





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


UpperCAmelCase_					:   int 							=						logging.get_logger(__name__)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  ["""pixel_values"""]







  def __init__(	self    ,						_lowercase = True    ,						_lowercase = 1 / 255    ,						_lowercase = True    ,						_lowercase = 8    ,						**_lowercase    ,						):
       super().__init__(**_lowercase						)

       lowerCAmelCase_					:       Dict    =		do_rescale
       lowerCAmelCase_					:       List[str]    =		rescale_factor
       lowerCAmelCase_					:       Union[str, Any]    =		do_pad
       lowerCAmelCase_					:       List[Any]    =		pad_size







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None    ,						**_lowercase						):
       return rescale(_lowercase    ,						scale=_lowercase    ,						data_format=_lowercase    ,						**_lowercase						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase    ,						_lowercase = None						):
       lowerCAmelCase_   ,  lowerCAmelCase_					:       str    =		get_image_size(_lowercase						)
       lowerCAmelCase_					:       Any    =		(old_height // size + 1) * size - old_height
       lowerCAmelCase_					:       Tuple    =		(old_width // size + 1) * size - old_width

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







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = None    ,						_lowercase = ChannelDimension.FIRST    ,						**_lowercase    ,						):
       lowerCAmelCase_					:       Optional[int]    =		do_rescale if do_rescale is not None else self.do_rescale
       lowerCAmelCase_					:       Dict    =		rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCAmelCase_					:       Union[str, Any]    =		do_pad if do_pad is not None else self.do_pad
       lowerCAmelCase_					:       Dict    =		pad_size if pad_size is not None else self.pad_size

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

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

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

       if do_pad:
            lowerCAmelCase_					:       Any    =		[self.pad(_lowercase    ,						size=_lowercase						) for image in images]

       lowerCAmelCase_					:       List[Any]    =		[to_channel_dimension_format(_lowercase    ,						_lowercase						) for image in images]

       lowerCAmelCase_					:       Optional[int]    =		{"""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,
    is_torch_available,
)


UpperCAmelCase_					:   int 							=						{"""configuration_plbart""": ["""PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PLBartConfig"""]}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Optional[int] 							=						["""PLBartTokenizer"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_					:   Union[str, Any] 							=						[
          """PLBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """PLBartForCausalLM""",
          """PLBartForConditionalGeneration""",
          """PLBartForSequenceClassification""",
          """PLBartModel""",
          """PLBartPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_plbart import PLBartTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_plbart import (
                PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                PLBartForCausalLM,
                PLBartForConditionalGeneration,
                PLBartForSequenceClassification,
                PLBartModel,
                PLBartPreTrainedModel,
            )


else:
      import sys

      UpperCAmelCase_					:   List[str] 							=						_LazyModule(__name__, globals()["""__file__"""], _import_structure)
