


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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCamelCase       :       str						       =				logging.get_logger(__name__)

class       __magic_name__	(     __lowercase):
     A:		str			=      ['pixel_values']


     def __init__(      self							:     Optional[Any]							,	lowerCamelCase__							:     str = True							,	lowerCamelCase__							:     Union[str, Any] = None							,	lowerCamelCase__							:     Any = PILImageResampling.BICUBIC							,	lowerCamelCase__							:     Tuple = True							,	lowerCamelCase__							:     Union[str, Any] = True							,	lowerCamelCase__							:     Tuple = 1 / 255							,	lowerCamelCase__							:     Union[str, Any] = None							,	lowerCamelCase__							:     str = True							,	lowerCamelCase__							:     Any = None							,	lowerCamelCase__							:     Tuple = None							,	**lowerCamelCase__							:     str							,	)  ->      None:





      '''simple docstring'''






      super().__init__(**__A			)
      UpperCamelCase__					:    List[str]          =   size if size is not None else {'''height''': 224, '''width''': 224}
      UpperCamelCase__					:    List[Any]          =   get_size_dict(__A			)
      UpperCamelCase__					:    Tuple          =   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
      UpperCamelCase__					:    List[Any]          =   get_size_dict(__A							,	default_to_square=__A							,	param_name='''crop_size'''			)

      UpperCamelCase__					:    List[str]          =   do_resize
      UpperCamelCase__					:    Optional[Any]          =   do_rescale
      UpperCamelCase__					:    Optional[int]          =   do_normalize
      UpperCamelCase__					:    Any          =   do_center_crop
      UpperCamelCase__					:    List[str]          =   crop_size
      UpperCamelCase__					:    Tuple          =   size
      UpperCamelCase__					:    Tuple          =   resample
      UpperCamelCase__					:    Optional[int]          =   rescale_factor
      UpperCamelCase__					:    Optional[int]          =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      UpperCamelCase__					:    Any          =   image_std if image_std is not None else IMAGENET_DEFAULT_STD


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     List[str] = PILImageResampling.BILINEAR							,	lowerCamelCase__							:     Dict = None							,	**lowerCamelCase__							:     Dict							,	)  ->      np.ndarray:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   get_size_dict(__A			)

      if "shortest_edge" in size:
       UpperCamelCase__					:    Optional[int]          =   get_resize_output_image_size(__A							,	size=size['''shortest_edge''']							,	default_to_square=__A			)
       # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
      elif "height" in size and "width" in size:
       UpperCamelCase__					:    int          =   (size['''height'''], size['''width'''])
      else:
       raise ValueError(F"Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"			)
      return resize(__A							,	size=__A							,	resample=__A							,	data_format=__A							,	**__A			)


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Optional[int] = None							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      np.ndarray:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   get_size_dict(__A			)
      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(__A							,	size=(size['''height'''], size['''width'''])							,	data_format=__A							,	**__A			)


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     Union[str, Any] = None							,	**lowerCamelCase__							:     Optional[Any]			)  ->      np.ndarray:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     List[Any] = None							,	**lowerCamelCase__							:     Optional[Any]							,	)  ->      np.ndarray:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     int = None							,	lowerCamelCase__							:     int = None							,	lowerCamelCase__							:     Any = None							,	lowerCamelCase__							:     Tuple = None							,	lowerCamelCase__							:     List[Any] = None							,	lowerCamelCase__							:     str = None							,	lowerCamelCase__							:     Tuple = None							,	lowerCamelCase__							:     Dict = None							,	lowerCamelCase__							:     Optional[Any] = None							,	lowerCamelCase__							:     Union[str, Any] = None							,	lowerCamelCase__							:     int = None							,	lowerCamelCase__							:     Tuple = ChannelDimension.FIRST							,	**lowerCamelCase__							:     List[str]							,	)  ->      BatchFeature:





      '''simple docstring'''






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

      if not is_batched(__A			):
       UpperCamelCase__					:    Optional[Any]          =   [images]

      if not valid_images(__A			):
       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.'''			)

      # All transformations expect numpy arrays.
      UpperCamelCase__					:    List[str]          =   [to_numpy_array(__A			) for image in images]

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

      if do_center_crop:
       UpperCamelCase__					:    List[str]          =   [self.center_crop(image=__A							,	size=__A			) for image in images]

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

      if do_normalize:
       UpperCamelCase__					:    Optional[Any]          =   [self.normalize(image=__A							,	mean=__A							,	std=__A			) for image in images]

      UpperCamelCase__					:    Dict          =   [to_channel_dimension_format(__A							,	__A			) for image in images]

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






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__UpperCamelCase       :       int						       =				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       __UpperCamelCase       :       List[str]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


