




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


a_   		=     logging.get_logger(__name__)


a_   		=     {
    "Salesforce/codegen-350M-nl": "https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json",
    "Salesforce/codegen-350M-multi": "https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json",
    "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json",
    "Salesforce/codegen-2B-nl": "https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json",
    "Salesforce/codegen-2B-multi": "https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json",
    "Salesforce/codegen-2B-mono": "https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json",
    "Salesforce/codegen-6B-nl": "https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json",
    "Salesforce/codegen-6B-multi": "https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json",
    "Salesforce/codegen-6B-mono": "https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json",
    "Salesforce/codegen-16B-nl": "https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json",
    "Salesforce/codegen-16B-multi": "https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json",
    "Salesforce/codegen-16B-mono": "https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json",
}

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: List[Any]    =					"codegen"
      lowerCAmelCase__			: Optional[int]    =					{
          "max_position_embeddings": "n_positions",
          "hidden_size": "n_embd",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }







      def __init__(     self					: Tuple ,       snake_case					: Dict=50400 ,       snake_case					: Union[str, Any]=2048 ,       snake_case					: List[str]=2048 ,       snake_case					: List[Any]=4096 ,       snake_case					: Union[str, Any]=28 ,       snake_case					: Any=16 ,       snake_case					: Optional[int]=64 ,       snake_case					: str=None ,       snake_case					: Union[str, Any]="gelu_new" ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: Any=0.0 ,       snake_case					: Tuple=0.0 ,       snake_case					: Tuple=1E-5 ,       snake_case					: Dict=0.02 ,       snake_case					: Dict=True ,       snake_case					: Dict=50256 ,       snake_case					: Union[str, Any]=50256 ,       snake_case					: str=False ,       **snake_case					: str ,       ):
          __UpperCamelCase  		=						vocab_size
          __UpperCamelCase  		=						n_ctx
          __UpperCamelCase  		=						n_positions
          __UpperCamelCase  		=						n_embd
          __UpperCamelCase  		=						n_layer
          __UpperCamelCase  		=						n_head
          __UpperCamelCase  		=						n_inner
          __UpperCamelCase  		=						rotary_dim
          __UpperCamelCase  		=						activation_function
          __UpperCamelCase  		=						resid_pdrop
          __UpperCamelCase  		=						embd_pdrop
          __UpperCamelCase  		=						attn_pdrop
          __UpperCamelCase  		=						layer_norm_epsilon
          __UpperCamelCase  		=						initializer_range
          __UpperCamelCase  		=						use_cache

          __UpperCamelCase  		=						bos_token_id
          __UpperCamelCase  		=						eos_token_id

          super().__init__(
              bos_token_id=snake_case ,       eos_token_id=snake_case ,       tie_word_embeddings=snake_case ,       **snake_case				)







class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: PretrainedConfig ,       snake_case					: str = "default" ,       snake_case					: List[PatchingSpec] = None ,       snake_case					: bool = False ,       ):
          super().__init__(snake_case ,       task=snake_case ,       patching_specs=snake_case ,       use_past=snake_case				)
          if not getattr(self._config ,       '''pad_token_id''' ,       snake_case				):
              # TODO: how to do that better?
              __UpperCamelCase  		=						0







      @property
      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}				)
          if self.use_past:
              self.fill_with_past_key_values_(snake_case ,       direction='''inputs'''				)
              __UpperCamelCase  		=						{0: '''batch''', 1: '''past_sequence + sequence'''}
          else:
              __UpperCamelCase  		=						{0: '''batch''', 1: '''sequence'''}

          return common_inputs







      @property
      def       snake_case	(     self					: Union[str, Any]				):
          return self._config.n_layer







      @property
      def       snake_case	(     self					: Optional[Any]				):
          return self._config.n_head







      def       snake_case	(     self					: str ,       snake_case					: PreTrainedTokenizer ,       snake_case					: int = -1 ,       snake_case					: int = -1 ,       snake_case					: bool = False ,       snake_case					: Optional[TensorType] = None ,       ):
          __UpperCamelCase  		=						super(snake_case ,       self				).generate_dummy_inputs(
              snake_case ,       batch_size=snake_case ,       seq_length=snake_case ,       is_pair=snake_case ,       framework=snake_case				)

          # We need to order the input in the way they appears in the forward()
          __UpperCamelCase  		=						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

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

          __UpperCamelCase  		=						common_inputs['''attention_mask''']
          if self.use_past:
              __UpperCamelCase  		=						ordered_inputs['''attention_mask'''].dtype
              __UpperCamelCase  		=						torch.cat(
                  [ordered_inputs['''attention_mask'''], torch.ones(snake_case ,       snake_case ,       dtype=snake_case				)] ,       dim=1				)

          return ordered_inputs







      @property
      def       snake_case	(     self					: str				):
          return 13












from __future__ import annotations

import queue

class  _lowerCamelCase						:

      """simple docstring"""

      def __init__(     self					: Any ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						data
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						None







def 					__SCREAMING_SNAKE_CASE   (  )       ->	TreeNode:

    """simple docstring"""


    print('''\n********Press N to stop entering at any point of time********\n'''      )
    __UpperCamelCase  		=						input('''Enter the value of the root node: '''      ).strip().lower()
    __UpperCamelCase  		=						queue.Queue()
    __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						q.get()
        __UpperCamelCase  		=						F"Enter the left node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						left_node
        q.put(lowercase_      )
        __UpperCamelCase  		=						F"Enter the right node of {node_found.data}: "
        __UpperCamelCase  		=						input(lowercase_      ).strip().lower() or '''n'''
        if check == "n":
            return tree_node
        __UpperCamelCase  		=						TreeNode(int(lowercase_      )      )
        __UpperCamelCase  		=						right_node
        q.put(lowercase_      )
    raise




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						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 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						queue.Queue()
    q.put(lowercase_      )
    while not q.empty():
        __UpperCamelCase  		=						[]
        while not q.empty():
            __UpperCamelCase  		=						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(lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:  # start from root node, find its left child
            print(n.data					,     end=''','''      )
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        # end of while means current node doesn't have left child
        __UpperCamelCase  		=						stack.pop()
        # start to traverse its right child
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


    if not isinstance(lowercase_					,     lowercase_      ) or not node:
        return
    __UpperCamelCase  		=						[]
    __UpperCamelCase  		=						node
    while n or stack:
        while n:
            stack.append(lowercase_      )
            __UpperCamelCase  		=						n.left
        __UpperCamelCase  		=						stack.pop()
        print(n.data					,     end=''','''      )
        __UpperCamelCase  		=						n.right




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	None:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_ = ""					,     lowercase_=50					,     lowercase_="*"      )       ->	str:

    """simple docstring"""


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


if __name__ == "__main__":
 import doctest

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

 a_   		=     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())






