'''simple docstring'''




import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

__SCREAMING_SNAKE_CASE       :     Any     =  """bert-base-cased"""
__SCREAMING_SNAKE_CASE       :     Any     =  """fp16"""
__SCREAMING_SNAKE_CASE       :     Optional[Any]     =  """bf16"""
__SCREAMING_SNAKE_CASE       :     Dict     =  [FPaa, BFaa]

@require_fsdp
@require_cuda
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				Union[str, Any]       ):
             super().setUp()

             _UpperCAmelCase     :  Optional[int]							      =    dict(
                 ACCELERATE_USE_FSDP="true"						,					MASTER_ADDR="localhost"						,					MASTER_PORT="10999"						,					RANK="0"						,					LOCAL_RANK="0"						,					WORLD_SIZE="1"						,					)




      def      _A	(       self						:				Optional[Any]       ):
             from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

             for i, strategy in enumerate(A       ):
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  Any							      =    F"""{i + 1}"""
                    _UpperCAmelCase     :  str							      =    strategy
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  Any							      =    FullyShardedDataParallelPlugin()
                           self.assertEqual(fsdp_plugin.sharding_strategy						,					ShardingStrategy(i + 1       )       )




      def      _A	(       self						:				Any       ):
             from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

             for i, prefetch_policy in enumerate(A       ):
                    _UpperCAmelCase     :  Union[str, Any]							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  Optional[Any]							      =    prefetch_policy
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  Union[str, Any]							      =    FullyShardedDataParallelPlugin()
                           if prefetch_policy == "NO_PREFETCH":
                                  self.assertIsNone(fsdp_plugin.backward_prefetch       )
                           else:
                                  self.assertEqual(fsdp_plugin.backward_prefetch						,					BackwardPrefetch(i + 1       )       )




      def      _A	(       self						:				int       ):
             from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

             for i, state_dict_type in enumerate(A       ):
                    _UpperCAmelCase     :  Optional[int]							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  Tuple							      =    state_dict_type
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  List[str]							      =    FullyShardedDataParallelPlugin()
                           self.assertEqual(fsdp_plugin.state_dict_type						,					StateDictType(i + 1       )       )
                           if state_dict_type == "FULL_STATE_DICT":
                                  self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu       )
                                  self.assertTrue(fsdp_plugin.state_dict_config.ranka_only       )




      def      _A	(       self						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    AutoModel.from_pretrained(A       )
             for policy in FSDP_AUTO_WRAP_POLICY:
                    _UpperCAmelCase     :  Any							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  Optional[int]							      =    policy
                    if policy == "TRANSFORMER_BASED_WRAP":
                           _UpperCAmelCase     :  Optional[Any]							      =    "BertLayer"
                    elif policy == "SIZE_BASED_WRAP":
                           _UpperCAmelCase     :  Any							      =    "2000"
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  List[Any]							      =    FullyShardedDataParallelPlugin()
                           fsdp_plugin.set_auto_wrap_policy(A       )
                           if policy == "NO_WRAP":
                                  self.assertIsNone(fsdp_plugin.auto_wrap_policy       )
                           else:
                                  self.assertIsNotNone(fsdp_plugin.auto_wrap_policy       )

             _UpperCAmelCase     :  Any							      =    self.dist_env.copy()
             _UpperCAmelCase     :  Tuple							      =    "TRANSFORMER_BASED_WRAP"
             _UpperCAmelCase     :  Tuple							      =    "T5Layer"
             with mockenv_context(**A       ):
                    _UpperCAmelCase     :  Dict							      =    FullyShardedDataParallelPlugin()
                    with self.assertRaises(A       ) as cm:
                           fsdp_plugin.set_auto_wrap_policy(A       )
                    self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception       )       )

             _UpperCAmelCase     :  str							      =    self.dist_env.copy()
             _UpperCAmelCase     :  Optional[int]							      =    "SIZE_BASED_WRAP"
             _UpperCAmelCase     :  List[str]							      =    "0"
             with mockenv_context(**A       ):
                    _UpperCAmelCase     :  List[Any]							      =    FullyShardedDataParallelPlugin()
                    fsdp_plugin.set_auto_wrap_policy(A       )
                    self.assertIsNone(fsdp_plugin.auto_wrap_policy       )




      def      _A	(       self						:				List[str]       ):
             from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
             from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

