




from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase__						:       Any							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[int]							=    {
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}

class      UpperCAmelCase						(			A    ):
   __SCREAMING_SNAKE_CASE		:							Tuple							=					SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   __SCREAMING_SNAKE_CASE		:							str							=					"squeezebert"






   def __init__(	self   :       Dict						,					UpperCamelCase_   :       List[Any]=3_05_22						,					UpperCamelCase_   :       Tuple=7_68						,					UpperCamelCase_   :       Optional[Any]=12						,					UpperCamelCase_   :       int=12						,					UpperCamelCase_   :       int=30_72						,					UpperCamelCase_   :       int="gelu"						,					UpperCamelCase_   :       int=0.1						,					UpperCamelCase_   :       Tuple=0.1						,					UpperCamelCase_   :       int=5_12						,					UpperCamelCase_   :       Optional[Any]=2						,					UpperCamelCase_   :       int=0.02						,					UpperCamelCase_   :       Optional[int]=1E-12						,					UpperCamelCase_   :       int=0						,					UpperCamelCase_   :       Optional[int]=7_68						,					UpperCamelCase_   :       int=4						,					UpperCamelCase_   :       List[Any]=4						,					UpperCamelCase_   :       Optional[int]=4						,					UpperCamelCase_   :       Optional[int]=1						,					UpperCamelCase_   :       Any=4						,					UpperCamelCase_   :       Dict=4						,					**UpperCamelCase_   :       List[str]						,					)->			List[Any]:
     super().__init__(pad_token_id=__lowerCamelCase						,					**__lowerCamelCase     )

     _lowercase: str						     =    vocab_size
     _lowercase: int						     =    hidden_size
     _lowercase: Optional[Any]						     =    num_hidden_layers
     _lowercase: List[str]						     =    num_attention_heads
     _lowercase: Tuple						     =    hidden_act
     _lowercase: Union[str, Any]						     =    intermediate_size
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[Any]						     =    attention_probs_dropout_prob
     _lowercase: Union[str, Any]						     =    max_position_embeddings
     _lowercase: List[str]						     =    type_vocab_size
     _lowercase: Any						     =    initializer_range
     _lowercase: str						     =    layer_norm_eps
     _lowercase: Dict						     =    embedding_size
     _lowercase: Dict						     =    q_groups
     _lowercase: List[Any]						     =    k_groups
     _lowercase: List[Any]						     =    v_groups
     _lowercase: Any						     =    post_attention_groups
     _lowercase: Tuple						     =    intermediate_groups
     _lowercase: Union[str, Any]						     =    output_groups

class      UpperCAmelCase						(			A    ):






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






from cva import destroyAllWindows, imread, imshow, waitKey


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase     ,					_lowercase: Tuple						     =    img.shape[0], img.shape[1]

  def to_grayscale(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							):
    return 0.2_126 * red + 0.587 * green + 0.114 * blue

  def normalize(UpperCamelCase							):
    return min(UpperCamelCase					,     255							)

  for i in range(UpperCamelCase							):
    for j in range(UpperCamelCase							):
      _lowercase: Any						     =    int(to_grayscale(*img[i][j]							)							)
      _lowercase: str						     =    [
          normalize(UpperCamelCase							),
          normalize(greyscale + factor							),
          normalize(greyscale + 2 * factor							),
      ]

  return img


if __name__ == "__main__":
   # read original image
   UpperCAmelCase__						:       Optional[Any]							=    {
       percentage: imread('image_data/lena.jpg', 1) for percentage in (10, 20, 30, 40)
   }

   for percentage, img in images.items():
      make_sepia(img, percentage)

   for percentage, img in images.items():
      imshow(f'''Original image with sepia (factor: {percentage})''', img)

   waitKey(0)
   destroyAllWindows()
