


'''simple docstring'''



from collections import OrderedDict
from typing import Mapping

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


A__		:					Union[str, Any]												=  logging.get_logger(__name__)

A__		:					Optional[int]												=  {
    """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    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  :					List[str]							     =			'big_bird'







    def __init__(							self			,     SCREAMING_SNAKE_CASE_=5_03_58			,     SCREAMING_SNAKE_CASE_=7_68			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=12			,     SCREAMING_SNAKE_CASE_=30_72			,     SCREAMING_SNAKE_CASE_="gelu_new"			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=0.1			,     SCREAMING_SNAKE_CASE_=40_96			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=0.0_2			,     SCREAMING_SNAKE_CASE_=1E-12			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=1			,     SCREAMING_SNAKE_CASE_=2			,     SCREAMING_SNAKE_CASE_=66			,     SCREAMING_SNAKE_CASE_="block_sparse"			,     SCREAMING_SNAKE_CASE_=True			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_=64			,     SCREAMING_SNAKE_CASE_=3			,     SCREAMING_SNAKE_CASE_=None			,     **SCREAMING_SNAKE_CASE_			,     )				->     Optional[Any]:
         super().__init__(
             pad_token_id=SCREAMING_SNAKE_CASE_			,     bos_token_id=SCREAMING_SNAKE_CASE_			,     eos_token_id=SCREAMING_SNAKE_CASE_			,     sep_token_id=SCREAMING_SNAKE_CASE_			,     **SCREAMING_SNAKE_CASE_			,     )

         __lowerCamelCase   :	List[str]								=		vocab_size
         __lowerCamelCase   :	Dict								=		max_position_embeddings
         __lowerCamelCase   :	int								=		hidden_size
         __lowerCamelCase   :	Optional[Any]								=		num_hidden_layers
         __lowerCamelCase   :	str								=		num_attention_heads
         __lowerCamelCase   :	Optional[Any]								=		intermediate_size
         __lowerCamelCase   :	Any								=		hidden_act
         __lowerCamelCase   :	List[str]								=		hidden_dropout_prob
         __lowerCamelCase   :	List[str]								=		attention_probs_dropout_prob
         __lowerCamelCase   :	Optional[int]								=		initializer_range
         __lowerCamelCase   :	int								=		type_vocab_size
         __lowerCamelCase   :	Tuple								=		layer_norm_eps
         __lowerCamelCase   :	Dict								=		use_cache

         __lowerCamelCase   :	Optional[Any]								=		rescale_embeddings
         __lowerCamelCase   :	str								=		attention_type
         __lowerCamelCase   :	List[Any]								=		use_bias
         __lowerCamelCase   :	List[Any]								=		block_size
         __lowerCamelCase   :	Dict								=		num_random_blocks
         __lowerCamelCase   :	Tuple								=		classifier_dropout







class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






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










'''simple docstring'''



import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey





class    UpperCAmelCase_  :





    """simple docstring"""






    def __init__(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     int:
         if dst_width < 0 or dst_height < 0:
              raise ValueError('Destination width/height should be > 0'	)

         __lowerCamelCase   :	Dict								=		img
         __lowerCamelCase   :	Any								=		img.shape[1]
         __lowerCamelCase   :	Optional[int]								=		img.shape[0]
         __lowerCamelCase   :	Dict								=		dst_width
         __lowerCamelCase   :	str								=		dst_height

         __lowerCamelCase   :	Dict								=		self.src_w / self.dst_w
         __lowerCamelCase   :	List[Any]								=		self.src_h / self.dst_h

         __lowerCamelCase   :	Optional[int]								=		(
             np.ones((self.dst_h, self.dst_w, 3)			,     np.uinta	) * 2_55
         )







    def       lowercase_				(							self	)				->     List[Any]:
         for i in range(self.dst_h	):
              for j in range(self.dst_w	):
                   __lowerCamelCase   :	Union[str, Any]								=		self.img[self.get_y(SCREAMING_SNAKE_CASE_	)][self.get_x(SCREAMING_SNAKE_CASE_	)]







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_x * x	)







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_	)				->     int:
         return int(self.ratio_y * y	)


if __name__ == "__main__":
      A__						,					A__		:					Optional[Any]												=  800, 600
      A__		:					List[str]												=  imread("""image_data/lena.jpg""", 1)
      A__		:					List[Any]												=  NearestNeighbour(im, dst_w, dst_h)
      n.process()

      imshow(
          f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
      )
      waitKey(0)
      destroyAllWindows()






