




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


a_   		=     logging.get_logger(__name__)

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					["pixel_values"]







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

          __UpperCamelCase  		=						do_rescale
          __UpperCamelCase  		=						rescale_factor
          __UpperCamelCase  		=						do_pad
          __UpperCamelCase  		=						pad_size







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







      def       snake_case	(     self					: Dict ,       snake_case					: np.ndarray ,       snake_case					: int ,       snake_case					: Optional[Union[str, ChannelDimension]] = None				):
          __UpperCamelCase		,      __UpperCamelCase  		=						get_image_size(snake_case				)
          __UpperCamelCase  		=						(old_height // size + 1) * size - old_height
          __UpperCamelCase  		=						(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					: List[str] ,       snake_case					: ImageInput ,       snake_case					: Optional[bool] = None ,       snake_case					: Optional[float] = None ,       snake_case					: Optional[bool] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[Union[str, TensorType]] = None ,       snake_case					: Union[str, ChannelDimension] = ChannelDimension.FIRST ,       **snake_case					: List[str] ,       ):
          __UpperCamelCase  		=						do_rescale if do_rescale is not None else self.do_rescale
          __UpperCamelCase  		=						rescale_factor if rescale_factor is not None else self.rescale_factor
          __UpperCamelCase  		=						do_pad if do_pad is not None else self.do_pad
          __UpperCamelCase  		=						pad_size if pad_size is not None else self.pad_size

          __UpperCamelCase  		=						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.
          __UpperCamelCase  		=						[to_numpy_array(snake_case				) for image in images]

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

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

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

          __UpperCamelCase  		=						{'''pixel_values''': images}
          return BatchFeature(data=snake_case ,       tensor_type=snake_case				)












from typing import TYPE_CHECKING

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


a_   		=     {
    "configuration_longformer": [
        "LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "LongformerConfig",
        "LongformerOnnxConfig",
    ],
    "tokenization_longformer": ["LongformerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     ["LongformerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongformerForMaskedLM",
     "LongformerForMultipleChoice",
     "LongformerForQuestionAnswering",
     "LongformerForSequenceClassification",
     "LongformerForTokenClassification",
     "LongformerModel",
     "LongformerPreTrainedModel",
     "LongformerSelfAttention",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   		=     [
     "TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLongformerForMaskedLM",
     "TFLongformerForMultipleChoice",
     "TFLongformerForQuestionAnswering",
     "TFLongformerForSequenceClassification",
     "TFLongformerForTokenClassification",
     "TFLongformerModel",
     "TFLongformerPreTrainedModel",
     "TFLongformerSelfAttention",
 ]


if TYPE_CHECKING:
 from .configuration_longformer import (
     LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
     LongformerConfig,
     LongformerOnnxConfig,
 )
 from .tokenization_longformer import LongformerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_longformer_fast import LongformerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longformer import (
      LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongformerForMaskedLM,
      LongformerForMultipleChoice,
      LongformerForQuestionAnswering,
      LongformerForSequenceClassification,
      LongformerForTokenClassification,
      LongformerModel,
      LongformerPreTrainedModel,
      LongformerSelfAttention,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_longformer import (
      TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLongformerForMaskedLM,
      TFLongformerForMultipleChoice,
      TFLongformerForQuestionAnswering,
      TFLongformerForSequenceClassification,
      TFLongformerForTokenClassification,
      TFLongformerModel,
      TFLongformerPreTrainedModel,
      TFLongformerSelfAttention,
  )

else:
 import sys

 a_   		=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