             for mp_dtype in dtypes:
                    _UpperCAmelCase     :  str							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  str							      =    mp_dtype
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  int							      =    Accelerator()
                           if mp_dtype == "fp16":
                                  _UpperCAmelCase     :  Union[str, Any]							      =    torch.floataa
                           elif mp_dtype == "bf16":
                                  _UpperCAmelCase     :  Union[str, Any]							      =    torch.bfloataa
                           _UpperCAmelCase     :  Optional[int]							      =    MixedPrecision(param_dtype=A						,					reduce_dtype=A						,					buffer_dtype=A       )
                           self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy						,					A       )
                           if mp_dtype == FPaa:
                                  self.assertTrue(isinstance(accelerator.scaler						,					A       )       )
                           elif mp_dtype == BFaa:
                                  self.assertIsNone(accelerator.scaler       )
                           AcceleratorState._reset_state(A       )





      def      _A	(       self						:				Optional[Any]       ):
             from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

             for flag in [True, False]:
                    _UpperCAmelCase     :  str							      =    self.dist_env.copy()
                    _UpperCAmelCase     :  Dict							      =    str(A       ).lower()
                    with mockenv_context(**A       ):
                           _UpperCAmelCase     :  Tuple							      =    FullyShardedDataParallelPlugin()
                           self.assertEqual(fsdp_plugin.cpu_offload						,					CPUOffload(offload_params=A       )       )

@require_fsdp
@require_multi_gpu
@slow
class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def      _A	(       self						:				List[Any]       ):
             super().setUp()
             _UpperCAmelCase     :  Optional[int]							      =    0.82
             _UpperCAmelCase     :  int							      =    [
                 "fsdp_shard_grad_op_transformer_based_wrap",
                 "fsdp_full_shard_transformer_based_wrap",
             ]
             _UpperCAmelCase     :  Tuple							      =    {
                 "multi_gpu_fp16": 3200,
                 "fsdp_shard_grad_op_transformer_based_wrap_fp16": 2000,
                 "fsdp_full_shard_transformer_based_wrap_fp16": 1900,
                 # Disabling below test as it overwhelms the RAM memory usage
                 # on CI self-hosted runner leading to tests getting killed.
                 # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
             }
             _UpperCAmelCase     :  Tuple							      =    160
             _UpperCAmelCase     :  Any							      =    160

             _UpperCAmelCase     :  List[str]							      =    inspect.getfile(accelerate.test_utils       )
             _UpperCAmelCase     :  int							      =    os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "external_deps"]       )




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    os.path.join(self.test_scripts_folder						,					"test_performance.py"       )
             _UpperCAmelCase     :  Optional[int]							      =    ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
             for config in self.performance_configs:
                    _UpperCAmelCase     :  Tuple							      =    cmd.copy()
                    for i, strategy in enumerate(A       ):
                           if strategy.lower() in config:
                                  cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""       )
                                  break

                    if "fp32" in config:
                           cmd_config.append("--mixed_precision=no"       )
                    else:
                           cmd_config.append("--mixed_precision=fp16"       )

                    if "cpu_offload" in config:
                           cmd_config.append("--fsdp_offload_params=True"       )

                    for policy in FSDP_AUTO_WRAP_POLICY:
                           if policy.lower() in config:
                                  cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}"""       )
                                  break

                    if policy == "TRANSFORMER_BASED_WRAP":
                           cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"       )
                    elif policy == "SIZE_BASED_WRAP":
                           cmd_config.append("--fsdp_min_num_params=2000"       )

                    cmd_config.extend(
                        [
                            self.test_file_path,
                            F"""--output_dir={self.tmpdir}""",
                            F"""--performance_lower_bound={self.performance_lower_bound}""",
                        ]       )
                    with patch_environment(omp_num_threads=1       ):
                           execute_subprocess_async(A						,					env=os.environ.copy()       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Dict							      =    os.path.join(self.test_scripts_folder						,					"test_checkpointing.py"       )
             _UpperCAmelCase     :  Union[str, Any]							      =    [
                 "accelerate",
                 "launch",
                 "--num_processes=2",
                 "--num_machines=1",
                 "--machine_rank=0",
                 "--use_fsdp",
                 "--mixed_precision=fp16",
                 "--fsdp_transformer_layer_cls_to_wrap=BertLayer",
             ]

             for i, strategy in enumerate(A       ):
                    _UpperCAmelCase     :  Optional[Any]							      =    cmd.copy()
                    cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""       )
                    if strategy != "FULL_SHARD":
                           continue
                    _UpperCAmelCase     :  Optional[Any]							      =    len(A       )
                    for state_dict_type in FSDP_STATE_DICT_TYPE:
                           _UpperCAmelCase     :  Optional[Any]							      =    cmd_config[:state_dict_config_index]
                           cmd_config.append(F"""--fsdp_state_dict_type={state_dict_type}"""       )
                           cmd_config.extend(
                               [
                                   self.test_file_path,
                                   F"""--output_dir={self.tmpdir}""",
                                   "--partial_train_epoch=1",
                               ]       )
                           with patch_environment(omp_num_threads=1       ):
                                  execute_subprocess_async(A						,					env=os.environ.copy()       )

