


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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
    import PIL


UpperCamelCase					:       int			    =     logging.get_logger(__name__)



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		['pixel_values']

  def __init__( self			:  int				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Dict[str, int] = None				,				lowerCamelCase__			:  int = 0.9				,				lowerCamelCase__			:  PILImageResampling = PILImageResampling.BICUBIC				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Dict[str, int] = None				,				lowerCamelCase__			:  Union[int, float] = 1 / 255				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Optional[Union[float, List[float]]] = None				,				lowerCamelCase__			:  Optional[Union[float, List[float]]] = None				,				**lowerCamelCase__			:  Any				,				):
        super().__init__(**lowerCamelCase__       )
        a__	:  str     =   size if size is not None else {"shortest_edge": 224}
        a__	:  str     =   get_size_dict(lowerCamelCase__				,				default_to_square=lowerCamelCase__       )
        a__	:  Dict     =   crop_size if crop_size is not None else {"height": 224, "width": 224}
        a__	:  List[Any]     =   get_size_dict(lowerCamelCase__				,				param_name="crop_size"       )

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

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  Dict[str, int]				,				lowerCamelCase__			:  Optional[float] = None				,				lowerCamelCase__			:  PILImageResampling = PILImageResampling.BICUBIC				,				lowerCamelCase__			:  Optional[Union[str, ChannelDimension]] = None				,				**lowerCamelCase__			:  str				,				):
        a__	:  Optional[int]     =   get_size_dict(lowerCamelCase__				,				default_to_square=lowerCamelCase__       )
        if "shortest_edge" not in size and ("height" not in size or "width" not in size):
              raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''       )
        if crop_pct is not None:
              if "shortest_edge" in size:
                    a__	:  Dict     =   int(size["shortest_edge"] / crop_pct       )
              elif "height" in size and "width" in size:
                    if size["height"] == size["width"]:
                          a__	:  Optional[Any]     =   int(size["height"] / crop_pct       )
                    else:
                          a__	:  List[str]     =   (int(size["height"] / crop_pct       ), int(size["width"] / crop_pct       ))
              else:
                    raise ValueError("Invalid size for resize: {}".format(lowerCamelCase__       )       )

              a__	:  Tuple     =   get_resize_output_image_size(lowerCamelCase__				,				size=lowerCamelCase__				,				default_to_square=lowerCamelCase__       )
        else:
              if "shortest_edge" in size:
                    a__	:  int     =   get_resize_output_image_size(lowerCamelCase__				,				size=size["shortest_edge"]				,				default_to_square=lowerCamelCase__       )
              elif "height" in size and "width" in size:
                    a__	:  Optional[int]     =   (size["height"], size["width"])
              else:
                    raise ValueError("Invalid size for resize: {}".format(lowerCamelCase__       )       )

        return resize(lowerCamelCase__				,				size=lowerCamelCase__				,				resample=lowerCamelCase__				,				data_format=lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  List[str]				,				lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  Dict[str, int]				,				lowerCamelCase__			:  Optional[Union[str, ChannelDimension]] = None				,				**lowerCamelCase__			:  Any				,				):
        a__	:  List[Any]     =   get_size_dict(lowerCamelCase__       )
        if "height" not in size or "width" not in size:
              raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''       )
        return center_crop(lowerCamelCase__				,				size=(size["height"], size["width"])				,				data_format=lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  Union[int, float]				,				lowerCamelCase__			:  Optional[Union[str, ChannelDimension]] = None				,				**lowerCamelCase__			:  Tuple				,				):
        return rescale(lowerCamelCase__				,				scale=lowerCamelCase__				,				data_format=lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  Union[float, List[float]]				,				lowerCamelCase__			:  Union[float, List[float]]				,				lowerCamelCase__			:  Optional[Union[str, ChannelDimension]] = None				,				**lowerCamelCase__			:  Any				,				):
        return normalize(lowerCamelCase__				,				mean=lowerCamelCase__				,				std=lowerCamelCase__				,				data_format=lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  ImageInput				,				lowerCamelCase__			:  bool = None				,				lowerCamelCase__			:  Dict[str, int] = None				,				lowerCamelCase__			:  int = None				,				lowerCamelCase__			:  PILImageResampling = None				,				lowerCamelCase__			:  bool = None				,				lowerCamelCase__			:  Dict[str, int] = 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__			:  str				,				):
        a__	:  Union[str, Any]     =   do_resize if do_resize is not None else self.do_resize
        a__	:  Optional[Any]     =   crop_pct if crop_pct is not None else self.crop_pct
        a__	:  int     =   resample if resample is not None else self.resample
        a__	:  Any     =   do_center_crop if do_center_crop is not None else self.do_center_crop
        a__	:  int     =   do_rescale if do_rescale is not None else self.do_rescale
        a__	:  List[str]     =   rescale_factor if rescale_factor is not None else self.rescale_factor
        a__	:  int     =   do_normalize if do_normalize is not None else self.do_normalize
        a__	:  Union[str, Any]     =   image_mean if image_mean is not None else self.image_mean
        a__	:  List[Any]     =   image_std if image_std is not None else self.image_std

