
"""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, 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_vision_available, logging


if is_vision_available():
    import PIL


_a   :      Any       =				logging.get_logger(__name__)






class   _UpperCAmelCase					(       _snake_case):
    __lowercase				:			Any	=       ["""pixel_values"""]
    def __init__(     self    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = False    ,     snake_case_ = None    ,     snake_case_ = 1 / 2_55    ,     snake_case_ = False    ,     snake_case_ = True    ,     snake_case_ = True    ,     snake_case_ = None    ,     snake_case_ = None    ,     snake_case_ = True    ,     **snake_case_    ,     ):
      super().__init__(**snake_case_  )
      _snake_case  :    Tuple            = size if size is not None else {"height": 3_46, "width": 3_46}
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    List[str]            = crop_size if crop_size is not None else {"height": 2_89, "width": 2_89}
      _snake_case  :    List[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    Any            = do_resize
      _snake_case  :    Optional[int]            = size
      _snake_case  :    List[str]            = resample
      _snake_case  :    Union[str, Any]            = do_center_crop
      _snake_case  :    str            = crop_size
      _snake_case  :    str            = do_rescale
      _snake_case  :    Tuple            = rescale_factor
      _snake_case  :    Optional[Any]            = rescale_offset
      _snake_case  :    str            = do_normalize
      _snake_case  :    Dict            = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _snake_case  :    Optional[Any]            = image_std if image_std is not None else IMAGENET_STANDARD_STD
      _snake_case  :    Dict            = include_top
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = PIL.Image.NEAREST    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'  )
      return resize(
          snake_case_    ,     size=(size["height"], size["width"])    ,     resample=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      _snake_case  :    int            = get_size_dict(snake_case_  )
      if "height" not in size or "width" not in size:
        raise ValueError(F'The size dictionary must have keys \'height\' and \'width\'. 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_    ,     ):
      if offset:
        _snake_case  :    Optional[int]            = (image - 127.5) * scale
        if data_format is not None:
          _snake_case  :    Union[str, Any]            = to_channel_dimension_format(snake_case_    ,     snake_case_  )
        _snake_case  :    Tuple            = rescaled_image.astype(np.floataa  )
      else:
        _snake_case  :    Optional[int]            = rescale(snake_case_    ,     scale=snake_case_    ,     data_format=snake_case_    ,     **snake_case_  )
      return rescaled_image
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_ = None    ,     **snake_case_    ,     ):
      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_ = None    ,     snake_case_ = None    ,     snake_case_ = ChannelDimension.FIRST    ,     **snake_case_    ,     ):
      _snake_case  :    Dict            = do_resize if do_resize is not None else self.do_resize
      _snake_case  :    Tuple            = resample if resample is not None else self.resample
      _snake_case  :    Any            = do_center_crop if do_center_crop is not None else self.do_center_crop
      _snake_case  :    str            = do_rescale if do_rescale is not None else self.do_rescale
      _snake_case  :    int            = rescale_factor if rescale_factor is not None else self.rescale_factor
      _snake_case  :    Any            = rescale_offset if rescale_offset is not None else self.rescale_offset
      _snake_case  :    Tuple            = do_normalize if do_normalize is not None else self.do_normalize
      _snake_case  :    str            = image_mean if image_mean is not None else self.image_mean
      _snake_case  :    Any            = image_std if image_std is not None else self.image_std
      _snake_case  :    Any            = include_top if include_top is not None else self.include_top

      _snake_case  :    List[str]            = size if size is not None else self.size
      _snake_case  :    Any            = get_size_dict(snake_case_  )
      _snake_case  :    str            = crop_size if crop_size is not None else self.crop_size
      _snake_case  :    Optional[Any]            = get_size_dict(snake_case_    ,     param_name="crop_size"  )

      _snake_case  :    List[str]            = make_list_of_images(snake_case_  )

      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."  )

      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."  )

      # All transformations expect numpy arrays.
      _snake_case  :    str            = [to_numpy_array(snake_case_  ) for image in images]

      if do_resize:
        _snake_case  :    List[str]            = [self.resize(image=snake_case_    ,     size=snake_case_    ,     resample=snake_case_  ) for image in images]

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

      if do_rescale:
        _snake_case  :    Tuple            = [self.rescale(image=snake_case_    ,     scale=snake_case_    ,     offset=snake_case_  ) for image in images]

      if do_normalize:
        _snake_case  :    str            = [self.normalize(image=snake_case_    ,     mean=snake_case_    ,     std=snake_case_  ) for image in images]

      if include_top:
        _snake_case  :    Optional[int]            = [self.normalize(image=snake_case_    ,     mean=[0, 0, 0]    ,     std=snake_case_  ) for image in images]

      _snake_case  :    Union[str, Any]            = [to_channel_dimension_format(snake_case_    ,     snake_case_  ) for image in images]

      _snake_case  :    Any            = {"pixel_values": images}
      return BatchFeature(data=snake_case_    ,     tensor_type=snake_case_  )





from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims




