


"""simple docstring"""







from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin






class 						_A  (  _a   ,_a   ,_a  ):







    """simple docstring"""




    UpperCAmelCase	:							str     =	[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]






    @register_to_config
    def __init__( self    :   str    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   Optional[int] = None    ,			__UpperCAmelCase    :   int = 50257    ,			__UpperCAmelCase    :   int = 1024    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   Optional[int] = None    ,			__UpperCAmelCase    :   str = "gelu_new"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 1e-5    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   bool = True    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   bool = False    ,			):
      super().__init__()

      a						:		List[Any]											=      prefix_length

      if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
        raise ValueError(
            f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
            f''' `n_embd`: {n_embd} are not equal.''')

      a						:		str											=      prefix_inner_dim
      a						:		Dict											=      prefix_hidden_dim

      a						:		Optional[int]											=      (
          nn.Linear(self.prefix_inner_dim    ,			self.prefix_hidden_dim)
          if self.prefix_hidden_dim is not None
          else nn.Identity()
      )
      a						:		Dict											=      (
          nn.Linear(self.prefix_hidden_dim    ,			__UpperCAmelCase) if self.prefix_hidden_dim is not None else nn.Identity()
      )

      a						:		Dict											=      GPTaConfig(
          vocab_size=__UpperCAmelCase    ,			n_positions=__UpperCAmelCase    ,			n_embd=__UpperCAmelCase    ,			n_layer=__UpperCAmelCase    ,			n_head=__UpperCAmelCase    ,			n_inner=__UpperCAmelCase    ,			activation_function=__UpperCAmelCase    ,			resid_pdrop=__UpperCAmelCase    ,			embd_pdrop=__UpperCAmelCase    ,			attn_pdrop=__UpperCAmelCase    ,			layer_norm_epsilon=__UpperCAmelCase    ,			initializer_range=__UpperCAmelCase    ,			scale_attn_weights=__UpperCAmelCase    ,			use_cache=__UpperCAmelCase    ,			scale_attn_by_inverse_layer_idx=__UpperCAmelCase    ,			reorder_and_upcast_attn=__UpperCAmelCase    ,			)
      a						:		Tuple											=      GPTaLMHeadModel(__UpperCAmelCase)






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   torch.Tensor    ,			__UpperCAmelCase    :   torch.Tensor    ,			__UpperCAmelCase    :   Optional[torch.Tensor] = None    ,			__UpperCAmelCase    :   Optional[torch.Tensor] = None    ,			):
      a						:		Optional[Any]											=      self.transformer.transformer.wte(__UpperCAmelCase)
      a						:		Union[str, Any]											=      self.encode_prefix(__UpperCAmelCase)
      a						:		List[str]											=      self.decode_prefix(__UpperCAmelCase)
      a						:		Optional[Any]											=      torch.cat((prefix_embeds, embedding_text)    ,			dim=1)

      if labels is not None:
        a						:		Any											=      self.get_dummy_token(input_ids.shape[0]    ,			input_ids.device)
        a						:		Optional[Any]											=      torch.cat((dummy_token, input_ids)    ,			dim=1)
      a						:		Optional[int]											=      self.transformer(inputs_embeds=__UpperCAmelCase    ,			labels=__UpperCAmelCase    ,			attention_mask=__UpperCAmelCase)
      if self.prefix_hidden_dim is not None:
        return out, hidden
      else:
        return out






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   int    ,			__UpperCAmelCase    :   torch.device):
      return torch.zeros(__UpperCAmelCase    ,			self.prefix_length    ,			dtype=torch.intaa    ,			device=__UpperCAmelCase)






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Tuple):
      return self.encode_prefix(__UpperCAmelCase)






    @torch.no_grad()
    def      __snake_case							( self    :   Tuple    ,			__UpperCAmelCase    :   Optional[int]    ,			__UpperCAmelCase    :   List[str]    ,			__UpperCAmelCase    :   List[Any]):
      a						:		Dict											=      torch.split(__UpperCAmelCase    ,			1    ,			dim=0)
      a						:		str											=      []
      a						:		Any											=      []
      for feature in features:
        a						:		Dict											=      self.decode_prefix(feature.to(__UpperCAmelCase))  # back to the clip feature
        # Only support beam search for now
        a							,		a						:		Union[str, Any]											=      self.generate_beam(
            input_embeds=__UpperCAmelCase    ,			device=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase)
        generated_tokens.append(output_tokens[0])
        generated_seq_lengths.append(seq_lengths[0])
      a						:		Optional[Any]											=      torch.stack(__UpperCAmelCase)
      a						:		Optional[int]											=      torch.stack(__UpperCAmelCase)
      return generated_tokens, generated_seq_lengths






    @torch.no_grad()
    def      __snake_case							( self    :   Optional[int]    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   Union[str, Any]=None    ,			__UpperCAmelCase    :   str=None    ,			__UpperCAmelCase    :   int = 5    ,			__UpperCAmelCase    :   int = 67    ,			__UpperCAmelCase    :   float = 1.0    ,			__UpperCAmelCase    :   Optional[int] = None    ,			):
      a						:		Tuple											=      eos_token_id
      a						:		List[Any]											=      None
      a						:		Optional[Any]											=      None
      a						:		List[Any]											=      torch.ones(__UpperCAmelCase    ,			device=__UpperCAmelCase    ,			dtype=torch.int)
      a						:		List[Any]											=      torch.zeros(__UpperCAmelCase    ,			device=__UpperCAmelCase    ,			dtype=torch.bool)

      if input_embeds is not None:
        a						:		int											=      input_embeds
      else:
        a						:		Tuple											=      self.transformer.transformer.wte(__UpperCAmelCase)

      for i in range(__UpperCAmelCase):
        a						:		Any											=      self.transformer(inputs_embeds=__UpperCAmelCase)
        a						:		Dict											=      outputs.logits
        a						:		Optional[Any]											=      logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
        a						:		str											=      logits.softmax(-1).log()

        if scores is None:
          a							,		a						:		Optional[int]											=      logits.topk(__UpperCAmelCase    ,			-1)
          a						:		int											=      generated.expand(__UpperCAmelCase    ,			*generated.shape[1:])
          a							,		a						:		Dict											=      next_tokens.permute(1    ,			0), scores.squeeze(0)
          if tokens is None:
            a						:		Union[str, Any]											=      next_tokens
          else:
            a						:		Optional[Any]											=      tokens.expand(__UpperCAmelCase    ,			*tokens.shape[1:])
            a						:		List[Any]											=      torch.cat((tokens, next_tokens)    ,			dim=1)
        else:
          a						:		List[str]											=      -float(np.inf)
          a						:		List[str]											=      0
          a						:		Optional[Any]											=      scores[:, None] + logits
          seq_lengths[~is_stopped] += 1
          a						:		Optional[Any]											=      scores_sum / seq_lengths[:, None]
          a							,		a						:		Optional[Any]											=      scores_sum_average.view(-1).topk(__UpperCAmelCase    ,			-1)
          a						:		Any											=      next_tokens // scores_sum.shape[1]
          a						:		Tuple											=      seq_lengths[next_tokens_source]
          a						:		Union[str, Any]											=      next_tokens % scores_sum.shape[1]
          a						:		Any											=      next_tokens.unsqueeze(1)
          a						:		Any											=      tokens[next_tokens_source]
          a						:		List[str]											=      torch.cat((tokens, next_tokens)    ,			dim=1)
          a						:		Dict											=      generated[next_tokens_source]
          a						:		int											=      scores_sum_average * seq_lengths
          a						:		List[Any]											=      is_stopped[next_tokens_source]

        a						:		str											=      self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]    ,			1    ,			-1)
        a						:		Optional[int]											=      torch.cat((generated, next_token_embed)    ,			dim=1)
        a						:		List[str]											=      is_stopped + next_tokens.eq(__UpperCAmelCase).squeeze()
        if is_stopped.all():
          break

      a						:		List[Any]											=      scores / seq_lengths
      a						:		List[str]											=      scores.argsort(descending=__UpperCAmelCase)
      # tokens tensors are already padded to max_seq_length
      a						:		Any											=      [tokens[i] for i in order]
      a						:		Dict											=      torch.stack(__UpperCAmelCase    ,			dim=0)
      a						:		Optional[Any]											=      torch.tensor([seq_lengths[i] for i in order]    ,			dtype=seq_lengths.dtype)
      return output_texts, seq_lengths










"""simple docstring"""







from __future__ import annotations

import queue






class 						_A  :







    """simple docstring"""




