




import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(">=", FSDP_PYTORCH_VERSION):
   import torch.distributed.checkpoint as dist_cp
   from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
   from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
   from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
   from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


lowercase__				:      List[Any]	   = get_logger(__name__)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					List[Any]:
     os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)
     with FSDP.state_dict_type(
         __UpperCamelCase		, fsdp_plugin.state_dict_type		, fsdp_plugin.state_dict_config		, fsdp_plugin.optim_state_dict_config):
          a     =				model.state_dict()
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               a     =				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               if accelerator.process_index == 0:
                    logger.info(f'''Saving model to {output_model_file}''')
                    torch.save(__UpperCamelCase		, __UpperCamelCase)
                    logger.info(f'''Model saved to {output_model_file}''')
          elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
               a     =				(
                   f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
                   if model_index == 0
                   else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
               )
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Saving model to {output_model_file}''')
               torch.save(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Model saved to {output_model_file}''')
          elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
               a     =				os.path.join(__UpperCamelCase		, f'''{MODEL_NAME}_{model_index}''')
               os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)
               logger.info(f'''Saving model to {ckpt_dir}''')
               a     =				{"model": state_dict}

               dist_cp.save_state_dict(
                   state_dict=__UpperCamelCase		, storage_writer=dist_cp.FileSystemWriter(__UpperCamelCase)		, planner=DefaultSavePlanner()		, )
               logger.info(f'''Model saved to {ckpt_dir}''')

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					Optional[int]:
     accelerator.wait_for_everyone()
     with FSDP.state_dict_type(
         __UpperCamelCase		, fsdp_plugin.state_dict_type		, fsdp_plugin.state_dict_config		, fsdp_plugin.optim_state_dict_config):
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               if type(__UpperCamelCase) != FSDP and accelerator.process_index != 0:
                    if not fsdp_plugin.sync_module_states:
                         raise ValueError(
                             "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
                             "initializing FSDP object")
                    return
               a     =				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading model from {input_model_file}''')
               a     =				torch.load(__UpperCamelCase)
               logger.info(f'''Model loaded from {input_model_file}''')
          elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
               a     =				(
                   f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
                   if model_index == 0
                   else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
               )
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading model from {input_model_file}''')
               a     =				torch.load(__UpperCamelCase)
               logger.info(f'''Model loaded from {input_model_file}''')
          elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
               a     =				(
                   os.path.join(__UpperCamelCase		, f'''{MODEL_NAME}_{model_index}''')
                   if f'''{MODEL_NAME}''' not in input_dir
                   else input_dir
               )
               logger.info(f'''Loading model from {ckpt_dir}''')
               a     =				{"model": model.state_dict()}
               dist_cp.load_state_dict(
                   state_dict=__UpperCamelCase		, storage_reader=dist_cp.FileSystemReader(__UpperCamelCase)		, planner=DefaultLoadPlanner()		, )
               a     =				state_dict["model"]
               logger.info(f'''Model loaded from {ckpt_dir}''')
          model.load_state_dict(__UpperCamelCase)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					List[Any]:
     os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)
     with FSDP.state_dict_type(
         __UpperCamelCase		, fsdp_plugin.state_dict_type		, fsdp_plugin.state_dict_config		, fsdp_plugin.optim_state_dict_config):
          a     =				FSDP.optim_state_dict(__UpperCamelCase		, __UpperCamelCase)
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               if accelerator.process_index == 0:
                    a     =				(
                        f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
                    )
                    a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
                    logger.info(f'''Saving Optimizer state to {output_optimizer_file}''')
                    torch.save(__UpperCamelCase		, __UpperCamelCase)
                    logger.info(f'''Optimizer state saved in {output_optimizer_file}''')
          else:
               a     =				os.path.join(__UpperCamelCase		, f'''{OPTIMIZER_NAME}_{optimizer_index}''')
               os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)
               logger.info(f'''Saving Optimizer state to {ckpt_dir}''')
               dist_cp.save_state_dict(
                   state_dict={"optimizer": optim_state}		, storage_writer=dist_cp.FileSystemWriter(__UpperCamelCase)		, planner=DefaultSavePlanner()		, )
               logger.info(f'''Optimizer state saved in {ckpt_dir}''')





def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					Optional[Any]:
     accelerator.wait_for_everyone()
     with FSDP.state_dict_type(
         __UpperCamelCase		, fsdp_plugin.state_dict_type		, fsdp_plugin.state_dict_config		, fsdp_plugin.optim_state_dict_config):
          if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
               a     =				None
               # below check should work but currently it isn't working (mostly opytorch issue),
               # in the meantime disabling it at the cost of excess memory usage
               # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
               a     =				(
                   f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
               )
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading Optimizer state from {input_optimizer_file}''')
               a     =				torch.load(__UpperCamelCase)
               logger.info(f'''Optimizer state loaded from {input_optimizer_file}''')
          else:
               a     =				(
                   os.path.join(__UpperCamelCase		, f'''{OPTIMIZER_NAME}_{optimizer_index}''')
                   if f'''{OPTIMIZER_NAME}''' not in input_dir
                   else input_dir
               )
               logger.info(f'''Loading Optimizer from {ckpt_dir}''')
               a     =				load_sharded_optimizer_state_dict(
                   model_state_dict=model.state_dict()		, optimizer_key="optimizer"		, storage_reader=dist_cp.FileSystemReader(__UpperCamelCase)		, )
               a     =				optim_state["optimizer"]
               logger.info(f'''Optimizer loaded from {ckpt_dir}''')
          a     =				FSDP.optim_state_dict_to_load(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)
          optimizer.load_state_dict(__UpperCamelCase)







import argparse

import torch

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


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     if k.startswith("encoder"):
          a     =				k.replace(".attn"		, ".self_attn")
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "final_layer_norm")
     elif k.startswith("decoder"):
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "encoder_attn_layer_norm")
          a     =				k.replace("norm3"		, "final_layer_norm")
     return k

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "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     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
   lowercase__				:      int	   = 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__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

