

def       a__					(		UpperCAmelCase							:      int      )  ->						bool:
  if not isinstance(UpperCAmelCase    ,			UpperCAmelCase      ):
    UpperCAmelCase	:							List[str]						=			f'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCAmelCase      )
  if number < 0:
    return False
  UpperCAmelCase	:							List[str]						=			number * number
  while number > 0:
    if number % 10 != number_square % 10:
      return False
    number //= 10
    number_square //= 10
  return True


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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(4_2)

_lowerCamelCase    :       List[str]        =							"bert-base-cased"
_lowerCamelCase    :       str        =							"fp16"
_lowerCamelCase    :       Optional[int]        =							"bf16"
_lowerCamelCase    :       List[str]        =							[FPaa, BFaa]





@require_fsdp
@require_cuda
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					Tuple						):
     super().setUp()

     UpperCAmelCase	:							Optional[Any]						=			dict(
         ACCELERATE_USE_FSDP='''true''',		MASTER_ADDR='''localhost''',		MASTER_PORT='''10999''',		RANK='''0''',		LOCAL_RANK='''0''',		WORLD_SIZE='''1''',		)



   def 	__magic_name__   (							self							:					int						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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



   def 	__magic_name__   (							self							:					Dict						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

     for i, prefetch_policy in enumerate(__A						):
       UpperCAmelCase	:							int						=			self.dist_env.copy()
       UpperCAmelCase	:							Dict						=			prefetch_policy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
         if prefetch_policy == "NO_PREFETCH":
           self.assertIsNone(fsdp_plugin.backward_prefetch						)
         else:
           self.assertEqual(fsdp_plugin.backward_prefetch,		BackwardPrefetch(i + 1						)						)



   def 	__magic_name__   (							self							:					Any						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

     for i, state_dict_type in enumerate(__A						):
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			state_dict_type
       with mockenv_context(**__A						):
         UpperCAmelCase	:							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 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			AutoModel.from_pretrained(__A						)
     for policy in FSDP_AUTO_WRAP_POLICY:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							List[Any]						=			policy
       if policy == "TRANSFORMER_BASED_WRAP":
         UpperCAmelCase	:							Tuple						=			'''BertLayer'''
       elif policy == "SIZE_BASED_WRAP":
         UpperCAmelCase	:							List[str]						=			'''2000'''
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[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	:							List[str]						=			self.dist_env.copy()
     UpperCAmelCase	:							Tuple						=			'''TRANSFORMER_BASED_WRAP'''
     UpperCAmelCase	:							Optional[Any]						=			'''T5Layer'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							int						=			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	:							List[Any]						=			self.dist_env.copy()
     UpperCAmelCase	:							str						=			'''SIZE_BASED_WRAP'''
     UpperCAmelCase	:							str						=			'''0'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
       fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertIsNone(fsdp_plugin.auto_wrap_policy						)



   def 	__magic_name__   (							self							:					int						):
     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	:							List[Any]						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			mp_dtype
       with mockenv_context(**__A						):
         UpperCAmelCase	:							int						=			Accelerator()
         if mp_dtype == "fp16":
           UpperCAmelCase	:							Any						=			torch.floataa
         elif mp_dtype == "bf16":
           UpperCAmelCase	:							Any						=			torch.bfloataa
         UpperCAmelCase	:							Optional[Any]						=			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 	__magic_name__   (							self							:					Optional[int]						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

     for flag in [True, False]:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			str(__A						).lower()
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Union[str, Any]						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.cpu_offload,		CPUOffload(offload_params=__A						)						)






@require_fsdp
@require_multi_gpu
@slow
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					List[Any]						):
     super().setUp()
     UpperCAmelCase	:							int						=			0.8_2
     UpperCAmelCase	:							List[str]						=			[
         '''fsdp_shard_grad_op_transformer_based_wrap''',
         '''fsdp_full_shard_transformer_based_wrap''',
     ]
     UpperCAmelCase	:							int						=			{
         '''multi_gpu_fp16''': 3_2_0_0,
         '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
         '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
         # 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	:							List[str]						=			1_6_0
     UpperCAmelCase	:							Optional[int]						=			1_6_0

     UpperCAmelCase	:							Union[str, Any]						=			inspect.getfile(accelerate.test_utils						)
     UpperCAmelCase	:							Dict						=			os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''external_deps''']						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							str						=			os.path.join(self.test_scripts_folder,		'''test_performance.py'''						)
     UpperCAmelCase	:							Any						=			['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
     for config in self.performance_configs:
       UpperCAmelCase	:							Union[str, Any]						=			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 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Dict						=			os.path.join(self.test_scripts_folder,		'''test_checkpointing.py'''						)
     UpperCAmelCase	:							Optional[int]						=			[
         '''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	:							Any						=			len(__A						)
       for state_dict_type in FSDP_STATE_DICT_TYPE:
         UpperCAmelCase	:							List[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	:							Tuple						=			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 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Optional[Any]						=			os.path.join(self.test_scripts_folder,		'''test_peak_memory_usage.py'''						)
     UpperCAmelCase	:							str						=			[
         '''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	:							List[Any]						=			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()						)





