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


SCREAMING_SNAKE_CASE :	List[Any]							     =		get_logger(__name__)



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 os.makedirs(_A							,				exist_ok=_A					)
 with FSDP.state_dict_type(
     _A							,				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(_A							,				_A					)
   if accelerator.process_index == 0:
    logger.info(f"""Saving model to {output_model_file}"""					)
    torch.save(_A							,				_A					)
    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(_A							,				_A					)
   logger.info(f"""Saving model to {output_model_file}"""					)
   torch.save(_A							,				_A					)
   logger.info(f"""Model saved to {output_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __a       =  os.path.join(_A							,				f"""{MODEL_NAME}_{model_index}"""					)
   os.makedirs(_A							,				exist_ok=_A					)
   logger.info(f"""Saving model to {ckpt_dir}"""					)
   __a       =  {"model": state_dict}

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



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     _A							,				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(_A					) != 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(_A							,				_A					)
   logger.info(f"""Loading model from {input_model_file}"""					)
   __a       =  torch.load(_A					)
   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(_A							,				_A					)
   logger.info(f"""Loading model from {input_model_file}"""					)
   __a       =  torch.load(_A					)
   logger.info(f"""Model loaded from {input_model_file}"""					)
  elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
   __a       =  (
       os.path.join(_A							,				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=_A							,				storage_reader=dist_cp.FileSystemReader(_A					)							,				planner=DefaultLoadPlanner()							,				)
   __a       =  state_dict["model"]
   logger.info(f"""Model loaded from {ckpt_dir}"""					)
  model.load_state_dict(_A					)



def   __A		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 os.makedirs(_A							,				exist_ok=_A					)
 with FSDP.state_dict_type(
     _A							,				fsdp_plugin.state_dict_type							,				fsdp_plugin.state_dict_config							,				fsdp_plugin.optim_state_dict_config					):
  __a       =  FSDP.optim_state_dict(_A							,				_A					)
  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(_A							,				_A					)
    logger.info(f"""Saving Optimizer state to {output_optimizer_file}"""					)
    torch.save(_A							,				_A					)
    logger.info(f"""Optimizer state saved in {output_optimizer_file}"""					)
  else:
   __a       =  os.path.join(_A							,				f"""{OPTIMIZER_NAME}_{optimizer_index}"""					)
   os.makedirs(_A							,				exist_ok=_A					)
   logger.info(f"""Saving Optimizer state to {ckpt_dir}"""					)
   dist_cp.save_state_dict(
       state_dict={"optimizer": optim_state}							,				storage_writer=dist_cp.FileSystemWriter(_A					)							,				planner=DefaultSavePlanner()							,				)
   logger.info(f"""Optimizer state saved in {ckpt_dir}"""					)






def   __A		(			_A							,				_A							,				_A							,				_A							,				_A							,				_A=0					):



 """simple docstring"""


 accelerator.wait_for_everyone()
 with FSDP.state_dict_type(
     _A							,				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(_A							,				_A					)
   logger.info(f"""Loading Optimizer state from {input_optimizer_file}"""					)
   __a       =  torch.load(_A					)
   logger.info(f"""Optimizer state loaded from {input_optimizer_file}"""					)
  else:
   __a       =  (
       os.path.join(_A							,				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(_A					)							,				)
   __a       =  optim_state["optimizer"]
   logger.info(f"""Optimizer loaded from {ckpt_dir}"""					)
  __a       =  FSDP.optim_state_dict_to_load(_A							,				_A							,				_A					)
  optimizer.load_state_dict(_A					)



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
  import jax.numpy as jnp

  from transformers import FlaxXLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_flax
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base"       )
   __a       =  AutoTokenizer.from_pretrained("xlm-roberta-base"       )
   __a       =  "The dog is cute and lives in the garden house"
   __a       =  jnp.array([tokenizer.encode(__SCREAMING_SNAKE_CASE       )]       )

   __a       =  (1, 12, 7_68)  # batch_size, sequence_length, embedding_vector_dim
   __a       =  jnp.array(
       [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]       )

   __a       =  model(__SCREAMING_SNAKE_CASE       )["last_hidden_state"]
   self.assertEqual(output.shape     ,							__SCREAMING_SNAKE_CASE       )
   # compare the actual values for a slice of last dim
   self.assertTrue(jnp.allclose(output[:, :, -1]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-3       )       )


