






'''simple docstring'''






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

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


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''EleutherAI/gpt-neo-1.3B''': '''https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json''',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""gpt_neo"""
   _snake_case        =							["""past_key_values"""]
   _snake_case        =							{"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







   def __init__(self					,   __a=5_0_2_5_7					,   __a=2_0_4_8					,   __a=2_0_4_8					,   __a=2_4					,   __a=[[["global", "local"], 1_2]]					,   __a=1_6					,   __a=None					,   __a=2_5_6					,   __a="gelu_new"					,   __a=0.0					,   __a=0.0					,   __a=0.0					,   __a=0.1					,   __a=1E-5					,   __a=0.02					,   __a=True					,   __a=5_0_2_5_6					,   __a=5_0_2_5_6					,   **__a					,   )			->      Dict:
      """simple docstring"""






      __snake_case				:      Tuple     							= vocab_size
      __snake_case				:      Tuple     							= max_position_embeddings
      __snake_case				:      List[Any]     							= hidden_size
      __snake_case				:      int     							= num_layers
      __snake_case				:      Dict     							= num_heads
      __snake_case				:      Optional[int]     							= intermediate_size
      __snake_case				:      List[Any]     							= window_size
      __snake_case				:      Optional[Any]     							= activation_function
      __snake_case				:      str     							= resid_dropout
      __snake_case				:      str     							= embed_dropout
      __snake_case				:      Union[str, Any]     							= attention_dropout
      __snake_case				:      Any     							= classifier_dropout
      __snake_case				:      Tuple     							= layer_norm_epsilon
      __snake_case				:      Any     							= initializer_range
      __snake_case				:      Optional[int]     							= use_cache

      __snake_case				:      List[str]     							= bos_token_id
      __snake_case				:      int     							= eos_token_id

      __snake_case				:      Any     							= attention_types
      __snake_case				:      Tuple     							= self.expand_attention_types_params(__a)

      if len(self.attention_layers) != self.num_layers:
         raise ValueError(
             'Configuration for convolutional module is incorrect. '
             'It is required that `len(config.attention_layers)` == `config.num_layers` '
             F"""but is `len(config.attention_layers) = {len(self.attention_layers)}`, """
             F"""`config.num_layers = {self.num_layers}`. """
             '`config.attention_layers` is prepared using `config.attention_types`. '
             'Please verify the value of `config.attention_types` argument.')

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







   @staticmethod
   def     SCREAMING_SNAKE_CASE__					(__a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      List[str]     							= []
      for item in attention_types:
         for _ in range(item[1]):
            attentions.extend(item[0])
      return attentions




def        _SCREAMING_SNAKE_CASE  (				A  :       Any  ,		A  :       Tuple  ,		A  :       Dict  ,		A  :       List[str]  )    -> Optional[int]:







   """simple docstring"""
   import torch

   __snake_case				:      str     							= input.size()
   __snake_case				:      str     							= len(A  )
   __snake_case				:      List[Any]     							= shape[dimension]

   __snake_case				:      int     							= torch.arange(0  ,		A  ,		A  )
   __snake_case				:      List[Any]     							= torch.div(sizedim - size  ,		A  ,		rounding_mode='floor'  ) + 1
   __snake_case				:      Dict     							= torch.arange(A  ) + low_indices[:min_length][:, None]

   __snake_case				:      int     							= [slice(A  )] * rank
   __snake_case				:      Dict     							= indices
   __snake_case				:      List[str]     							= input[s]

   __snake_case				:      Tuple     							= list(range(0  ,		rank + 1  )  )
   perm.append(perm.pop(dimension + 1  )  )

   return sliced.permute(A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       List[Any]  )    -> Union[str, Any]:







   """simple docstring"""
   import torch

   __snake_case				:      Tuple     							= torch.arange(1  ,		A  )
   __snake_case				:      Tuple     							= torch.remainder(A  ,		A  )
   __snake_case				:      Optional[Any]     							= remainders == 0
   __snake_case				:      Optional[int]     							= candidates[divisor_indices]
   __snake_case				:      Tuple     							= torch.max(A  )
   return largest_divisor, torch.div(A  ,		A  ,		rounding_mode='floor'  )





class a_    (    UpperCamelCase_							):







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Mapping[str, Mapping[int, str]]:
      """simple docstring"""






      __snake_case				:      str     							= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
      if self.use_past:
         self.fill_with_past_key_values_(__a					,   direction='inputs')
         __snake_case				:      List[str]     							= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
         __snake_case				:      Dict     							= {0: 'batch', 1: 'sequence'}

      return common_inputs







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return self._config.num_heads







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = -1					,   __a = -1					,   __a = False					,   __a = None					,   )			->      Mapping[str, Any]:
      """simple docstring"""






      __snake_case				:      int     							= super(__a					,   self).generate_dummy_inputs(
          __a					,   batch_size=__a					,   seq_length=__a					,   is_pair=__a					,   framework=__a)

      # We need to order the input in the way they appears in the forward()
      __snake_case				:      str     							= 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

            __snake_case							,__snake_case				:      Optional[Any]     							= common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            __snake_case				:      Optional[Any]     							= seqlen + 2
            __snake_case				:      str     							= (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )
            __snake_case				:      List[Any]     							= [
                (torch.zeros(__a), torch.zeros(__a)) for _ in range(self.num_layers)
            ]

      __snake_case				:      Any     							= common_inputs['attention_mask']
      if self.use_past:
         __snake_case				:      Any     							= ordered_inputs['attention_mask'].dtype
         __snake_case				:      Optional[Any]     							= torch.cat(
             [ordered_inputs['attention_mask'], torch.ones(__a					,   __a					,   dtype=__a)]					,   dim=1)

      return ordered_inputs







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      return 1_3







'''simple docstring'''






from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [root.data, *preorder(root.left  ), *preorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return postorder(root.left  ) + postorder(root.right  ) + [root.data] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







   """simple docstring"""
   return [*inorder(root.left  ), root.data, *inorder(root.right  )] if root else []




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







   """simple docstring"""
   return (max(height(root.left  )  ,		height(root.right  )  ) + 1) if root else 0




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= process_queue.popleft()
      output.append(node.data  )

      if node.left:
         process_queue.append(node.left  )
      if node.right:
         process_queue.append(node.right  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if not root:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.left  ,		level - 1  )
         populate_output(root.right  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   def populate_output(A  :       Node | None  ,		A  :       int  ) -> None:
      if root is None:
         return
      if level == 1:
         output.append(root.data  )
      elif level > 1:
         populate_output(root.right  ,		level - 1  )
         populate_output(root.left  ,		level - 1  )

   populate_output(A  ,		A  )
   return output




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

   __snake_case				:      list[Sequence[Node | None]]     							= []

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= make_tree()
   print(F"""In-order Traversal: {inorder(A  )}"""  )
   print(F"""Pre-order Traversal: {preorder(A  )}"""  )
   print(F"""Post-order Traversal: {postorder(A  )}"""  ,		'\n'  )

   print(F"""Height of Tree: {height(A  )}"""  ,		'\n'  )

   print('Complete Level Order Traversal: '  )
   print(level_order(A  )  ,		'\n'  )

   print('Level-wise order Traversal: '  )

   for level in range(1  ,		height(A  ) + 1  ):
      print(F"""Level {level}:"""  ,		get_nodes_from_left_to_right(A  ,		level=A  )  )

   print('\nZigZag order Traversal: '  )
   print(zigzag(A  )  )


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()