                           _UpperCAmelCase     :  Optional[int]							      =    cmd_config[:-1]
                           _UpperCAmelCase     :  List[str]							      =    os.path.join(self.tmpdir						,					"epoch_0"       )
                           cmd_config.extend(
                               [
                                   F"""--resume_from_checkpoint={resume_from_checkpoint}""",
                               ]       )
                           with patch_environment(omp_num_threads=1       ):
                                  execute_subprocess_async(A						,					env=os.environ.copy()       )





      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  str							      =    os.path.join(self.test_scripts_folder						,					"test_peak_memory_usage.py"       )
             _UpperCAmelCase     :  Tuple							      =    [
                 "accelerate",
                 "launch",
                 "--num_processes=2",
                 "--num_machines=1",
                 "--machine_rank=0",
             ]
             for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
                    _UpperCAmelCase     :  str							      =    cmd.copy()
                    if "fp16" in spec:
                           cmd_config.extend(["--mixed_precision=fp16"]       )
                    else:
                           cmd_config.extend(["--mixed_precision=no"]       )

                    if "multi_gpu" in spec:
                           continue
                    else:
                           cmd_config.extend(["--use_fsdp"]       )
                           for i, strategy in enumerate(A       ):
                                  if strategy.lower() in spec:
                                         cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""       )
                                         break

                           if "cpu_offload" in spec:
                                  cmd_config.append("--fsdp_offload_params=True"       )

                           for policy in FSDP_AUTO_WRAP_POLICY:
                                  if policy.lower() in spec:
                                         cmd_config.append(F"""--fsdp_auto_wrap_policy={policy}"""       )
                                         break

                           if policy == "TRANSFORMER_BASED_WRAP":
                                  cmd_config.append("--fsdp_transformer_layer_cls_to_wrap=BertLayer"       )
                           elif policy == "SIZE_BASED_WRAP":
                                  cmd_config.append("--fsdp_min_num_params=2000"       )

                    cmd_config.extend(
                        [
                            self.test_file_path,
                            F"""--output_dir={self.tmpdir}""",
                            F"""--peak_memory_upper_bound={peak_mem_upper_bound}""",
                            F"""--n_train={self.n_train}""",
                            F"""--n_val={self.n_val}""",
                        ]       )
                    with patch_environment(omp_num_threads=1       ):
                           execute_subprocess_async(A						,					env=os.environ.copy()       )




class  A						:







    """simple docstring"""





    def __init__(      self :       Any,lowercase_ :       Tuple,lowercase_ :       Any,lowercase_ :       List[str]		)->    List[Any]:





        '''simple docstring'''







        A__          =   name
        A__          =   value
        A__          =   weight


    def __repr__(      self :       int		)->    Tuple:





        '''simple docstring'''







        return F'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        return self.value


    def 						snake_case__     (      self :       Any		)->    Tuple:





        '''simple docstring'''







        return self.name


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        return self.weight


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        return self.value / self.weight




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	List[Any]      )							->      List[Any]:





    '''simple docstring'''






    A__          =   []
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        menu.append(Things(name[i]    ,			value[i]    ,			weight[i]      )      )
    return menu
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Dict    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	int      )							->      Any:





    '''simple docstring'''






    A__          =   sorted(SCREAMING_SNAKE_CASE__    ,			key=SCREAMING_SNAKE_CASE__    ,			reverse=SCREAMING_SNAKE_CASE__      )
    A__          =   []
    A__      ,						A__          =   0.0, 0.0
    for i in range(len(SCREAMING_SNAKE_CASE__      )      ):
        if (total_cost + items_copy[i].get_weight()) <= max_cost:
            result.append(items_copy[i]      )
            total_cost += items_copy[i].get_weight()
            total_value += items_copy[i].get_value()
    return (result, total_value)
def 						_snake_case(    )							->      Any:





    '''simple docstring'''








if __name__ == "__main__":
     import doctest

     doctest.testmod()

