




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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_   		=     logging.get_logger(__name__)

a_   		=     {
    "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
    "google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
    "google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"big_bird"







      def __init__(     self					: str ,       snake_case					: Optional[Any]=50358 ,       snake_case					: Union[str, Any]=768 ,       snake_case					: Dict=12 ,       snake_case					: List[Any]=12 ,       snake_case					: Tuple=3072 ,       snake_case					: Tuple="gelu_new" ,       snake_case					: Dict=0.1 ,       snake_case					: Any=0.1 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=2 ,       snake_case					: Union[str, Any]=0.02 ,       snake_case					: Union[str, Any]=1E-12 ,       snake_case					: List[Any]=True ,       snake_case					: List[Any]=0 ,       snake_case					: int=1 ,       snake_case					: int=2 ,       snake_case					: Any=66 ,       snake_case					: str="block_sparse" ,       snake_case					: Any=True ,       snake_case					: Tuple=False ,       snake_case					: List[Any]=64 ,       snake_case					: Dict=3 ,       snake_case					: Union[str, Any]=None ,       **snake_case					: Any ,       ):
          super().__init__(
              pad_token_id=snake_case ,       bos_token_id=snake_case ,       eos_token_id=snake_case ,       sep_token_id=snake_case ,       **snake_case ,       )

          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						max_position_embeddings
          __UpperCamelCase  		=						hidden_size
          __UpperCamelCase  		=						num_hidden_layers
          __UpperCamelCase  		=						num_attention_heads
          __UpperCamelCase  		=						intermediate_size
          __UpperCamelCase  		=						hidden_act
          __UpperCamelCase  		=						hidden_dropout_prob
          __UpperCamelCase  		=						attention_probs_dropout_prob
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						type_vocab_size
          __UpperCamelCase  		=						layer_norm_eps
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						rescale_embeddings
          __UpperCamelCase  		=						attention_type
          __UpperCamelCase  		=						use_bias
          __UpperCamelCase  		=						block_size
          __UpperCamelCase  		=						num_random_blocks
          __UpperCamelCase  		=						classifier_dropout







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      @property
      def       snake_case	(     self					: int				):
          if self.task == "multiple-choice":
              __UpperCamelCase  		=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}
          return OrderedDict(
              [
                  ('''input_ids''', dynamic_axis),
                  ('''attention_mask''', dynamic_axis),
              ]				)






