



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


lowerCAmelCase__					  =  logging.get_logger(__name__)

class 				snake_case__(A_      ):

     """simple docstring"""






     lowercase_								=		["pixel_values"]







     def __init__(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     Union[int, float] = 1 / 255				,       SCREAMING_SNAKE_CASE :     bool = True				,       SCREAMING_SNAKE_CASE :     int = 8				,       **SCREAMING_SNAKE_CASE :     Dict				,       ):
         super().__init__(**snake_case__					)

         lowercase__					:   int													=					do_rescale
         lowercase__					:   List[str]													=					rescale_factor
         lowercase__					:   Optional[Any]													=					do_pad
         lowercase__					:   Dict													=					pad_size







     def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     float				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None				,       **SCREAMING_SNAKE_CASE :     List[str]					):
         return rescale(snake_case__				,       scale=snake_case__				,       data_format=snake_case__				,       **snake_case__					)







     def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     np.ndarray				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, ChannelDimension]] = None					):
         lowercase__					:   Union[str, Any]													=					get_image_size(snake_case__					)
         lowercase__					:   str													=					(old_height // size + 1) * size - old_height
         lowercase__					:   List[str]													=					(old_width // size + 1) * size - old_width

         return pad(snake_case__				,       ((0, pad_height), (0, pad_width))				,       mode="symmetric"				,       data_format=snake_case__					)







     def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     ImageInput				,       SCREAMING_SNAKE_CASE :     Optional[bool] = None				,       SCREAMING_SNAKE_CASE :     Optional[float] = None				,       SCREAMING_SNAKE_CASE :     Optional[bool] = None				,       SCREAMING_SNAKE_CASE :     Optional[int] = None				,       SCREAMING_SNAKE_CASE :     Optional[Union[str, TensorType]] = None				,       SCREAMING_SNAKE_CASE :     Union[str, ChannelDimension] = ChannelDimension.FIRST				,       **SCREAMING_SNAKE_CASE :     List[Any]				,       ):
         lowercase__					:   str													=					do_rescale if do_rescale is not None else self.do_rescale
         lowercase__					:   List[Any]													=					rescale_factor if rescale_factor is not None else self.rescale_factor
         lowercase__					:   Optional[Any]													=					do_pad if do_pad is not None else self.do_pad
         lowercase__					:   Dict													=					pad_size if pad_size is not None else self.pad_size

         lowercase__					:   Union[str, Any]													=					make_list_of_images(snake_case__					)

         if not valid_images(snake_case__					):
             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.
         lowercase__					:   str													=					[to_numpy_array(snake_case__					) for image in images]

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

         if do_pad:
             lowercase__					:   List[Any]													=					[self.pad(snake_case__				,       size=snake_case__					) for image in images]

         lowercase__					:   Union[str, Any]													=					[to_channel_dimension_format(snake_case__				,       snake_case__					) for image in images]

         lowercase__					:   Optional[Any]													=					{"pixel_values": images}
         return BatchFeature(data=snake_case__				,       tensor_type=snake_case__					)









from typing import TYPE_CHECKING

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


lowerCAmelCase__					  =  {}

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

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__					  =  ['''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

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




