




'''simple docstring'''



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCamelCase_	 =		logging.get_logger(__name__)

lowerCamelCase_	 =		{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}







class 	lowercase_					(				A     ):


   """simple docstring"""

   lowerCamelCase_       						=		'''gpt2'''
   lowerCamelCase_       						=		['''past_key_values''']
   lowerCamelCase_       						=		{
       '''hidden_size''': '''n_embd''',
       '''max_position_embeddings''': '''n_positions''',
       '''num_attention_heads''': '''n_head''',
       '''num_hidden_layers''': '''n_layer''',
   }



   def __init__(					self      :			Dict						,   __lowerCamelCase      :			Tuple=5_0_2_5_7						,   __lowerCamelCase      :			List[Any]=1_0_2_4						,   __lowerCamelCase      :			str=7_6_8						,   __lowerCamelCase      :			int=1_2						,   __lowerCamelCase      :			Tuple=1_2						,   __lowerCamelCase      :			int=None						,   __lowerCamelCase      :			Union[str, Any]="gelu_new"						,   __lowerCamelCase      :			Optional[Any]=0.1						,   __lowerCamelCase      :			Any=0.1						,   __lowerCamelCase      :			Union[str, Any]=0.1						,   __lowerCamelCase      :			Tuple=1e-5						,   __lowerCamelCase      :			Any=0.0_2						,   __lowerCamelCase      :			Union[str, Any]="cls_index"						,   __lowerCamelCase      :			Any=True						,   __lowerCamelCase      :			Tuple=None						,   __lowerCamelCase      :			Tuple=True						,   __lowerCamelCase      :			Tuple=0.1						,   __lowerCamelCase      :			Optional[int]=True						,   __lowerCamelCase      :			List[Any]=True						,   __lowerCamelCase      :			str=5_0_2_5_6						,   __lowerCamelCase      :			Any=5_0_2_5_6						,   __lowerCamelCase      :			Tuple=False						,   __lowerCamelCase      :			Dict=False						,   **__lowerCamelCase      :			str						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   vocab_size
      _SCREAMING_SNAKE_CASE =   n_positions
      _SCREAMING_SNAKE_CASE =   n_embd
      _SCREAMING_SNAKE_CASE =   n_layer
      _SCREAMING_SNAKE_CASE =   n_head
      _SCREAMING_SNAKE_CASE =   n_inner
      _SCREAMING_SNAKE_CASE =   activation_function
      _SCREAMING_SNAKE_CASE =   resid_pdrop
      _SCREAMING_SNAKE_CASE =   embd_pdrop
      _SCREAMING_SNAKE_CASE =   attn_pdrop
      _SCREAMING_SNAKE_CASE =   layer_norm_epsilon
      _SCREAMING_SNAKE_CASE =   initializer_range
      _SCREAMING_SNAKE_CASE =   summary_type
      _SCREAMING_SNAKE_CASE =   summary_use_proj
      _SCREAMING_SNAKE_CASE =   summary_activation
      _SCREAMING_SNAKE_CASE =   summary_first_dropout
      _SCREAMING_SNAKE_CASE =   summary_proj_to_labels
      _SCREAMING_SNAKE_CASE =   scale_attn_weights
      _SCREAMING_SNAKE_CASE =   use_cache
      _SCREAMING_SNAKE_CASE =   scale_attn_by_inverse_layer_idx
      _SCREAMING_SNAKE_CASE =   reorder_and_upcast_attn

      _SCREAMING_SNAKE_CASE =   bos_token_id
      _SCREAMING_SNAKE_CASE =   eos_token_id

      super().__init__(bos_token_id=__lowerCamelCase						,   eos_token_id=__lowerCamelCase						,   **__lowerCamelCase					)







class 	lowercase_					(				A     ):


   """simple docstring"""

   def __init__(					self      :			List[str]						,   __lowerCamelCase      :			PretrainedConfig						,   __lowerCamelCase      :			str = "default"						,   __lowerCamelCase      :			List[PatchingSpec] = None						,   __lowerCamelCase      :			bool = False						,   ):

      """simple docstring"""
      super().__init__(__lowerCamelCase						,   task=__lowerCamelCase						,   patching_specs=__lowerCamelCase						,   use_past=__lowerCamelCase					)
      if not getattr(self._config						,   "pad_token_id"						,   __lowerCamelCase					):
         # TODO: how to do that better?
         _SCREAMING_SNAKE_CASE =   0



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
      if self.use_past:
         self.fill_with_past_key_values_(__lowerCamelCase						,   direction="inputs"					)
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "past_sequence + sequence"}
      else:
         _SCREAMING_SNAKE_CASE =   {0: "batch", 1: "sequence"}

      return common_inputs



   @property
   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      return self._config.n_layer



   @property
   def 							lowerCAmelCase_    (					self      :			Union[str, Any]					):

      """simple docstring"""
      return self._config.n_head



   def 							lowerCAmelCase_    (					self      :			Optional[Any]						,   __lowerCamelCase      :			PreTrainedTokenizer						,   __lowerCamelCase      :			int = -1						,   __lowerCamelCase      :			int = -1						,   __lowerCamelCase      :			bool = False						,   __lowerCamelCase      :			Optional[TensorType] = None						,   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE =   super(__lowerCamelCase						,   self					).generate_dummy_inputs(
          __lowerCamelCase						,   batch_size=__lowerCamelCase						,   seq_length=__lowerCamelCase						,   is_pair=__lowerCamelCase						,   framework=__lowerCamelCase					)

      # We need to order the input in the way they appears in the forward()
      _SCREAMING_SNAKE_CASE =   OrderedDict({"input_ids": common_inputs["input_ids"]}					)

      # Need to add the past_keys
      if self.use_past:
         if not is_torch_available():
            raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
         else:
            import torch

            _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE =   common_inputs["input_ids"].shape
            # Not using the same length for past_key_values
            _SCREAMING_SNAKE_CASE =   seqlen + 2
            _SCREAMING_SNAKE_CASE =   (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            _SCREAMING_SNAKE_CASE =   [
                (torch.zeros(__lowerCamelCase					), torch.zeros(__lowerCamelCase					)) for _ in range(self.num_layers					)
            ]

      _SCREAMING_SNAKE_CASE =   common_inputs["attention_mask"]
      if self.use_past:
         _SCREAMING_SNAKE_CASE =   ordered_inputs["attention_mask"].dtype
         _SCREAMING_SNAKE_CASE =   torch.cat(
             [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase						,   __lowerCamelCase						,   dtype=__lowerCamelCase					)]						,   dim=1					)

      return ordered_inputs



   @property
   def 							lowerCAmelCase_    (					self      :			int					):

      """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()