        a__	:  Tuple     =   size if size is not None else self.size
        a__	:  Union[str, Any]     =   get_size_dict(lowerCamelCase__				,				default_to_square=lowerCamelCase__       )
        a__	:  Optional[int]     =   crop_size if crop_size is not None else self.crop_size
        a__	:  List[str]     =   get_size_dict(lowerCamelCase__				,				param_name="crop_size"       )

        a__	:  Union[str, Any]     =   make_list_of_images(lowerCamelCase__       )

        if not valid_images(lowerCamelCase__       ):
              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_pct is None:
              raise ValueError("Crop_pct 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.
        a__	:  Optional[int]     =   [to_numpy_array(lowerCamelCase__       ) for image in images]

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

        if do_center_crop:
              a__	:  Tuple     =   [self.center_crop(image=lowerCamelCase__				,				size=lowerCamelCase__       ) for image in images]

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

        if do_normalize:
              a__	:  Optional[int]     =   [self.normalize(image=lowerCamelCase__				,				mean=lowerCamelCase__				,				std=lowerCamelCase__       ) for image in images]

        a__	:  Tuple     =   [to_channel_dimension_format(lowerCamelCase__				,				lowerCamelCase__       ) for image in images]

        a__	:  Optional[Any]     =   {"pixel_values": images}
        return BatchFeature(data=lowerCamelCase__				,				tensor_type=lowerCamelCase__       )





import math

import tensorflow as tf
from packaging import version






def 	__lowerCamelCase						(							lowerCamelCase__		:						Any      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0      )  ,							x.dtype      )      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[str]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(math.pi  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi      ) * (x + coeff * tf.pow(lowerCamelCase__  ,							3      ))      ))

			return x * cdf






def 	__lowerCamelCase						(							lowerCamelCase__		:						int      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )

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






def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(0.0_4_4_7_1_5  ,							x.dtype      )
			lowerCamelCase    		=  tf.cast(0.7_9_7_8_8_4_5_6_0_8  ,							x.dtype      )

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






def 	__lowerCamelCase						(							lowerCamelCase__		:						Optional[int]      ):





			'''simple docstring'''




			lowerCamelCase    		=  tf.convert_to_tensor(lowerCamelCase__      )
			lowerCamelCase    		=  tf.cast(1.7_0_2  ,							x.dtype      )
			return x * tf.math.sigmoid(coeff * x      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						Dict      ):





			'''simple docstring'''




			return tf.clip_by_value(_gelu(lowerCamelCase__      )  ,							-10  ,							10      )






def 	__lowerCamelCase						(							lowerCamelCase__		:						int  ,							lowerCamelCase__		:						Dict=-1      ):





			'''simple docstring'''




			lowerCamelCase		,		lowerCamelCase    		=  tf.split(lowerCamelCase__  ,							2  ,							axis=lowerCamelCase__      )
			return a * tf.math.sigmoid(lowerCamelCase__      )


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






		def 	__lowerCamelCase						(							lowerCamelCase__		:						Union[str, Any]      ):





					'''simple docstring'''




					return tf.keras.activations.gelu(lowerCamelCase__  ,							approximate=lowerCamelCase__      )

		UpperCAmelCase   :    Union[str, Any]     =		tf.keras.activations.gelu



		UpperCAmelCase   :    int     =		approximate_gelu_wrap
else:
		UpperCAmelCase   :    Dict     =		_gelu
		UpperCAmelCase   :    Dict     =		_gelu_new


UpperCAmelCase   :    List[str]     =		{
    "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 	__lowerCamelCase						(							lowerCamelCase__		:						Tuple      ):





			'''simple docstring'''




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

