




"""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 					lowercase(     __lowerCamelCase ):







 '''simple docstring'''





 lowercase__ 					=					["pixel_values"]


 def __init__(				self:		str,      a_:		bool = True,      a_:		Dict[str, int] = None,      a_:		int = 0.9,      a_:		PILImageResampling = PILImageResampling.BICUBIC,      a_:		bool = True,      a_:		Dict[str, int] = None,      a_:		Union[int, float] = 1 / 255,      a_:		bool = True,      a_:		bool = True,      a_:		Optional[Union[float, List[float]]] = None,      a_:		Optional[Union[float, List[float]]] = None,      **a_:		Tuple,      ):




   '''simple docstring'''



   super().__init__(**a_		)
   _snake_case							:		Optional[Any]								=  size if size is not None else {"""shortest_edge""": 224}
   _snake_case							:		str								=  get_size_dict(a_,      default_to_square=a_		)
   _snake_case							:		List[Any]								=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
   _snake_case							:		Dict								=  get_size_dict(a_,      param_name="""crop_size"""		)

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


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




   '''simple docstring'''



   _snake_case							:		int								=  get_size_dict(a_,      default_to_square=a_		)
   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:
       _snake_case							:		Optional[Any]								=  int(size["""shortest_edge"""] / crop_pct		)
     elif "height" in size and "width" in size:
       if size["height"] == size["width"]:
         _snake_case							:		List[str]								=  int(size["""height"""] / crop_pct		)
       else:
         _snake_case							:		Dict								=  (int(size["""height"""] / crop_pct		), int(size["""width"""] / crop_pct		))
     else:
       raise ValueError("""Invalid size for resize: {}""".format(a_		)		)

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

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


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		np.ndarray,      a_:		Dict[str, int],      a_:		Optional[Union[str, ChannelDimension]] = None,      **a_:		Optional[Any],      ):




   '''simple docstring'''



   _snake_case							:		Union[str, Any]								=  get_size_dict(a_		)
   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(a_,      size=(size["""height"""], size["""width"""]),      data_format=a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		np.ndarray,      a_:		Union[int, float],      a_:		Optional[Union[str, ChannelDimension]] = None,      **a_:		int,      ):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Tuple,      a_:		np.ndarray,      a_:		Union[float, List[float]],      a_:		Union[float, List[float]],      a_:		Optional[Union[str, ChannelDimension]] = None,      **a_:		int,      ):




   '''simple docstring'''



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


 def  UpperCamelCase_   (				self:		Dict,      a_:		ImageInput,      a_:		bool = None,      a_:		Dict[str, int] = None,      a_:		int = None,      a_:		PILImageResampling = None,      a_:		bool = None,      a_:		Dict[str, int] = None,      a_:		bool = None,      a_:		float = None,      a_:		bool = None,      a_:		Optional[Union[float, List[float]]] = None,      a_:		Optional[Union[float, List[float]]] = None,      a_:		Optional[Union[str, TensorType]] = None,      a_:		ChannelDimension = ChannelDimension.FIRST,      **a_:		int,      ):




   '''simple docstring'''



   _snake_case							:		List[str]								=  do_resize if do_resize is not None else self.do_resize
   _snake_case							:		Tuple								=  crop_pct if crop_pct is not None else self.crop_pct
   _snake_case							:		int								=  resample if resample is not None else self.resample
   _snake_case							:		int								=  do_center_crop if do_center_crop is not None else self.do_center_crop
   _snake_case							:		Optional[Any]								=  do_rescale if do_rescale is not None else self.do_rescale
   _snake_case							:		Optional[int]								=  rescale_factor if rescale_factor is not None else self.rescale_factor
   _snake_case							:		int								=  do_normalize if do_normalize is not None else self.do_normalize
   _snake_case							:		Any								=  image_mean if image_mean is not None else self.image_mean
   _snake_case							:		Optional[Any]								=  image_std if image_std is not None else self.image_std

   _snake_case							:		int								=  size if size is not None else self.size
   _snake_case							:		List[str]								=  get_size_dict(a_,      default_to_square=a_		)
   _snake_case							:		Optional[int]								=  crop_size if crop_size is not None else self.crop_size
   _snake_case							:		Optional[int]								=  get_size_dict(a_,      param_name="""crop_size"""		)

   _snake_case							:		Tuple								=  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_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.
   _snake_case							:		List[str]								=  [to_numpy_array(a_		) for image in images]

   if do_resize:
     _snake_case							:		List[str]								=  [self.resize(image=a_,      size=a_,      crop_pct=a_,      resample=a_		) for image in images]

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

   if do_rescale:
     _snake_case							:		Union[str, Any]								=  [self.rescale(image=a_,      scale=a_		) for image in images]

   if do_normalize:
     _snake_case							:		Union[str, Any]								=  [self.normalize(image=a_,      mean=a_,      std=a_		) for image in images]

   _snake_case							:		Dict								=  [to_channel_dimension_format(a_,      a_		) for image in images]

   _snake_case							:		List[Any]								=  {"""pixel_values""": images}
   return BatchFeature(data=a_,      tensor_type=a_		)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase       :   Union[str, Any]				       =							{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase       :   str				       =							["BartphoTokenizer"]

if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bartpho import BartphoTokenizer

else:
      import sys

      __UpperCAmelCase       :   Optional[Any]				       =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






