        __UpperCamelCase					:Union[str, Any]        =		do_resize
        __UpperCamelCase					:str        =		size
        __UpperCamelCase					:List[Any]        =		resample
        __UpperCamelCase					:Union[str, Any]        =		do_rescale
        __UpperCamelCase					:List[Any]        =		rescale_factor
        __UpperCamelCase					:Optional[Any]        =		do_center_crop
        __UpperCamelCase					:Optional[int]        =		crop_size
        __UpperCamelCase					:int        =		do_flip_channel_order







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = PIL.Image.BILINEAR			,			__lowercase = None			,			**__lowercase			,			)	->     np.ndarray:
        __UpperCamelCase					:Dict        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        if "shortest_edge" not in size:
            raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}""")
        __UpperCamelCase					:List[str]        =		get_resize_output_image_size(__lowercase			,			size=size['''shortest_edge''']			,			default_to_square=__lowercase)
        return resize(__lowercase			,			size=__lowercase			,			resample=__lowercase			,			data_format=__lowercase			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     np.ndarray:
        __UpperCamelCase					:int        =		get_size_dict(__lowercase)
        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(__lowercase			,			size=(size['''height'''], size['''width'''])			,			data_format=__lowercase			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase			,			__lowercase = None			,			**__lowercase			,			)	->     List[str]:
        return rescale(__lowercase			,			scale=__lowercase			,			data_format=__lowercase			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     np.ndarray:
        return flip_channel_order(__lowercase			,			data_format=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = None			,			__lowercase = ChannelDimension.FIRST			,			**__lowercase			,			)	->     PIL.Image.Image:
        __UpperCamelCase					:Any        =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase					:str        =		resample if resample is not None else self.resample
        __UpperCamelCase					:List[str]        =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase					:Optional[int]        =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase					:Union[str, Any]        =		do_center_crop if do_center_crop is not None else self.do_center_crop
        __UpperCamelCase					:Union[str, Any]        =		(
            do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
        )

        __UpperCamelCase					:List[Any]        =		size if size is not None else self.size
        __UpperCamelCase					:Any        =		get_size_dict(__lowercase			,			default_to_square=__lowercase)
        __UpperCamelCase					:int        =		crop_size if crop_size is not None else self.crop_size
        __UpperCamelCase					:Optional[Any]        =		get_size_dict(__lowercase			,			param_name='''crop_size''')

        __UpperCamelCase					:Union[str, Any]        =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            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.
        __UpperCamelCase					:Union[str, Any]        =		[to_numpy_array(__lowercase) for image in images]

        if do_resize:
            __UpperCamelCase					:Optional[int]        =		[self.resize(image=__lowercase			,			size=__lowercase			,			resample=__lowercase) for image in images]

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

        if do_rescale:
            __UpperCamelCase					:List[Any]        =		[self.rescale(image=__lowercase			,			scale=__lowercase) for image in images]

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

        __UpperCamelCase					:int        =		[to_channel_dimension_format(__lowercase			,			__lowercase) for image in images]

        __UpperCamelCase					:Any        =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase			,			tensor_type=__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = None)	->     str:
        __UpperCamelCase					:Optional[int]        =		outputs.logits

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

            if is_torch_tensor(__lowercase):
                __UpperCamelCase					:int        =		target_sizes.numpy()

            __UpperCamelCase					:Any        =		[]

            for idx in range(len(__lowercase)):
                __UpperCamelCase					:List[Any]        =		torch.nn.functional.interpolate(
                    logits[idx].unsqueeze(dim=0)			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=__lowercase)
                __UpperCamelCase					:Optional[Any]        =		resized_logits[0].argmax(dim=0)
                semantic_segmentation.append(__lowercase)
        else:
            __UpperCamelCase					:Optional[Any]        =		logits.argmax(dim=1)
            __UpperCamelCase					:List[str]        =		[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,
)


__lowercase										= {
    '''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:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

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

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

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

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





