





from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


snake_case_           =      logging.get_logger(__name__)

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   ['pixel_values']


      def __init__(self	:				Dict    ,      a__	:				bool = True    ,      a__	:				Union[int, float] = 1 / 255    ,      a__	:				bool = True    ,      a__	:				int = 8    ,      **a__	:				Optional[Any]    ,      ):





         """simple docstring"""



         super().__init__(**a__				)

         __snake_case								=       do_rescale
         __snake_case								=       rescale_factor
         __snake_case								=       do_pad
         __snake_case								=       pad_size


      def        a			(self	:				str    ,      a__	:				np.ndarray    ,      a__	:				float    ,      a__	:				Optional[Union[str, ChannelDimension]] = None    ,      **a__	:				Dict				):





         """simple docstring"""



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


      def        a			(self	:				List[str]    ,      a__	:				np.ndarray    ,      a__	:				int    ,      a__	:				Optional[Union[str, ChannelDimension]] = None				):





         """simple docstring"""



         __snake_case	,     __snake_case								=       get_image_size(a__				)
         __snake_case								=       (old_height // size + 1) * size - old_height
         __snake_case								=       (old_width // size + 1) * size - old_width

         return pad(a__    ,      ((0, pad_height), (0, pad_width))    ,      mode='''symmetric'''    ,      data_format=a__				)


      def        a			(self	:				Optional[Any]    ,      a__	:				ImageInput    ,      a__	:				Optional[bool] = None    ,      a__	:				Optional[float] = None    ,      a__	:				Optional[bool] = None    ,      a__	:				Optional[int] = None    ,      a__	:				Optional[Union[str, TensorType]] = None    ,      a__	:				Union[str, ChannelDimension] = ChannelDimension.FIRST    ,      **a__	:				int    ,      ):





         """simple docstring"""



         __snake_case								=       do_rescale if do_rescale is not None else self.do_rescale
         __snake_case								=       rescale_factor if rescale_factor is not None else self.rescale_factor
         __snake_case								=       do_pad if do_pad is not None else self.do_pad
         __snake_case								=       pad_size if pad_size is not None else self.pad_size

         __snake_case								=       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_rescale and rescale_factor is None:
            raise ValueError('''Rescale factor must be specified if do_rescale is True.'''				)

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

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

         if do_pad:
            __snake_case								=       [self.pad(a__    ,      size=a__				) for image in images]

         __snake_case								=       [to_channel_dimension_format(a__    ,      a__				) for image in images]

         __snake_case								=       {'''pixel_values''': images}
         return BatchFeature(data=a__    ,      tensor_type=a__				)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case_           =      {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizer']

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_           =      ['NllbTokenizerFast']


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

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_nllb_fast import NllbTokenizerFast

else:
    import sys

    snake_case_           =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

