




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())












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
        '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
        '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
        '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
    }
    for old_key, new_key in keys_to_rename.items():
        if old_key in sd:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

    __UpperCamelCase  		=						list(sd.keys()      )
    for key in keys:
        if ".qkv_proj." in key:
            __UpperCamelCase  		=						sd[key]
            # We split QKV in separate Q,K,V

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						value.shape[0]
            assert depth % 3 == 0
            # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
            # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

    # Check results
    Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
    model.save_pretrained(lowercase_      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--fairseq_path",
     type=str,
     help=(
         "path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"
         " https://huggingface.co/models?other=opt_metasq"
     ),
 )
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--hf_config", default=None, type=str, help="Define HF config.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






