
"""simple docstring"""



from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


a					:	List[str]							=		logging.get_logger(__name__)




class 		a_						( _UpperCAmelCase	):
    a      :				int     =				['pixel_values']







    def __init__(			self			:       List[Any]     ,					__UpperCamelCase			:       bool = True     ,					__UpperCamelCase			:       int = 32     ,					__UpperCamelCase			:       Optional[Any]=PILImageResampling.BILINEAR     ,					__UpperCamelCase			:       bool = True     ,					**__UpperCamelCase			:       Any     ,					)		->None:






        '''simple docstring'''






        _UpperCAmelCase          =   do_resize
        _UpperCAmelCase          =   do_rescale
        _UpperCAmelCase          =   size_divisor
        _UpperCAmelCase          =   resample
        super().__init__(**__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[ChannelDimension] = None     ,					**__UpperCamelCase			:       Any     )		->np.ndarray:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase          =   get_image_size(__UpperCamelCase     )
        # Rounds the height and width down to the closest multiple of size_divisor
        _UpperCAmelCase          =   height // size_divisor * size_divisor
        _UpperCAmelCase          =   width // size_divisor * size_divisor
        _UpperCAmelCase          =   resize(__UpperCamelCase     ,					(new_h, new_w)     ,					resample=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )
        return image







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       np.ndarray     ,					__UpperCamelCase			:       float     ,					__UpperCamelCase			:       Optional[ChannelDimension] = None     ,					**__UpperCamelCase			:       Optional[Any]     )		->np.ndarray:






        '''simple docstring'''






        return rescale(image=__UpperCamelCase     ,					scale=__UpperCamelCase     ,					data_format=__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[int] = None     ,					__UpperCamelCase			:       Dict=None     ,					__UpperCamelCase			:       Optional[bool] = None     ,					__UpperCamelCase			:       Optional[Union[TensorType, str]] = None     ,					__UpperCamelCase			:       ChannelDimension = ChannelDimension.FIRST     ,					**__UpperCamelCase			:       Optional[int]     ,					)		->BatchFeature:






        '''simple docstring'''






        _UpperCAmelCase          =   do_resize if do_resize is not None else self.do_resize
        _UpperCAmelCase          =   do_rescale if do_rescale is not None else self.do_rescale
        _UpperCAmelCase          =   size_divisor if size_divisor is not None else self.size_divisor
        _UpperCAmelCase          =   resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError("""size_divisor is required for resizing"""     )

        _UpperCAmelCase          =   make_list_of_images(__UpperCamelCase     )

        if not valid_images(__UpperCamelCase     ):
            raise ValueError("""Invalid image(s)"""     )

        # All transformations expect numpy arrays.
        _UpperCAmelCase          =   [to_numpy_array(__UpperCamelCase     ) for img in images]

        if do_resize:
            _UpperCAmelCase          =   [self.resize(__UpperCamelCase     ,					size_divisor=__UpperCamelCase     ,					resample=__UpperCamelCase     ) for image in images]

        if do_rescale:
            _UpperCAmelCase          =   [self.rescale(__UpperCamelCase     ,					scale=1 / 2_55     ) for image in images]

        _UpperCAmelCase          =   [to_channel_dimension_format(__UpperCamelCase     ,					__UpperCamelCase     ) for image in images]

        _UpperCAmelCase          =   {"""pixel_values""": images}
        return BatchFeature(data=__UpperCamelCase     ,					tensor_type=__UpperCamelCase     )

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''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

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