



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


UpperCAmelCase__   =       get_logger(__name__)





def 							A	(     _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   Tuple							,   _UpperCAmelCase				:   str							,   _UpperCAmelCase				:   Optional[Any]=0    )				-> List[Any]:







			'''simple docstring'''
			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    ):
						_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(_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:
									_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(_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:
									_UpperCAmelCase		       =   os.path.join(_UpperCAmelCase							,   F"{MODEL_NAME}_{model_index}"    )
									os.makedirs(_UpperCAmelCase							,   exist_ok=_UpperCAmelCase    )
									logger.info(F"Saving model to {ckpt_dir}"    )
									_UpperCAmelCase		       =   {'''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 							A	(     _UpperCAmelCase				:   Dict							,   _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   Union[str, Any]=0    )				-> Optional[int]:







			'''simple docstring'''
			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
									_UpperCAmelCase		       =   F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
									_UpperCAmelCase		       =   os.path.join(_UpperCAmelCase							,   _UpperCAmelCase    )
									logger.info(F"Loading model from {input_model_file}"    )
									_UpperCAmelCase		       =   torch.load(_UpperCAmelCase    )
									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(_UpperCAmelCase							,   _UpperCAmelCase    )
									logger.info(F"Loading model from {input_model_file}"    )
									_UpperCAmelCase		       =   torch.load(_UpperCAmelCase    )
									logger.info(F"Model loaded from {input_model_file}"    )
						elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
									_UpperCAmelCase		       =   (
									    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}"    )
									_UpperCAmelCase		       =   {'''model''': model.state_dict()}
									dist_cp.load_state_dict(
									    state_dict=_UpperCAmelCase							,   storage_reader=dist_cp.FileSystemReader(_UpperCAmelCase    )							,   planner=DefaultLoadPlanner()							,   )
									_UpperCAmelCase		       =   state_dict['''model''']
									logger.info(F"Model loaded from {ckpt_dir}"    )
						model.load_state_dict(_UpperCAmelCase    )





def 							A	(     _UpperCAmelCase				:   int							,   _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   List[Any]							,   _UpperCAmelCase				:   Optional[int]							,   _UpperCAmelCase				:   str=0    )				-> List[Any]:







			'''simple docstring'''
			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    ):
						_UpperCAmelCase		       =   FSDP.optim_state_dict(_UpperCAmelCase							,   _UpperCAmelCase    )
						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(_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:
									_UpperCAmelCase		       =   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 							A	(     _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   Union[str, Any]							,   _UpperCAmelCase				:   Any							,   _UpperCAmelCase				:   Optional[int]=0    )				-> int:







			'''simple docstring'''
			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:
									_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(_UpperCAmelCase							,   _UpperCAmelCase    )
									logger.info(F"Loading Optimizer state from {input_optimizer_file}"    )
									_UpperCAmelCase		       =   torch.load(_UpperCAmelCase    )
									logger.info(F"Optimizer state loaded from {input_optimizer_file}"    )
						else:
									_UpperCAmelCase		       =   (
									    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}"    )
									_UpperCAmelCase		       =   load_sharded_optimizer_state_dict(
									    model_state_dict=model.state_dict()							,   optimizer_key='optimizer'							,   storage_reader=dist_cp.FileSystemReader(_UpperCAmelCase    )							,   )
									_UpperCAmelCase		       =   optim_state['''optimizer''']
									logger.info(F"Optimizer loaded from {ckpt_dir}"    )
						_UpperCAmelCase		       =   FSDP.optim_state_dict_to_load(_UpperCAmelCase							,   _UpperCAmelCase							,   _UpperCAmelCase    )
						optimizer.load_state_dict(_UpperCAmelCase    )

'''simple docstring'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

