



"""simple docstring"""

import argparse

import torch

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


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

lowercase__			   =  [
    ["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       __magic_name__    (   _lowerCamelCase				: Union[str, Any]					):
							if k == "embeddings.weight":
														return "shared.weight"

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

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





def       __magic_name__    (   _lowerCamelCase				: Union[str, Any]					):
							__a		:		Optional[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:
														__a		:		Union[str, Any]									=       sd.pop(_lowerCamelCase					)
														__a		:		List[Any]									=       k.replace("""layernorm_embedding"""  ,       """layer_norm"""					)
														assert new_k not in sd
														__a		:		Union[str, Any]									=       v


lowercase__			   =  ["START"]





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

														__a		:		List[str]									=       rename_state_dict_key(_lowerCamelCase					)
														if new_k not in valid_keys:
																					failures.append([k, new_k]					)
														else:
																					__a		:		List[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__			   =  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__			   =  parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)






"""simple docstring"""

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def       __magic_name__    (   _lowerCamelCase				: int  ,       _lowerCamelCase				: Optional[Any]=False					):
							__a		:		Dict									=       OmegaConf.load(_lowerCamelCase					)
							if display:
														print(yaml.dump(OmegaConf.to_container(_lowerCamelCase					)					)					)
							return config





def       __magic_name__    (   _lowerCamelCase				: str  ,       _lowerCamelCase				: Union[str, Any]=None  ,       _lowerCamelCase				: int=None					):
							if conf_path is None:
														__a		:		str									=       """./model_checkpoints/vqgan_only.yaml"""
							__a		:		List[Any]									=       load_config(_lowerCamelCase  ,       display=_lowerCamelCase					)
							__a		:		Dict									=       VQModel(**config.model.params					)
							if ckpt_path is None:
														__a		:		List[Any]									=       """./model_checkpoints/vqgan_only.pt"""
							__a		:		Tuple									=       torch.load(_lowerCamelCase  ,       map_location=_lowerCamelCase					)
							if ".ckpt" in ckpt_path:
														__a		:		List[str]									=       sd["""state_dict"""]
							model.load_state_dict(_lowerCamelCase  ,       strict=_lowerCamelCase					)
							model.to(_lowerCamelCase					)
							del sd
							return model





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: List[str]					):
							__a							,     __a							,     __a		:		Tuple									=       model.encode(_lowerCamelCase					)
							print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''					)
							__a		:		Union[str, Any]									=       model.decode(_lowerCamelCase					)
							return xrec





def       __magic_name__    (   _lowerCamelCase				: Optional[int]  ,       _lowerCamelCase				: Union[str, Any]=False					):
							__a							,     __a		:		Optional[Any]									=       string.rsplit("""."""  ,       1					)
							if reload:
														__a		:		Optional[Any]									=       importlib.import_module(_lowerCamelCase					)
														importlib.reload(_lowerCamelCase					)
							return getattr(importlib.import_module(_lowerCamelCase  ,       package=_lowerCamelCase					)  ,       cls					)





def       __magic_name__    (   _lowerCamelCase				: Any					):
							if "target" not in config:
														raise KeyError("""Expected key `target` to instantiate."""					)
							return get_obj_from_str(config["""target"""]					)(**config.get("""params"""  ,       {}					)					)





def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Dict  ,       _lowerCamelCase				: int=True  ,       _lowerCamelCase				: int=True					):
							__a		:		Union[str, Any]									=       instantiate_from_config(_lowerCamelCase					)
							if sd is not None:
														model.load_state_dict(_lowerCamelCase					)
							if gpu:
														model.cuda()
							if eval_mode:
														model.eval()
							return {"model": model}







def       __magic_name__    (   _lowerCamelCase				: Any  ,       _lowerCamelCase				: Union[str, Any]  ,       _lowerCamelCase				: str  ,       _lowerCamelCase				: int					):
							# load the specified checkpoint
							if ckpt:
														__a		:		List[str]									=       torch.load(_lowerCamelCase  ,       map_location="""cpu"""					)
														__a		:		Any									=       pl_sd["""global_step"""]
														print(F'''loaded model from global step {global_step}.'''					)
							else:
														__a		:		List[Any]									=       {"""state_dict""": None}
														__a		:		Any									=       None
							__a		:		Union[str, Any]									=       load_model_from_config(config.model  ,       pl_sd["""state_dict"""]  ,       gpu=_lowerCamelCase  ,       eval_mode=_lowerCamelCase					)["""model"""]
							return model, global_step

