





'''simple docstring'''

from typing import Dict, Iterable, 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, logging


UpperCAmelCase					:		List[Any]		=							logging.get_logger(__name__)





class lowerCamelCase						(a__					):
  _lowercase  :	int       					=				["""pixel_values"""]







  def __init__(		self     ,     lowercase__ = True     ,     lowercase__ = None     ,     lowercase__ = PILImageResampling.BICUBIC     ,     lowercase__ = True     ,     lowercase__ = None     ,     lowercase__ = True     ,     lowercase__ = 1 / 255     ,     lowercase__ = True     ,     lowercase__ = IMAGENET_DEFAULT_MEAN     ,     lowercase__ = IMAGENET_DEFAULT_STD     ,     **lowercase__     ,     )		->						None:






   """simple docstring"""





   super().__init__(**lowercase__		)
   _snake_case		:	Any					     =     size if size is not None else {'''shortest_edge''': 224}
   _snake_case		:	Union[str, Any]					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
   _snake_case		:	Optional[int]					     =     crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
   _snake_case		:	str					     =     get_size_dict(lowercase__     ,     param_name='''crop_size'''		)

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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = PILImageResampling.BICUBIC     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






   """simple docstring"""





   _snake_case		:	Optional[Any]					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
   # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
   if "shortest_edge" in size:
    _snake_case		:	Union[str, Any]					     =     int((256 / 224) * size['''shortest_edge''']		)
    _snake_case		:	Tuple					     =     get_resize_output_image_size(lowercase__     ,     size=lowercase__     ,     default_to_square=lowercase__		)
    _snake_case		:	str					     =     {'''height''': output_size[0], '''width''': output_size[1]}
   if "height" not in size_dict or "width" not in size_dict:
    raise ValueError(
        F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
   return resize(
       lowercase__     ,     size=(size_dict['''height'''], size_dict['''width'''])     ,     resample=lowercase__     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






   """simple docstring"""





   _snake_case		:	Union[str, Any]					     =     get_size_dict(lowercase__		)
   if "height" not in size or "width" not in size:
    raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
   return center_crop(lowercase__     ,     size=(size['''height'''], size['''width'''])     ,     data_format=lowercase__     ,     **lowercase__		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






   """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__     ,     lowercase__ = None     ,     **lowercase__     ,     )		->						np.ndarray:






   """simple docstring"""





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







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = None     ,     lowercase__ = ChannelDimension.FIRST     ,     **lowercase__     ,     )		->						BatchFeature:






   """simple docstring"""





   _snake_case		:	Optional[int]					     =     do_resize if do_resize is not None else self.do_resize
   _snake_case		:	Dict					     =     resample if resample is not None else self.resample
   _snake_case		:	Any					     =     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		:	List[str]					     =     do_normalize if do_normalize is not None else self.do_normalize
   _snake_case		:	List[Any]					     =     image_mean if image_mean is not None else self.image_mean
   _snake_case		:	Union[str, Any]					     =     image_std if image_std is not None else self.image_std

   _snake_case		:	Tuple					     =     size if size is not None else self.size
   _snake_case		:	Optional[Any]					     =     get_size_dict(lowercase__     ,     default_to_square=lowercase__		)
   _snake_case		:	Union[str, Any]					     =     crop_size if crop_size is not None else self.crop_size
   _snake_case		:	str					     =     get_size_dict(lowercase__     ,     param_name='''crop_size'''		)

   _snake_case		:	str					     =     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_resize and size is None:
    raise ValueError('''Size must be specified if do_resize is True.'''		)

   if do_center_crop and crop_size is None:
    raise ValueError('''Crop size 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[Any]					     =     [to_numpy_array(lowercase__		) for image in images]

   if do_resize:
    _snake_case		:	Optional[int]					     =     [self.resize(lowercase__     ,     lowercase__     ,     lowercase__		) for image in images]

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

   if do_rescale:
    _snake_case		:	Optional[Any]					     =     [self.rescale(lowercase__     ,     lowercase__		) for image in images]

   if do_normalize:
    _snake_case		:	str					     =     [self.normalize(lowercase__     ,     lowercase__     ,     lowercase__		) for image in images]

   _snake_case		:	int					     =     [to_channel_dimension_format(lowercase__     ,     lowercase__		) for image in images]

   _snake_case		:	Dict					     =     {'''pixel_values''': images}
   return BatchFeature(data=lowercase__     ,     tensor_type=lowercase__		)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase					:		int		=							{
    'configuration_pegasus_x': ['PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusXConfig'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase					:		Union[str, Any]		=							[
       'PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PegasusXForConditionalGeneration',
       'PegasusXModel',
       'PegasusXPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pegasus_x import (
          PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
          PegasusXForConditionalGeneration,
          PegasusXModel,
          PegasusXPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase					:		Dict		=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


