


'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
 import PIL

lowercase				=logging.get_logger(__name__)





def lowerCamelCase__     (		__lowerCamelCase			:				Dict				):







   '''simple docstring'''




   if isinstance(__lowerCamelCase			,       (list, tuple)				) and isinstance(videos[0]			,       (list, tuple)				) and is_valid_image(videos[0][0]				):
      return videos

   elif isinstance(__lowerCamelCase			,       (list, tuple)				) and is_valid_image(videos[0]				):
      return [videos]

   elif is_valid_image(__lowerCamelCase				):
      return [[videos]]

   raise ValueError(f"Could not make batched video from {videos}"				)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["pixel_values"]






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




          '''simple docstring'''
          super().__init__(**snake_case)
          _UpperCAmelCase  :  Dict				       =size if size is not None else {'shortest_edge': 2_5_6}
          _UpperCAmelCase  :  int				       =get_size_dict(snake_case    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  str				       =crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
          _UpperCAmelCase  :  str				       =get_size_dict(snake_case    ,				param_name='crop_size')

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






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = PILImageResampling.BILINEAR    ,				snake_case = None    ,				**snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =get_size_dict(snake_case    ,				default_to_square=snake_case)
          if "shortest_edge" in size:
             _UpperCAmelCase  :  Optional[Any]				       =get_resize_output_image_size(snake_case    ,				size['shortest_edge']    ,				default_to_square=snake_case)
          elif "height" in size and "width" in size:
             _UpperCAmelCase  :  Union[str, Any]				       =(size['height'], size['width'])
          else:
             raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}")
          return resize(snake_case    ,				size=snake_case    ,				resample=snake_case    ,				data_format=snake_case    ,				**snake_case)






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




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =get_size_dict(snake_case)
          if "height" not in size or "width" not in size:
             raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}")
          return center_crop(snake_case    ,				size=(size['height'], size['width'])    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case    ,				snake_case = True    ,				snake_case = None    ,				**snake_case    ,				)		->    Union[str, Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       =image.astype(np.floataa)
          if offset:
             _UpperCAmelCase  :  Any				       =image - (scale / 2)
          return rescale(snake_case    ,				scale=snake_case    ,				data_format=snake_case    ,				**snake_case)






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




          '''simple docstring'''
          return normalize(snake_case    ,				mean=snake_case    ,				std=snake_case    ,				data_format=snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = ChannelDimension.FIRST    ,				)		->    np.ndarray:




          '''simple docstring'''
          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.')

          if offset and not do_rescale:
             raise ValueError('For offset, do_rescale must also be set to True.')

          # All transformations expect numpy arrays.
          _UpperCAmelCase  :  List[str]				       =to_numpy_array(snake_case)

          if do_resize:
             _UpperCAmelCase  :  Optional[int]				       =self.resize(image=snake_case    ,				size=snake_case    ,				resample=snake_case)

          if do_center_crop:
             _UpperCAmelCase  :  Optional[Any]				       =self.center_crop(snake_case    ,				size=snake_case)

          if do_rescale:
             _UpperCAmelCase  :  Tuple				       =self.rescale(image=snake_case    ,				scale=snake_case    ,				offset=snake_case)

          if do_normalize:
             _UpperCAmelCase  :  Optional[int]				       =self.normalize(image=snake_case    ,				mean=snake_case    ,				std=snake_case)

          _UpperCAmelCase  :  List[Any]				       =to_channel_dimension_format(snake_case    ,				snake_case)
          return image






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = ChannelDimension.FIRST    ,				**snake_case    ,				)		->    PIL.Image.Image:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase  :  int				       =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  :  List[str]				       =do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase  :  List[Any]				       =rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase  :  str				       =offset if offset is not None else self.offset
          _UpperCAmelCase  :  Any				       =do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase  :  Union[str, Any]				       =image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase  :  Tuple				       =image_std if image_std is not None else self.image_std

          _UpperCAmelCase  :  Union[str, Any]				       =size if size is not None else self.size
          _UpperCAmelCase  :  List[Any]				       =get_size_dict(snake_case    ,				default_to_square=snake_case)
          _UpperCAmelCase  :  Any				       =crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase  :  Dict				       =get_size_dict(snake_case    ,				param_name='crop_size')

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

          _UpperCAmelCase  :  Optional[Any]				       =make_batched(snake_case)

          _UpperCAmelCase  :  Optional[Any]				       =[
              [
                  self._preprocess_image(
                      image=snake_case    ,				do_resize=snake_case    ,				size=snake_case    ,				resample=snake_case    ,				do_center_crop=snake_case    ,				crop_size=snake_case    ,				do_rescale=snake_case    ,				rescale_factor=snake_case    ,				offset=snake_case    ,				do_normalize=snake_case    ,				image_mean=snake_case    ,				image_std=snake_case    ,				data_format=snake_case    ,				)
                  for img in video
              ]
              for video in videos
          ]

          _UpperCAmelCase  :  Optional[Any]				       ={'pixel_values': videos}
          return BatchFeature(data=snake_case    ,				tensor_type=snake_case)





'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase				={
    'configuration_table_transformer': [
        'TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TableTransformerConfig',
        'TableTransformerOnnxConfig',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase				=[
     'TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TableTransformerForObjectDetection',
     'TableTransformerModel',
     'TableTransformerPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_table_transformer import (
     TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     TableTransformerConfig,
     TableTransformerOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_table_transformer import (
      TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TableTransformerForObjectDetection,
      TableTransformerModel,
      TableTransformerPreTrainedModel,
  )

else:
 import sys

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

