






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


snake_case__   :						List[str]													=   get_logger(__name__)






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

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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=0):
   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       __lowercase     ,   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(__lowercase) != 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
         UpperCamelCase_						=  f"""{MODEL_NAME}.bin""" if model_index == 0 else f"""{MODEL_NAME}_{model_index}.bin"""
         UpperCamelCase_						=  os.path.join(__lowercase     ,   __lowercase)
         logger.info(f"""Loading model from {input_model_file}""")
         UpperCamelCase_						=  torch.load(__lowercase)
         logger.info(f"""Model loaded from {input_model_file}""")
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCamelCase_						=  (
             f"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else f"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCamelCase_						=  os.path.join(__lowercase     ,   __lowercase)
         logger.info(f"""Loading model from {input_model_file}""")
         UpperCamelCase_						=  torch.load(__lowercase)
         logger.info(f"""Model loaded from {input_model_file}""")
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCamelCase_						=  (
             os.path.join(__lowercase     ,   f"""{MODEL_NAME}_{model_index}""")
             if f"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(f"""Loading model from {ckpt_dir}""")
         UpperCamelCase_						=  {'model': model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=__lowercase     ,   storage_reader=dist_cp.FileSystemReader(__lowercase)     ,   planner=DefaultLoadPlanner()     ,   )
         UpperCamelCase_						=  state_dict['model']
         logger.info(f"""Model loaded from {ckpt_dir}""")
      model.load_state_dict(__lowercase)






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






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase=0):
   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       __lowercase     ,   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:
         UpperCamelCase_						=  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:
         UpperCamelCase_						=  (
             f"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else f"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         UpperCamelCase_						=  os.path.join(__lowercase     ,   __lowercase)
         logger.info(f"""Loading Optimizer state from {input_optimizer_file}""")
         UpperCamelCase_						=  torch.load(__lowercase)
         logger.info(f"""Optimizer state loaded from {input_optimizer_file}""")
      else:
         UpperCamelCase_						=  (
             os.path.join(__lowercase     ,   f"""{OPTIMIZER_NAME}_{optimizer_index}""")
             if f"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(f"""Loading Optimizer from {ckpt_dir}""")
         UpperCamelCase_						=  load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()     ,   optimizer_key='optimizer'     ,   storage_reader=dist_cp.FileSystemReader(__lowercase)     ,   )
         UpperCamelCase_						=  optim_state['optimizer']
         logger.info(f"""Optimizer loaded from {ckpt_dir}""")
      UpperCamelCase_						=  FSDP.optim_state_dict_to_load(__lowercase     ,   __lowercase     ,   __lowercase)
      optimizer.load_state_dict(__lowercase)













import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple							)     ->  List[str]:
   __UpperCamelCase	:			List[str]					=					R"""\w+[.]\d+"""
   __UpperCamelCase	:			Optional[int]					=					re.findall(__lowerCAmelCase					,							__lowerCAmelCase							)
   for pat in pats:
      __UpperCamelCase	:			str					=					key.replace(__lowerCAmelCase					,							"""_""".join(pat.split("""."""							)							)							)
   return key


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  str:
   __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
   if (
       any("""norm""" in str_ for str_ in pt_tuple_key							)
       and (pt_tuple_key[-1] == "bias")
       and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
       and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
   ):
      __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor
   elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
      __UpperCamelCase	:			Dict					=					pt_tuple_key[:-1] + ("""scale""",)
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
      __UpperCamelCase	:			Any					=					pt_tuple_key[:-1] + ("""embedding""",)
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
      __UpperCamelCase	:			Dict					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight":
      __UpperCamelCase	:			int					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			List[Any]					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Dict					,							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Optional[int]=42							)     ->  int:
   # Step 1: Convert pytorch tensor to numpy
   __UpperCamelCase	:			Dict					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   # Step 2: Since the model is stateless, get random Flax params
   __UpperCamelCase	:			str					=					flax_model.init_weights(PRNGKey(__lowerCAmelCase							)							)

   __UpperCamelCase	:			Union[str, Any]					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			List[str]					=					{}

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			str					=					rename_key(__lowerCAmelCase							)
      __UpperCamelCase	:			Any					=					tuple(renamed_pt_key.split("""."""							)							)

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Tuple					=					rename_key_and_reshape_tensor(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # also add unexpected weight so that warning is thrown
      __UpperCamelCase	:			Any					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)



