




from typing import Mapping

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


lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{
    '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 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		'''longt5'''
   lowerCamelCase_       						=		['''past_key_values''']
   lowerCamelCase_       						=		{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



   def __init__(					self      :			Any						,   __lowerCamelCase      :			List[Any]=3_2_1_2_8						,   __lowerCamelCase      :			str=5_1_2						,   __lowerCamelCase      :			Dict=6_4						,   __lowerCamelCase      :			Union[str, Any]=2_0_4_8						,   __lowerCamelCase      :			Dict=6						,   __lowerCamelCase      :			Any=None						,   __lowerCamelCase      :			int=8						,   __lowerCamelCase      :			Optional[int]=1_2_7						,   __lowerCamelCase      :			Union[str, Any]=1_6						,   __lowerCamelCase      :			Any=3_2						,   __lowerCamelCase      :			str=1_2_8						,   __lowerCamelCase      :			Tuple=0.1						,   __lowerCamelCase      :			Any=1e-6						,   __lowerCamelCase      :			Optional[Any]=1.0						,   __lowerCamelCase      :			Dict="relu"						,   __lowerCamelCase      :			Optional[int]=True						,   __lowerCamelCase      :			Tuple="local"						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			int=0						,   __lowerCamelCase      :			Dict=1						,   **__lowerCamelCase      :			Optional[int]						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   d_model
      _SCREAMING_SNAKE_CASE =   d_kv
      _SCREAMING_SNAKE_CASE =   d_ff
      _SCREAMING_SNAKE_CASE =   num_layers
      # default = symmetry
      _SCREAMING_SNAKE_CASE =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
      _SCREAMING_SNAKE_CASE =   num_heads
      _SCREAMING_SNAKE_CASE =   local_radius
      _SCREAMING_SNAKE_CASE =   global_block_size
      _SCREAMING_SNAKE_CASE =   relative_attention_num_buckets
      _SCREAMING_SNAKE_CASE =   relative_attention_max_distance
      _SCREAMING_SNAKE_CASE =   dropout_rate
      _SCREAMING_SNAKE_CASE =   layer_norm_epsilon
      _SCREAMING_SNAKE_CASE =   initializer_factor
      _SCREAMING_SNAKE_CASE =   feed_forward_proj
      _SCREAMING_SNAKE_CASE =   encoder_attention_type
      _SCREAMING_SNAKE_CASE =   use_cache

      _SCREAMING_SNAKE_CASE =   self.feed_forward_proj.split("-"					)
      _SCREAMING_SNAKE_CASE =   act_info[-1]
      _SCREAMING_SNAKE_CASE =   act_info[0] == "gated"

      if len(__lowerCamelCase					) > 1 and act_info[0] != "gated" or len(__lowerCamelCase					) > 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":
         _SCREAMING_SNAKE_CASE =   "gelu_new"

      super().__init__(
          pad_token_id=__lowerCamelCase						,   eos_token_id=__lowerCamelCase						,   is_encoder_decoder=__lowerCamelCase						,   **__lowerCamelCase						,   )







class 	lowercase_					(				A     ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_    (					self      :			Dict					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   {
          "input_ids": {0: "batch", 1: "encoder_sequence"},
          "attention_mask": {0: "batch", 1: "encoder_sequence"},
      }
      if self.use_past:
         _SCREAMING_SNAKE_CASE =   "past_encoder_sequence + sequence"
         _SCREAMING_SNAKE_CASE =   {0: "batch"}
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "past_decoder_sequence + sequence"}
      else:
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "decoder_sequence"}
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "decoder_sequence"}

      if self.use_past:
         self.fill_with_past_key_values_(__lowerCamelCase						,   direction="inputs"					)

      return common_inputs



   @property
   def 							lowerCAmelCase_    (					self      :			Optional[Any]					):

      """simple docstring"""
      return 1_3







'''simple docstring'''



lowerCamelCase_	 =		[[0, 1, 1], [0, 0, 1], [1, 0, 0]]







class 	lowercase_					:


   """simple docstring"""

   def __init__(					self      :			List[Any]						,   __lowerCamelCase      :			str					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   name
      _SCREAMING_SNAKE_CASE =   []
      _SCREAMING_SNAKE_CASE =   []



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Union[str, Any]					):

      """simple docstring"""
      self.inbound.append(__lowerCamelCase					)



   def 							lowerCAmelCase_    (					self      :			int						,   __lowerCamelCase      :			Any					):

      """simple docstring"""
      self.outbound.append(__lowerCamelCase					)



   def __repr__(					self      :			List[str]					):

      """simple docstring"""
      return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""


def 				SCREAMING_SNAKE_CASE_				(							__A       :				Tuple ,  __A       :				List[Any]=3 ,  __A       :				Optional[int]=0.8_5						)				->				Optional[Any]:
   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   1

   _SCREAMING_SNAKE_CASE =   {}
   for node in nodes:
      _SCREAMING_SNAKE_CASE =   len(node.outbound						)

   for i in range(__A						):
      print(f"""======= Iteration {i + 1} ======="""						)
      for _, node in enumerate(__A						):
         _SCREAMING_SNAKE_CASE =   (1 - d) + d * sum(
             ranks[ib] / outbounds[ib] for ib in node.inbound						)
      print(__A						)
def 				SCREAMING_SNAKE_CASE_				(							)				->				List[str]:
   _SCREAMING_SNAKE_CASE =   list(input("Enter Names of the Nodes: "						).split()						)

   _SCREAMING_SNAKE_CASE =   [Node(__A						) for name in names]

   for ri, row in enumerate(__A						):
      for ci, col in enumerate(__A						):
         if col == 1:
            nodes[ci].add_inbound(names[ri]						)
            nodes[ri].add_outbound(names[ci]						)

   print("======= Nodes ======="						)
   for node in nodes:
      print(__A						)

   page_rank(__A						)


if __name__ == "__main__":
 main()

