





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


if is_vision_available():
    import PIL


a   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			List[Any]						   =						['pixel_values']





   def __init__(       self					:      Dict					,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					:      Union[str, Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							size if size is not None else {"""shortest_edge""": 224}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,default_to_square=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE   				=							do_resize
      __SCREAMING_SNAKE_CASE   				=							size
      __SCREAMING_SNAKE_CASE   				=							crop_pct
      __SCREAMING_SNAKE_CASE   				=							resample
      __SCREAMING_SNAKE_CASE   				=							do_center_crop
      __SCREAMING_SNAKE_CASE   				=							crop_size
      __SCREAMING_SNAKE_CASE   				=							do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_normalize
      __SCREAMING_SNAKE_CASE   				=							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
      __SCREAMING_SNAKE_CASE   				=							image_std if image_std is not None else IMAGENET_DEFAULT_STD





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      Optional[float] = None					,lowerCamelCase					:      PILImageResampling = PILImageResampling.BICUBIC					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      Any					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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:
            __SCREAMING_SNAKE_CASE   				=							int(size["""shortest_edge"""] / crop_pct     )
         elif "height" in size and "width" in size:
            if size["height"] == size["width"]:
               __SCREAMING_SNAKE_CASE   				=							int(size["""height"""] / crop_pct     )
            else:
               __SCREAMING_SNAKE_CASE   				=							(int(size["""height"""] / crop_pct     ), int(size["""width"""] / crop_pct     ))
         else:
            raise ValueError("""Invalid size for resize: {}""".format(lowerCamelCase     )     )

         __SCREAMING_SNAKE_CASE   				=							get_resize_output_image_size(lowerCamelCase					,size=lowerCamelCase					,default_to_square=lowerCamelCase     )
      else:
         if "shortest_edge" in size:
            __SCREAMING_SNAKE_CASE   				=							get_resize_output_image_size(lowerCamelCase					,size=size["""shortest_edge"""]					,default_to_square=lowerCamelCase     )
         elif "height" in size and "width" in size:
            __SCREAMING_SNAKE_CASE   				=							(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					:      Optional[int]					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Dict[str, int]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      int					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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					:      Any					,lowerCamelCase					:      np.ndarray					,lowerCamelCase					:      Union[int, float]					,lowerCamelCase					:      Optional[Union[str, ChannelDimension]] = None					,**lowerCamelCase					:      List[str]					,):

      '''simple docstring'''

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





   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(lowerCamelCase					,mean=lowerCamelCase					,std=lowerCamelCase					,data_format=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,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					:      Optional[int]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							do_resize if do_resize is not None else self.do_resize
      __SCREAMING_SNAKE_CASE   				=							crop_pct if crop_pct is not None else self.crop_pct
      __SCREAMING_SNAKE_CASE   				=							resample if resample is not None else self.resample
      __SCREAMING_SNAKE_CASE   				=							do_center_crop if do_center_crop is not None else self.do_center_crop
      __SCREAMING_SNAKE_CASE   				=							do_rescale if do_rescale is not None else self.do_rescale
      __SCREAMING_SNAKE_CASE   				=							rescale_factor if rescale_factor is not None else self.rescale_factor
      __SCREAMING_SNAKE_CASE   				=							do_normalize if do_normalize is not None else self.do_normalize
      __SCREAMING_SNAKE_CASE   				=							image_mean if image_mean is not None else self.image_mean
      __SCREAMING_SNAKE_CASE   				=							image_std if image_std is not None else self.image_std

      __SCREAMING_SNAKE_CASE   				=							size if size is not None else self.size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,default_to_square=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							crop_size if crop_size is not None else self.crop_size
      __SCREAMING_SNAKE_CASE   				=							get_size_dict(lowerCamelCase					,param_name="""crop_size"""     )

      __SCREAMING_SNAKE_CASE   				=							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.
      __SCREAMING_SNAKE_CASE   				=							[to_numpy_array(lowerCamelCase     ) for image in images]

      if do_resize:
         __SCREAMING_SNAKE_CASE   				=							[self.resize(image=lowerCamelCase					,size=lowerCamelCase					,crop_pct=lowerCamelCase					,resample=lowerCamelCase     ) for image in images]

      if do_center_crop:
         __SCREAMING_SNAKE_CASE   				=							[self.center_crop(image=lowerCamelCase					,size=lowerCamelCase     ) for image in images]

      if do_rescale:
         __SCREAMING_SNAKE_CASE   				=							[self.rescale(image=lowerCamelCase					,scale=lowerCamelCase     ) for image in images]

      if do_normalize:
         __SCREAMING_SNAKE_CASE   				=							[self.normalize(image=lowerCamelCase					,mean=lowerCamelCase					,std=lowerCamelCase     ) for image in images]

      __SCREAMING_SNAKE_CASE   				=							[to_channel_dimension_format(lowerCamelCase					,lowerCamelCase     ) for image in images]

      __SCREAMING_SNAKE_CASE   				=							{"""pixel_values""": images}
      return BatchFeature(data=lowerCamelCase					,tensor_type=lowerCamelCase     )












'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-12      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


