



'''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,
    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											=		logging.get_logger(__name__)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""pixel_values"""]




  def __init__(					self :      Any     ,      _a :      bool = True     ,      _a :      Optional[Dict[str, int]] = None     ,      _a :      PILImageResampling = PILImageResampling.BICUBIC     ,      _a :      bool = True     ,      _a :      bool = True     ,      _a :      Union[int, float] = 1 / 255     ,      _a :      Dict[str, int] = None     ,      _a :      bool = True     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      **_a :      Dict     ,      )   ->   None:
    super().__init__(**_a  )
    __lowerCamelCase			:			List[str]       			=					size if size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase			:			Any       			=					get_size_dict(_a  )
    __lowerCamelCase			:			Optional[int]       			=					crop_size if crop_size is not None else {'height': 224, 'width': 224}
    __lowerCamelCase			:			Dict       			=					get_size_dict(_a     ,      default_to_square=_a     ,      param_name='crop_size'  )

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




  def 		_lowercase	(					self :      int     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      PILImageResampling = PILImageResampling.BILINEAR     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      List[str]     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			int       			=					get_size_dict(_a  )

    if "shortest_edge" in size:
      __lowerCamelCase			:			str       			=					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:
      __lowerCamelCase			:			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 		_lowercase	(					self :      Tuple     ,      _a :      np.ndarray     ,      _a :      Dict[str, int]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Dict     ,      )   ->   np.ndarray:
    __lowerCamelCase			:			Dict       			=					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 		_lowercase	(					self :      Optional[int]     ,      _a :      np.ndarray     ,      _a :      float     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Dict  )   ->   np.ndarray:
    return rescale(_a     ,      scale=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      str     ,      _a :      np.ndarray     ,      _a :      Union[float, List[float]]     ,      _a :      Union[float, List[float]]     ,      _a :      Optional[Union[str, ChannelDimension]] = None     ,      **_a :      Union[str, Any]     ,      )   ->   np.ndarray:
    return normalize(_a     ,      mean=_a     ,      std=_a     ,      data_format=_a     ,      **_a  )




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      ImageInput     ,      _a :      Optional[bool] = None     ,      _a :      Dict[str, int] = None     ,      _a :      PILImageResampling = None     ,      _a :      bool = None     ,      _a :      int = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[float] = None     ,      _a :      Optional[bool] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[float, List[float]]] = None     ,      _a :      Optional[Union[str, TensorType]] = None     ,      _a :      Union[str, ChannelDimension] = ChannelDimension.FIRST     ,      **_a :      List[Any]     ,      )   ->   BatchFeature:
    __lowerCamelCase			:			Any       			=					do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase			:			List[str]       			=					do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase			:			Union[str, Any]       			=					do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase			:			str       			=					do_center_crop if do_center_crop is not None else self.do_center_crop
    __lowerCamelCase			:			Tuple       			=					crop_size if crop_size is not None else self.crop_size
    __lowerCamelCase			:			List[str]       			=					get_size_dict(_a     ,      param_name='crop_size'     ,      default_to_square=_a  )
    __lowerCamelCase			:			int       			=					resample if resample is not None else self.resample
    __lowerCamelCase			:			Any       			=					rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase			:			Dict       			=					image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase			:			Dict       			=					image_std if image_std is not None else self.image_std

    __lowerCamelCase			:			Dict       			=					size if size is not None else self.size
    __lowerCamelCase			:			str       			=					get_size_dict(_a  )

    if not is_batched(_a  ):
      __lowerCamelCase			:			List[str]       			=					[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.
    __lowerCamelCase			:			str       			=					[to_numpy_array(_a  ) for image in images]

    if do_resize:
      __lowerCamelCase			:			int       			=					[self.resize(image=_a     ,      size=_a     ,      resample=_a  ) for image in images]

    if do_center_crop:
      __lowerCamelCase			:			Union[str, Any]       			=					[self.center_crop(image=_a     ,      size=_a  ) for image in images]

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

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

    __lowerCamelCase			:			List[str]       			=					[to_channel_dimension_format(_a     ,      _a  ) for image in images]

    __lowerCamelCase			:			Optional[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__)