    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Union[str, Any]):
      a						:		Optional[Any]											=      data
      a						:		Optional[int]											=      None
      a						:		Union[str, Any]											=      None






def 					lowercase      (    )->				TreeNode:
  '''simple docstring'''







  print("\n********Press N to stop entering at any point of time********\n"	)
  a						:		int											=      input("Enter the value of the root node: "	).strip().lower()
  a						:		queue.Queue											=      queue.Queue()
  a						:		Tuple											=      TreeNode(int(A_	)	)
  q.put(A_	)
  while not q.empty():
    a						:		Union[str, Any]											=      q.get()
    a						:		Optional[int]											=      F'''Enter the left node of {node_found.data}: '''
    a						:		Union[str, Any]											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		List[str]											=      TreeNode(int(A_	)	)
    a						:		Any											=      left_node
    q.put(A_	)
    a						:		Dict											=      F'''Enter the right node of {node_found.data}: '''
    a						:		str											=      input(A_	).strip().lower() or "n"
    if check == "n":
      return tree_node
    a						:		Any											=      TreeNode(int(A_	)	)
    a						:		str											=      right_node
    q.put(A_	)
  raise


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  print(node.data  ,       end=","	)
  pre_order(node.left	)
  pre_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  in_order(node.left	)
  print(node.data  ,       end=","	)
  in_order(node.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  post_order(node.left	)
  post_order(node.right	)
  print(node.data  ,       end=","	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		str											=      q.get()
    print(node_dequeued.data  ,       end=","	)
    if node_dequeued.left:
      q.put(node_dequeued.left	)
    if node_dequeued.right:
      q.put(node_dequeued.right	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		queue.Queue											=      queue.Queue()
  q.put(A_	)
  while not q.empty():
    a						:		Any											=      []
    while not q.empty():
      a						:		Any											=      q.get()
      print(node_dequeued.data  ,       end=","	)
      if node_dequeued.left:
        list_.append(node_dequeued.left	)
      if node_dequeued.right:
        list_.append(node_dequeued.right	)
    print()
    for node in list_:
      q.put(A_	)


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		int											=      node
  while n or stack:
    while n:  # start from root node, find its left child
      print(n.data  ,       end=","	)
      stack.append(A_	)
      a						:		Tuple											=      n.left
    # end of while means current node doesn't have left child
    a						:		Optional[Any]											=      stack.pop()
    # start to traverse its right child
    a						:		str											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a						:		list[TreeNode]											=      []
  a						:		Union[str, Any]											=      node
  while n or stack:
    while n:
      stack.append(A_	)
      a						:		int											=      n.left
    a						:		str											=      stack.pop()
    print(n.data  ,       end=","	)
    a						:		Optional[int]											=      n.right


def 					lowercase      (    A_	)->				None:
  '''simple docstring'''







  if not isinstance(A_  ,       A_	) or not node:
    return
  a							,		a						:		Tuple											=      [], []
  a						:		Tuple											=      node
  stacka.append(A_	)
  while stacka:  # to find the reversed order of post order, store it in stack2
    a						:		Optional[Any]											=      stacka.pop()
    if n.left:
      stacka.append(n.left	)
    if n.right:
      stacka.append(n.right	)
    stacka.append(A_	)
  while stacka:  # pop up from stack2 will be the post order
    print(stacka.pop().data  ,       end=","	)


def 					lowercase      (    A_ = ""  ,       A_=50  ,       A_="*"	)->				str:
  '''simple docstring'''







  if not s:
    return "\n" + width * char
  a							,		a						:		Dict											=      divmod(width - len(A_	) - 2  ,       2	)
  return F'''{left * char} {s} {(left + extra) * char}'''


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(prompt("""Binary Tree Traversals"""))

 __lowercase    =				build_tree()
 print(prompt("""Pre Order Traversal"""))
 pre_order(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal"""))
 in_order(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal"""))
 post_order(node)
 print(prompt() + """\n""")

 print(prompt("""Level Order Traversal"""))
 level_order(node)
 print(prompt() + """\n""")

 print(prompt("""Actual Level Order Traversal"""))
 level_order_actual(node)
 print("""*""" * 50 + """\n""")

 print(prompt("""Pre Order Traversal - Iteration Version"""))
 pre_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""In Order Traversal - Iteration Version"""))
 in_order_iter(node)
 print(prompt() + """\n""")

 print(prompt("""Post Order Traversal - Iteration Version"""))
 post_order_iter(node)
 print(prompt())






