

from typing import TYPE_CHECKING

from ...utils import _LazyModule


lowerCAmelCase       :				Tuple         =					{'tokenization_phobert': ['PhobertTokenizer']}


if TYPE_CHECKING:
    from .tokenization_phobert import PhobertTokenizer

else:
    import sys

    lowerCAmelCase       :				Optional[int]         =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)









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


lowerCAmelCase       :				Dict         =					logging.get_logger(__name__)
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : Any    =					['''pixel_values''']

  def __init__(			self	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 1 / 255	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = True	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        super().__init__(**_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					str          =       size if size is not None else {'height': 346, 'width': 346}
        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					int          =       crop_size if crop_size is not None else {'height': 289, 'width': 289}
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

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

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = PIL.Image.NEAREST	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					str          =       get_size_dict(_SCREAMING_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(
            _SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   resample=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_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(_SCREAMING_SNAKE_CASE	,   size=(size['height'], size['width'])	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        if offset:
              SCREAMING_SNAKE_CASE_			:					Dict          =       (image - 127.5) * scale
              if data_format is not None:
                    SCREAMING_SNAKE_CASE_			:					Tuple          =       to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   )
              SCREAMING_SNAKE_CASE_			:					int          =       rescaled_image.astype(np.floataa   )
        else:
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       rescale(_SCREAMING_SNAKE_CASE	,   scale=_SCREAMING_SNAKE_CASE	,   data_format=_SCREAMING_SNAKE_CASE	,   **_SCREAMING_SNAKE_CASE   )
        return rescaled_image

  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







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



  def 	UpperCAmelCase      (			self	,   _SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE=None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					List[Any]          =       do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       do_center_crop if do_center_crop is not None else self.do_center_crop
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE_			:					int          =       rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE_			:					Tuple          =       rescale_offset if rescale_offset is not None else self.rescale_offset
        SCREAMING_SNAKE_CASE_			:					Dict          =       do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE_			:					str          =       image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       include_top if include_top is not None else self.include_top

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       size if size is not None else self.size
        SCREAMING_SNAKE_CASE_			:					Tuple          =       get_size_dict(_SCREAMING_SNAKE_CASE   )
        SCREAMING_SNAKE_CASE_			:					Any          =       crop_size if crop_size is not None else self.crop_size
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       get_size_dict(_SCREAMING_SNAKE_CASE	,   param_name='crop_size'   )

        SCREAMING_SNAKE_CASE_			:					List[str]          =       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 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.
        SCREAMING_SNAKE_CASE_			:					int          =       [to_numpy_array(_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.resize(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE	,   resample=_SCREAMING_SNAKE_CASE   ) for image in images]

        if do_center_crop:
              SCREAMING_SNAKE_CASE_			:					str          =       [self.center_crop(image=_SCREAMING_SNAKE_CASE	,   size=_SCREAMING_SNAKE_CASE   ) for image in images]

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

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

        if include_top:
              SCREAMING_SNAKE_CASE_			:					Dict          =       [self.normalize(image=_SCREAMING_SNAKE_CASE	,   mean=[0, 0, 0]	,   std=_SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Dict          =       [to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ) for image in images]

        SCREAMING_SNAKE_CASE_			:					Any          =       {'pixel_values': images}
        return BatchFeature(data=_SCREAMING_SNAKE_CASE	,   tensor_type=_SCREAMING_SNAKE_CASE   )






