
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,
    get_resize_output_image_size,
    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_torch_available, is_torch_tensor, logging


if is_torch_available():
    import torch


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 1 / 2_5_5    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           super().__init__(**__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : str											=					size if size is not None else {'''shortest_edge''': 2_5_6}
           UpperCamelCase__     : Optional[int]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : List[Any]											=					crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
           UpperCamelCase__     : Optional[Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)
           UpperCamelCase__     : List[Any]											=					do_resize
           UpperCamelCase__     : Dict											=					size
           UpperCamelCase__     : Union[str, Any]											=					resample
           UpperCamelCase__     : Dict											=					do_center_crop
           UpperCamelCase__     : Tuple											=					crop_size
           UpperCamelCase__     : Union[str, Any]											=					do_rescale
           UpperCamelCase__     : Dict											=					rescale_factor
           UpperCamelCase__     : int											=					do_normalize
           UpperCamelCase__     : str											=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           UpperCamelCase__     : List[Any]											=					image_std if image_std is not None else IMAGENET_STANDARD_STD






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__     : str											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           if "shortest_edge" not in size:
                 raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
           UpperCamelCase__     : Optional[int]											=					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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__     : int											=					get_size_dict(__SCREAMING_SNAKE_CASE	)
           if "height" not in size or "width" not in size:
                 raise ValueError(F'''The `size` parameter 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  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







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






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> np.ndarray:




           """simple docstring"""







           return normalize(__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> Dict:




           """simple docstring"""







           UpperCamelCase__     : Any											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : str											=					size if size is not None else self.size
           UpperCamelCase__     : Tuple											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    default_to_square=__SCREAMING_SNAKE_CASE	)
           UpperCamelCase__     : Union[str, Any]											=					resample if resample is not None else self.resample
           UpperCamelCase__     : List[str]											=					do_center_crop if do_center_crop is not None else self.do_center_crop
           UpperCamelCase__     : Optional[int]											=					crop_size if crop_size is not None else self.crop_size
           UpperCamelCase__     : List[Any]											=					get_size_dict(__SCREAMING_SNAKE_CASE    ,    param_name='''crop_size'''	)
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Optional[Any]											=					rescale_factor if rescale_factor is not None else self.rescale_factor
           UpperCamelCase__     : Optional[Any]											=					do_normalize if do_normalize is not None else self.do_normalize
           UpperCamelCase__     : Tuple											=					image_mean if image_mean is not None else self.image_mean
           UpperCamelCase__     : Optional[Any]											=					image_std if image_std is not None else self.image_std

           UpperCamelCase__     : int											=					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_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.
           UpperCamelCase__     : Tuple											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for image in images]

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

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

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

           if do_normalize:
                 UpperCamelCase__     : Tuple											=					[self.normalize(image=__SCREAMING_SNAKE_CASE    ,    mean=__SCREAMING_SNAKE_CASE    ,    std=__SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : int											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : List[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None	)	-> Tuple:




           """simple docstring"""







           UpperCamelCase__     : List[Any]											=					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	):
                       UpperCamelCase__     : Any											=					target_sizes.numpy()

                 UpperCamelCase__     : List[Any]											=					[]

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


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    lowerCamelCase  =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


