

"""simple docstring"""






import math

import tensorflow as tf
from packaging import version





def 	lowercase      (   __UpperCamelCase						) ->      Union[str, Any]:
 __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)
 __magic_name__     =      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0						)      ,   x.dtype						)						))

 return x * cdf





def 	lowercase      (   __UpperCamelCase						) ->      Any:
 __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)
 __magic_name__     =      tf.cast(math.pi      ,   x.dtype						)
 __magic_name__     =      tf.cast(0.044_715      ,   x.dtype						)
 __magic_name__     =      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi						) * (x + coeff * tf.pow(__UpperCamelCase      ,   3						))						))

 return x * cdf





def 	lowercase      (   __UpperCamelCase						) ->      Optional[Any]:
 __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)

 return x * tf.tanh(tf.math.softplus(__UpperCamelCase						)						)





def 	lowercase      (   __UpperCamelCase						) ->      List[Any]:
 __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)
 __magic_name__     =      tf.cast(0.044_715      ,   x.dtype						)
 __magic_name__     =      tf.cast(0.7_978_845_608      ,   x.dtype						)

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)						))





def 	lowercase      (   __UpperCamelCase						) ->      List[Any]:
 __magic_name__     =      tf.convert_to_tensor(__UpperCamelCase						)
 __magic_name__     =      tf.cast(1.702      ,   x.dtype						)
 return x * tf.math.sigmoid(coeff * x						)





def 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:
 return tf.clip_by_value(_gelu(__UpperCamelCase						)      ,   -10      ,   10						)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase=-1						) ->      List[str]:
 __magic_name__   ,						__magic_name__     =      tf.split(__UpperCamelCase      ,   2      ,   axis=__UpperCamelCase						)
 return a * tf.math.sigmoid(__UpperCamelCase						)


if version.parse(tf.version.VERSION) >= version.parse("2.4"):





 def 	lowercase      (   __UpperCamelCase						) ->      Optional[int]:
  return tf.keras.activations.gelu(__UpperCamelCase      ,   approximate=__UpperCamelCase						)

 __lowerCamelCase										=		tf.keras.activations.gelu
 __lowerCamelCase										=		approximate_gelu_wrap
else:
 __lowerCamelCase										=		_gelu
 __lowerCamelCase										=		_gelu_new


__lowerCamelCase										=		{
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}





def 	lowercase      (   __UpperCamelCase						) ->      str:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}'''						)




"""simple docstring"""






from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowerCamelCase										=		logging.get_logger(__name__)
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       ['''pixel_values''']







     def __init__(      self ,						UpperCamelCase_ = True ,						UpperCamelCase_ = 32 ,						UpperCamelCase_=PILImageResampling.BILINEAR ,						UpperCamelCase_ = True ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize
      __magic_name__     =      do_rescale
      __magic_name__     =      size_divisor
      __magic_name__     =      resample
      super().__init__(**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      __magic_name__   ,						__magic_name__     =      get_image_size(UpperCamelCase_       )
      # Rounds the height and width down to the closest multiple of size_divisor
      __magic_name__     =      height // size_divisor * size_divisor
      __magic_name__     =      width // size_divisor * size_divisor
      __magic_name__     =      resize(UpperCamelCase_ ,						(new_h, new_w) ,						resample=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )
      return image







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						**UpperCamelCase_       ):
      return rescale(image=UpperCamelCase_ ,						scale=UpperCamelCase_ ,						data_format=UpperCamelCase_ ,						**UpperCamelCase_       )







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_=None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = None ,						UpperCamelCase_ = ChannelDimension.FIRST ,						**UpperCamelCase_ ,						):
      __magic_name__     =      do_resize if do_resize is not None else self.do_resize
      __magic_name__     =      do_rescale if do_rescale is not None else self.do_rescale
      __magic_name__     =      size_divisor if size_divisor is not None else self.size_divisor
      __magic_name__     =      resample if resample is not None else self.resample

      if do_resize and size_divisor is None:
       raise ValueError('''size_divisor is required for resizing'''       )

      __magic_name__     =      make_list_of_images(UpperCamelCase_       )

      if not valid_images(UpperCamelCase_       ):
       raise ValueError('''Invalid image(s)'''       )

      # All transformations expect numpy arrays.
      __magic_name__     =      [to_numpy_array(UpperCamelCase_       ) for img in images]

      if do_resize:
       __magic_name__     =      [self.resize(UpperCamelCase_ ,						size_divisor=UpperCamelCase_ ,						resample=UpperCamelCase_       ) for image in images]

      if do_rescale:
       __magic_name__     =      [self.rescale(UpperCamelCase_ ,						scale=1 / 255       ) for image in images]

      __magic_name__     =      [to_channel_dimension_format(UpperCamelCase_ ,						UpperCamelCase_       ) for image in images]

      __magic_name__     =      {'''pixel_values''': images}
      return BatchFeature(data=UpperCamelCase_ ,						tensor_type=UpperCamelCase_       )

