
"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE      =     logging.get_logger(__name__)


if is_vision_available():
      import PIL



class   __a  (      _lowerCAmelCase	):
       UpperCamelCase_		:					str   =			['''pixel_values''']






       def __init__(  self :							Tuple       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = PILImageResampling.BICUBIC       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Union[int, float] = 1 / 255       ,      UpperCAmelCase_ :							bool = True       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							bool = True       ,      **UpperCAmelCase_ :							int       ,      )->  None:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_			)
              UpperCamelCase					 =		size if size is not None else {"shortest_edge": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_			)
              UpperCamelCase					 =		crop_size if crop_size is not None else {"height": 224, "width": 224}
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_       ,      param_name="crop_size"			)

              UpperCamelCase					 =		do_resize
              UpperCamelCase					 =		size
              UpperCamelCase					 =		resample
              UpperCamelCase					 =		do_center_crop
              UpperCamelCase					 =		crop_size
              UpperCamelCase					 =		do_rescale
              UpperCamelCase					 =		rescale_factor
              UpperCamelCase					 =		do_normalize
              UpperCamelCase					 =		image_mean if image_mean is not None else OPENAI_CLIP_MEAN
              UpperCamelCase					 =		image_std if image_std is not None else OPENAI_CLIP_STD
              UpperCamelCase					 =		do_convert_rgb






       def 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Dict[str, int]       ,      UpperCAmelCase_ :							PILImageResampling = PILImageResampling.BICUBIC       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							Tuple       ,      )->  np.ndarray:







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      default_to_square=UpperCAmelCase_			)
              if "shortest_edge" not in size:
                     raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
              UpperCamelCase					 =		get_resize_output_image_size(UpperCAmelCase_       ,      size=size["shortest_edge"]       ,      default_to_square=UpperCAmelCase_			)
              return resize(UpperCAmelCase_       ,      size=UpperCAmelCase_       ,      resample=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_			)
              if "height" not in size or "width" not in size:
                     raise ValueError(f"The `size` parameter must contain the keys (height, width). Got {size.keys()}"			)
              return center_crop(UpperCAmelCase_       ,      size=(size["height"], size["width"])       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							np.ndarray       ,      UpperCAmelCase_ :							Union[int, float]       ,      UpperCAmelCase_ :							Optional[Union[str, ChannelDimension]] = None       ,      **UpperCAmelCase_ :							str       ,      )->  Tuple:







              """simple docstring"""
              return rescale(UpperCAmelCase_       ,      scale=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






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







              """simple docstring"""
              return normalize(UpperCAmelCase_       ,      mean=UpperCAmelCase_       ,      std=UpperCAmelCase_       ,      data_format=UpperCAmelCase_       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							ImageInput       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Dict[str, int] = None       ,      UpperCAmelCase_ :							PILImageResampling = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							int = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							float = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							Optional[Union[float, List[float]]] = None       ,      UpperCAmelCase_ :							bool = None       ,      UpperCAmelCase_ :							Optional[Union[str, TensorType]] = None       ,      UpperCAmelCase_ :							Optional[ChannelDimension] = ChannelDimension.FIRST       ,      **UpperCAmelCase_ :							Optional[int]       ,      )->  PIL.Image.Image:







              """simple docstring"""
              UpperCamelCase					 =		do_resize if do_resize is not None else self.do_resize
              UpperCamelCase					 =		size if size is not None else self.size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="size"       ,      default_to_square=UpperCAmelCase_			)
              UpperCamelCase					 =		resample if resample is not None else self.resample
              UpperCamelCase					 =		do_center_crop if do_center_crop is not None else self.do_center_crop
              UpperCamelCase					 =		crop_size if crop_size is not None else self.crop_size
              UpperCamelCase					 =		get_size_dict(UpperCAmelCase_       ,      param_name="crop_size"       ,      default_to_square=UpperCAmelCase_			)
              UpperCamelCase					 =		do_rescale if do_rescale is not None else self.do_rescale
              UpperCamelCase					 =		rescale_factor if rescale_factor is not None else self.rescale_factor
              UpperCamelCase					 =		do_normalize if do_normalize is not None else self.do_normalize
              UpperCamelCase					 =		image_mean if image_mean is not None else self.image_mean
              UpperCamelCase					 =		image_std if image_std is not None else self.image_std
              UpperCamelCase					 =		do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

              UpperCamelCase					 =		make_list_of_images(UpperCAmelCase_			)

              if not valid_images(UpperCAmelCase_			):
                     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."			)

              # PIL RGBA images are converted to RGB
              if do_convert_rgb:
                     UpperCamelCase					 =		[convert_to_rgb(UpperCAmelCase_			) for image in images]

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

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

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

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

              if do_normalize:
                     UpperCamelCase					 =		[self.normalize(image=UpperCAmelCase_       ,      mean=UpperCAmelCase_       ,      std=UpperCAmelCase_			) for image in images]

              UpperCamelCase					 =		[to_channel_dimension_format(UpperCAmelCase_       ,      UpperCAmelCase_			) for image in images]

              UpperCamelCase					 =		{"pixel_values": images}
              return BatchFeature(data=UpperCAmelCase_       ,      tensor_type=UpperCAmelCase_			)







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

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





