





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_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__:    str  	=	logging.get_logger(__name__)




class        _a    (    SCREAMING_SNAKE_CASE_):

      """simple docstring"""


      UpperCamelCase__  		=       ["""pixel_values"""]






      def __init__(		self:			Tuple	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			float = None	,    __lowerCamelCase:			PILImageResampling = PILImageResampling.BILINEAR	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Union[int, float] = 1 / 255	,    __lowerCamelCase:			bool = True	,    __lowerCamelCase:			Optional[Union[float, List[float]]] = None	,    __lowerCamelCase:			Optional[Union[float, List[float]]] = None	,    **__lowerCamelCase:			Optional[int]	,    ):
          '''simple docstring'''


          super().__init__(**_a					)
          UpperCamelCase__:	Any					=     size if size is not None else {'shortest_edge': 384}
          UpperCamelCase__:	Union[str, Any]					=     get_size_dict(_a	,    default_to_square=_a					)

          UpperCamelCase__:	Optional[int]					=     do_resize
          UpperCamelCase__:	Optional[Any]					=     size
          # Default value set here for backwards compatibility where the value in config is None
          UpperCamelCase__:	Union[str, Any]					=     crop_pct if crop_pct is not None else 224 / 256
          UpperCamelCase__:	Tuple					=     resample
          UpperCamelCase__:	str					=     do_rescale
          UpperCamelCase__:	Optional[Any]					=     rescale_factor
          UpperCamelCase__:	Optional[Any]					=     do_normalize
          UpperCamelCase__:	Optional[int]					=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase__:	int					=     image_std if image_std is not None else IMAGENET_STANDARD_STD






      def 			UpperCAmelCase_       (		self:			int	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Dict[str, int]	,    __lowerCamelCase:			float	,    __lowerCamelCase:			PILImageResampling = PILImageResampling.BICUBIC	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			List[Any]	,    ):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     get_size_dict(_a	,    default_to_square=_a					)
          if "shortest_edge" not in size:
              raise ValueError(F"Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}"					)
          UpperCamelCase__:	Any					=     size['shortest_edge']

          if shortest_edge < 384:
              # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
              UpperCamelCase__:	List[str]					=     int(shortest_edge / crop_pct					)
              UpperCamelCase__:	Optional[int]					=     get_resize_output_image_size(_a	,    size=_a	,    default_to_square=_a					)
              UpperCamelCase__:	Any					=     resize(image=_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a					)
              # then crop to (shortest_edge, shortest_edge)
              return center_crop(image=_a	,    size=(shortest_edge, shortest_edge)	,    data_format=_a	,    **_a					)
          else:
              # warping (no cropping) when evaluated at 384 or larger
              return resize(
                  _a	,    size=(shortest_edge, shortest_edge)	,    resample=_a	,    data_format=_a	,    **_a					)






      def 			UpperCAmelCase_       (		self:			Optional[Any]	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Union[int, float]	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			List[str]	,    ):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			np.ndarray	,    __lowerCamelCase:			Union[float, List[float]]	,    __lowerCamelCase:			Union[float, List[float]]	,    __lowerCamelCase:			Optional[Union[str, ChannelDimension]] = None	,    **__lowerCamelCase:			int	,    ):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			ImageInput	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			Dict[str, int] = None	,    __lowerCamelCase:			float = None	,    __lowerCamelCase:			PILImageResampling = None	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			float = None	,    __lowerCamelCase:			bool = None	,    __lowerCamelCase:			Optional[Union[float, List[float]]] = None	,    __lowerCamelCase:			Optional[Union[float, List[float]]] = None	,    __lowerCamelCase:			Optional[Union[str, TensorType]] = None	,    __lowerCamelCase:			ChannelDimension = ChannelDimension.FIRST	,    **__lowerCamelCase:			Any	,    ):
          '''simple docstring'''


          UpperCamelCase__:	int					=     do_resize if do_resize is not None else self.do_resize
          UpperCamelCase__:	Tuple					=     crop_pct if crop_pct is not None else self.crop_pct
          UpperCamelCase__:	Union[str, Any]					=     resample if resample is not None else self.resample
          UpperCamelCase__:	Any					=     do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase__:	str					=     rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase__:	int					=     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__:	int					=     image_std if image_std is not None else self.image_std

          UpperCamelCase__:	List[Any]					=     size if size is not None else self.size
          UpperCamelCase__:	Any					=     get_size_dict(_a	,    default_to_square=_a					)

          UpperCamelCase__:	str					=     make_list_of_images(_a					)

          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 or resample is None:
              raise ValueError("Size and resample must be specified if do_resize is True."					)

          if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
              raise ValueError("crop_pct must be specified if size < 384."					)

          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.
          UpperCamelCase__:	str					=     [to_numpy_array(_a					) for image in images]

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

          if do_rescale:
              UpperCamelCase__:	str					=     [self.rescale(image=_a	,    scale=_a					) for image in images]

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

          UpperCamelCase__:	int					=     [to_channel_dimension_format(_a	,    _a					) for image in images]

          UpperCamelCase__:	int					=     {'pixel_values': images}
          return BatchFeature(data=_a	,    tensor_type=_a					)






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCamelCase											=		{'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=		[
       'IBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'IBertForMaskedLM',
       'IBertForMultipleChoice',
       'IBertForQuestionAnswering',
       'IBertForSequenceClassification',
       'IBertForTokenClassification',
       'IBertModel',
       'IBertPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

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






