





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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_snake_case				= logging.get_logger(__name__)





class 					lowerCAmelCase							(   lowercase_					):
  __lowerCamelCase		=			['pixel_values']







  def __init__(			self  :List[Any]    ,							_lowercase  :bool = True    ,							_lowercase  :Dict[str, int] = None    ,							_lowercase  :bool = True    ,							_lowercase  :Dict[str, int] = None    ,							_lowercase  :PILImageResampling = PILImageResampling.BICUBIC    ,							_lowercase  :bool = True    ,							_lowercase  :Union[int, float] = 1 / 2_55    ,							_lowercase  :bool = True    ,							_lowercase  :Optional[Union[float, List[float]]] = None    ,							_lowercase  :Optional[Union[float, List[float]]] = None    ,							**_lowercase  :List[str]    ,							):






     '''simple docstring'''





     super().__init__(**_lowercase					)
     lowercase__    =  crop_size if crop_size is not None else {"height": 2_56, "width": 2_56}
     lowercase__    =  get_size_dict(_lowercase    ,							param_name="crop_size"					)
     lowercase__    =  size if size is not None else {"height": 2_24, "width": 2_24}
     lowercase__    =  get_size_dict(_lowercase					)

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







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






     '''simple docstring'''





     lowercase__    =  self.size if size is None else size
     lowercase__    =  get_size_dict(_lowercase					)
     lowercase__    =  get_size_dict(_lowercase    ,							param_name="crop_size"					)

     lowercase__   ,							lowercase__    =  get_image_size(_lowercase					)
     lowercase__    =  min(_lowercase    ,							_lowercase					)
     lowercase__    =  (size["height"] / crop_size["height"]) * min_dim
     lowercase__    =  (size["width"] / crop_size["width"]) * min_dim
     return center_crop(_lowercase    ,							size=(cropped_height, cropped_width)    ,							data_format=_lowercase    ,							**_lowercase					)







  def   UpperCAmelCase     (			self  :int    ,							_lowercase  :np.ndarray    ,							_lowercase  :Dict[str, int]    ,							_lowercase  :PILImageResampling = PIL.Image.BICUBIC    ,							_lowercase  :Optional[Union[str, ChannelDimension]] = None    ,							**_lowercase  :str    ,							):






     '''simple docstring'''





     lowercase__    =  get_size_dict(_lowercase					)
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''					)
     return resize(
         _lowercase    ,							size=(size["height"], size["width"])    ,							resample=_lowercase    ,							data_format=_lowercase    ,							**_lowercase					)







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






     '''simple docstring'''





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







  def   UpperCAmelCase     (			self  :Dict    ,							_lowercase  :np.ndarray    ,							_lowercase  :Union[float, List[float]]    ,							_lowercase  :Union[float, List[float]]    ,							_lowercase  :Optional[Union[str, ChannelDimension]] = None    ,							**_lowercase  :Union[str, Any]    ,							):






     '''simple docstring'''





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







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






     '''simple docstring'''





     lowercase__    =  do_center_crop if do_center_crop is not None else self.do_center_crop
     lowercase__    =  crop_size if crop_size is not None else self.crop_size
     lowercase__    =  get_size_dict(_lowercase    ,							param_name="crop_size"					)
     lowercase__    =  do_resize if do_resize is not None else self.do_resize
     lowercase__    =  size if size is not None else self.size
     lowercase__    =  get_size_dict(_lowercase					)
     lowercase__    =  resample if resample is not None else self.resample
     lowercase__    =  do_rescale if do_rescale is not None else self.do_rescale
     lowercase__    =  rescale_factor if rescale_factor is not None else self.rescale_factor
     lowercase__    =  do_normalize if do_normalize is not None else self.do_normalize
     lowercase__    =  image_mean if image_mean is not None else self.image_mean
     lowercase__    =  image_std if image_std is not None else self.image_std

     lowercase__    =  make_list_of_images(_lowercase					)

     if not valid_images(_lowercase					):
        raise ValueError(
            "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
            "torch.Tensor, tf.Tensor or jax.ndarray."					)

     if do_center_crop and crop_size is None:
        raise ValueError("If `do_center_crop` is set to `True`, `crop_size` must be provided."					)

     if do_resize and size is None:
        raise ValueError("Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True."					)

     # All transformations expect numpy arrays.
     lowercase__    =  [to_numpy_array(_lowercase					) for image in images]

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

     if do_resize:
        lowercase__    =  [self.resize(image=_lowercase    ,							size=_lowercase    ,							resample=_lowercase					) for image in images]

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

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

     lowercase__    =  [to_channel_dimension_format(_lowercase    ,							_lowercase					) for image in images]

     lowercase__    =  {"pixel_values": images}
     return BatchFeature(data=_lowercase    ,							tensor_type=_lowercase					)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


_snake_case				= {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50Tokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= ["""MBart50TokenizerFast"""]


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

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






