






"""simple docstring"""
import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


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

lowerCamelCase_					=      [
    ['''attention''', '''attn'''],
    ['''encoder_attention''', '''encoder_attn'''],
    ['''q_lin''', '''q_proj'''],
    ['''k_lin''', '''k_proj'''],
    ['''v_lin''', '''v_proj'''],
    ['''out_lin''', '''out_proj'''],
    ['''norm_embeddings''', '''layernorm_embedding'''],
    ['''position_embeddings''', '''embed_positions'''],
    ['''embeddings''', '''embed_tokens'''],
    ['''ffn.lin''', '''fc'''],
]

def 				snake_case      (   A__						):
			if k == "embeddings.weight":
						return "shared.weight"

			for parlai_name, hf_name in PATTERNS:
						UpperCAmelCase_     : Optional[Any]			     =					k.replace(A__	,A__						)

			if k.startswith("encoder"						):
						UpperCAmelCase_     : List[Any]			     =					k.replace(".attn"	,".self_attn"						)
						UpperCAmelCase_     : Union[str, Any]			     =					k.replace("norm1"	,"self_attn_layer_norm"						)
						UpperCAmelCase_     : str			     =					k.replace("norm2"	,"final_layer_norm"						)
			elif k.startswith("decoder"						):
						UpperCAmelCase_     : Union[str, Any]			     =					k.replace("norm1"	,"self_attn_layer_norm"						)
						UpperCAmelCase_     : Optional[int]			     =					k.replace("norm2"	,"encoder_attn_layer_norm"						)
						UpperCAmelCase_     : List[str]			     =					k.replace("norm3"	,"final_layer_norm"						)
			return k

def 				snake_case      (   A__						):
			UpperCAmelCase_     : List[Any]			     =					[
			    "model.encoder.layernorm_embedding.weight",
			    "model.encoder.layernorm_embedding.bias",
			    "model.decoder.layernorm_embedding.weight",
			    "model.decoder.layernorm_embedding.bias",
			]
			for k in keys:
						UpperCAmelCase_     : Tuple			     =					sd.pop(A__						)
						UpperCAmelCase_     : Any			     =					k.replace("layernorm_embedding"	,"layer_norm"						)
						assert new_k not in sd
						UpperCAmelCase_     : Any			     =					v


lowerCamelCase_					=      ['''START''']

@torch.no_grad()
def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Optional[Any]			     =					torch.load(A__	,map_location="cpu"						)
			UpperCAmelCase_     : Dict			     =					model["model"]
			UpperCAmelCase_     : int			     =					BlenderbotConfig.from_json_file(A__						)
			UpperCAmelCase_     : Tuple			     =					BlenderbotForConditionalGeneration(A__						)
			UpperCAmelCase_     : str			     =					m.model.state_dict().keys()
			UpperCAmelCase_     : Any			     =					[]
			UpperCAmelCase_     : List[str]			     =					{}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						UpperCAmelCase_     : int			     =					rename_state_dict_key(A__						)
						if new_k not in valid_keys:
									failures.append([k, new_k]						)
						else:
									UpperCAmelCase_     : Any			     =					v
			if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
						rename_layernorm_keys(A__						)
			m.model.load_state_dict(A__	,strict=A__						)
			m.half()
			m.save_pretrained(A__						)


if __name__ == "__main__":
			lowerCamelCase_					=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('''--src_path''', type=str, help='''like blenderbot-model.bin''')
			parser.add_argument('''--save_dir''', default='''hf_blenderbot''', type=str, help='''Where to save converted model.''')
			parser.add_argument(
			    '''--hf_config_json''', default='''blenderbot-3b-config.json''', type=str, help='''Path to config to use'''
			)
			lowerCamelCase_					=      parser.parse_args()
			convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)











"""simple docstring"""
from __future__ import annotations







class 			UpperCamelCase_       :





	def __init__(      self					: Any	,	lowerCAmelCase_					: int						)						->			None:
				UpperCAmelCase_     : Any			     =					data
				UpperCAmelCase_     : Node | None			     =					None
				UpperCAmelCase_     : Node | None			     =					None





def 				snake_case      (   A__						):  # In Order traversal of the tree
			if tree:
						display(tree.left						)
						print(tree.data						)
						display(tree.right						)

def 				snake_case      (   A__						):
			return 1 + max(depth_of_tree(tree.left						)	,depth_of_tree(tree.right						)						) if tree else 0

def 				snake_case      (   A__						):
			if not tree:
						return True
			if tree.left and tree.right:
						return is_full_binary_tree(tree.left						) and is_full_binary_tree(tree.right						)
			else:
						return not tree.left and not tree.right

def 				snake_case      (   ):  # Main function for testing.
			UpperCAmelCase_     : List[str]			     =					Node(1						)
			UpperCAmelCase_     : Any			     =					Node(2						)
			UpperCAmelCase_     : Optional[Any]			     =					Node(3						)
			UpperCAmelCase_     : Union[str, Any]			     =					Node(4						)
			UpperCAmelCase_     : int			     =					Node(5						)
			UpperCAmelCase_     : Optional[int]			     =					Node(6						)
			UpperCAmelCase_     : Any			     =					Node(7						)
			UpperCAmelCase_     : List[str]			     =					Node(8						)
			UpperCAmelCase_     : List[Any]			     =					Node(9						)

			print(is_full_binary_tree(A__						)						)
			print(depth_of_tree(A__						)						)
			print("Tree is: "						)
			display(A__						)


if __name__ == "__main__":
			main()



