code: 
  
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxSeqaSeqConfigWithPast
  from ...utils import logging
  
  
  snake_case_    						=				logging.get_logger(__name__)
  
  snake_case_    						=				{
      '''google/long-t5-local-base''': '''https://huggingface.co/google/long-t5-local-base/blob/main/config.json''',
      '''google/long-t5-local-large''': '''https://huggingface.co/google/long-t5-local-large/blob/main/config.json''',
      '''google/long-t5-tglobal-base''': '''https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json''',
      '''google/long-t5-tglobal-large''': '''https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json''',
  }
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
      '''simple docstring'''
  
  
  
  
  
      __lowerCamelCase		: str														=  """longt5"""
      __lowerCamelCase		: Optional[Any]														=  ["""past_key_values"""]
      __lowerCamelCase		: int														=  {"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}
  
  
  
  
  
      def __init__(   self		,     a=3_2128		,     a=512		,     a=64		,     a=2048		,     a=6		,     a=None		,     a=8		,     a=127		,     a=16		,     a=32		,     a=128		,     a=0.1		,     a=1e-6		,     a=1.0		,     a="relu"		,     a=True		,     a="local"		,     a=True		,     a=0		,     a=1		,     **a		,     ):
           lowercase__					:     Dict   = vocab_size
           lowercase__					:     Dict   = d_model
           lowercase__					:     str   = d_kv
           lowercase__					:     Union[str, Any]   = d_ff
           lowercase__					:     Any   = num_layers
           # default = symmetry
           lowercase__					:     List[Any]   = num_decoder_layers if num_decoder_layers is not None else self.num_layers
           lowercase__					:     int   = num_heads
           lowercase__					:     Any   = local_radius
           lowercase__					:     int   = global_block_size
           lowercase__					:     int   = relative_attention_num_buckets
           lowercase__					:     Dict   = relative_attention_max_distance
           lowercase__					:     Optional[int]   = dropout_rate
           lowercase__					:     str   = layer_norm_epsilon
           lowercase__					:     Optional[int]   = initializer_factor
           lowercase__					:     Tuple   = feed_forward_proj
           lowercase__					:     Optional[Any]   = encoder_attention_type
           lowercase__					:     Dict   = use_cache
  
           lowercase__					:     List[str]   = self.feed_forward_proj.split('-')
           lowercase__					:     int   = act_info[-1]
           lowercase__					:     Union[str, Any]   = act_info[0] == 'gated'
  
           if len(a) > 1 and act_info[0] != "gated" or len(a) > 2:
                raise ValueError(
                    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                    '\'gated-gelu\' or \'relu\'')
  
           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                lowercase__					:     List[Any]   = 'gelu_new'
  
           super().__init__(
               pad_token_id=a		,     eos_token_id=a		,     is_encoder_decoder=a		,     **a		,     )
  
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case  ):
  
      '''simple docstring'''
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           lowercase__					:     str   = {
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                lowercase__					:     int   = 'past_encoder_sequence + sequence'
                lowercase__					:     List[str]   = {0: 'batch'}
                lowercase__					:     Dict   = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                lowercase__					:     Union[str, Any]   = {0: 'batch', 1: 'decoder_sequence'}
                lowercase__					:     Any   = {0: 'batch', 1: 'decoder_sequence'}
  
           if self.use_past:
                self.fill_with_past_key_values_(a		,     direction='inputs')
  
           return common_inputs
  
  
  
  
  
      @property
      def    snake_case_       (   self):
           return 13
  
  
style_context: 
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]
  
       def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
            return 0.2126 * red + 0.587 * green + 0.114 * blue
  
       def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
            return min(SCREAMING_SNAKE_CASE_			,  255    )
  
       for i in range(SCREAMING_SNAKE_CASE_    ):
            for j in range(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
                 lowercase__					:     Dict   = [
                     normalize(SCREAMING_SNAKE_CASE_    ),
                     normalize(greyscale + factor    ),
                     normalize(greyscale + 2 * factor    ),
                 ]
  
       return img
  
  
  if __name__ == "__main__":
         # read original image
         snake_case_    						=				{
             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()
  
  