"""simple docstring"""



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		:		Tuple								=				logging.get_logger(__name__)



class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




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

         __snake_case				=				do_resize
         __snake_case				=				size
         __snake_case				=				resample
         __snake_case				=				do_rescale
         __snake_case				=				rescale_factor
         __snake_case				=				do_center_crop
         __snake_case				=				crop_size
         __snake_case				=				do_flip_channel_order
    def 			lowercase       (		self				:  Tuple						,   A_				:  int						,   A_				:  Optional[int]						,   A_				:  Optional[Any] = PIL.Image.BILINEAR						,   A_				:  Optional[int] = None						,   **A_				:  Optional[int]						,   )     ->							np.ndarray:
         __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()}"     )
         __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 			lowercase       (		self				:  Any						,   A_				:  Tuple						,   A_				:  int						,   A_				:  List[str] = None						,   **A_				:  Any						,   )     ->							np.ndarray:
         __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 			lowercase       (		self				:  List[Any]						,   A_				:  List[Any]						,   A_				:  Dict						,   A_				:  Union[str, Any] = None						,   **A_				:  Tuple						,   )     ->							Optional[Any]:
         return rescale(__SCREAMING_SNAKE_CASE						,   scale=__SCREAMING_SNAKE_CASE						,   data_format=__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  int						,   A_				:  List[str]						,   A_				:  Optional[int] = None     )     ->							np.ndarray:
         return flip_channel_order(__SCREAMING_SNAKE_CASE						,   data_format=__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Optional[int]						,   A_				:  Dict						,   A_				:  str = None						,   A_				:  Tuple = None						,   A_				:  Any = None						,   A_				:  Union[str, Any] = None						,   A_				:  Any = None						,   A_				:  Union[str, Any] = None						,   A_				:  List[Any] = None						,   A_				:  Any = None						,   A_				:  Optional[int] = None						,   A_				:  str = ChannelDimension.FIRST						,   **A_				:  Optional[int]						,   )     ->							PIL.Image.Image:
         __snake_case				=				do_resize if do_resize is not None else self.do_resize
         __snake_case				=				resample if resample is not None else self.resample
         __snake_case				=				do_rescale if do_rescale is not None else self.do_rescale
         __snake_case				=				rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case				=				do_center_crop if do_center_crop is not None else self.do_center_crop
         __snake_case				=				(
             do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
         )

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

         __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.
         __snake_case				=				[to_numpy_array(__SCREAMING_SNAKE_CASE     ) for image in images]

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

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

         if do_rescale:
              __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:
              __snake_case				=				[self.flip_channel_order(image=__SCREAMING_SNAKE_CASE     ) for image in images]

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

         __snake_case				=				{'''pixel_values''': images}
         return BatchFeature(data=__SCREAMING_SNAKE_CASE						,   tensor_type=__SCREAMING_SNAKE_CASE     )







    def 			lowercase       (		self				:  Any						,   A_				:  Optional[Any]						,   A_				:  Any = None     )     ->							Tuple:
         __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     ):
                   __snake_case				=				target_sizes.numpy()

              __snake_case				=				[]

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


