





import argparse

import torch

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


logging.set_verbosity_info()
__lowercase       :Any       =  logging.get_logger(__name__)

__lowercase       :List[Any]       =  [
    ["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       UpperCAmelCase							(     _lowerCamelCase      :  List[Any]     ):





						'''simple docstring'''


						if k == "embeddings.weight":
												return "shared.weight"

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

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







def       UpperCAmelCase							(     _lowerCamelCase      :  Union[str, Any]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				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:
												SCREAMING_SNAKE_CASE__     :				Optional[int]   =					sd.pop(_lowerCamelCase     )
												SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					k.replace("layernorm_embedding"			,   "layer_norm"     )
												assert new_k not in sd
												SCREAMING_SNAKE_CASE__     :				str   =					v


__lowercase       :List[str]       =  ["START"]







@torch.no_grad()
def       UpperCAmelCase							(     _lowerCamelCase      :  Tuple			,   _lowerCamelCase      :  str			,   _lowerCamelCase      :  List[str]     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				List[Any]   =					torch.load(_lowerCamelCase			,   map_location="cpu"     )
						SCREAMING_SNAKE_CASE__     :				Union[str, Any]   =					model["model"]
						SCREAMING_SNAKE_CASE__     :				List[str]   =					BlenderbotConfig.from_json_file(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					BlenderbotForConditionalGeneration(_lowerCamelCase     )
						SCREAMING_SNAKE_CASE__     :				str   =					m.model.state_dict().keys()
						SCREAMING_SNAKE_CASE__     :				Dict   =					[]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					{}
						for k, v in sd.items():
												if k in IGNORE_KEYS:
																		continue

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


if __name__ == "__main__":
				__lowercase       :Dict       =  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"
				)
				__lowercase       :List[str]       =  parser.parse_args()
				convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)










def       UpperCAmelCase							(     _lowerCamelCase      :  int = 4_000_000     ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__     :				Any   =					[0, 1]
						SCREAMING_SNAKE_CASE__     :				List[Any]   =					0
						while fib[i] <= n:
												fib.append(fib[i] + fib[i + 1]     )
												if fib[i + 2] > n:
																		break
												i += 1
						SCREAMING_SNAKE_CASE__     :				Optional[Any]   =					0
						for j in range(len(_lowerCamelCase     ) - 1     ):
												if fib[j] % 2 == 0:
																		total += fib[j]

						return total


if __name__ == "__main__":
				print(f"{solution() = }")