


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)

__UpperCamelCase				:  Dict      					=							'bert-base-cased'
__UpperCamelCase				:  Any      					=							'fp16'
__UpperCamelCase				:  List[str]      					=							'bf16'
__UpperCamelCase				:  Any      					=							[FPaa, BFaa]





@require_fsdp
@require_cuda
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       super().setUp()

       UpperCAmelCase_							:      int						=   dict(
           ACCELERATE_USE_FSDP='''true'''       ,   MASTER_ADDR='''localhost'''       ,   MASTER_PORT='''10999'''       ,   RANK='''0'''       ,   LOCAL_RANK='''0'''       ,   WORLD_SIZE='''1'''       ,   )





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

       for i, strategy in enumerate(__snake_case	):
          UpperCAmelCase_							:      Optional[int]						=   self.dist_env.copy()
          UpperCAmelCase_							:      str						=   f'''{i + 1}'''
          UpperCAmelCase_							:      Any						=   strategy
          with mockenv_context(**__snake_case	):
             UpperCAmelCase_							:      Dict						=   FullyShardedDataParallelPlugin()
             self.assertEqual(fsdp_plugin.sharding_strategy       ,   ShardingStrategy(i + 1	)	)





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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





    def 					_lowerCamelCase (	self				:   Any	):





       '''simple docstring'''
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

       for i, state_dict_type in enumerate(__snake_case	):
          UpperCAmelCase_							:      Dict						=   self.dist_env.copy()
          UpperCAmelCase_							:      Tuple						=   state_dict_type
          with mockenv_context(**__snake_case	):
             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 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   AutoModel.from_pretrained(__snake_case	)
       for policy in FSDP_AUTO_WRAP_POLICY:
          UpperCAmelCase_							:      Optional[int]						=   self.dist_env.copy()
          UpperCAmelCase_							:      Tuple						=   policy
          if policy == "TRANSFORMER_BASED_WRAP":
             UpperCAmelCase_							:      List[str]						=   '''BertLayer'''
          elif policy == "SIZE_BASED_WRAP":
             UpperCAmelCase_							:      Tuple						=   '''2000'''
          with mockenv_context(**__snake_case	):
             UpperCAmelCase_							:      List[str]						=   FullyShardedDataParallelPlugin()
             fsdp_plugin.set_auto_wrap_policy(__snake_case	)
             if policy == "NO_WRAP":
                self.assertIsNone(fsdp_plugin.auto_wrap_policy	)
             else:
                self.assertIsNotNone(fsdp_plugin.auto_wrap_policy	)

       UpperCAmelCase_							:      List[Any]						=   self.dist_env.copy()
       UpperCAmelCase_							:      Any						=   '''TRANSFORMER_BASED_WRAP'''
       UpperCAmelCase_							:      Dict						=   '''T5Layer'''
       with mockenv_context(**__snake_case	):
          UpperCAmelCase_							:      Dict						=   FullyShardedDataParallelPlugin()
          with self.assertRaises(__snake_case	) as cm:
             fsdp_plugin.set_auto_wrap_policy(__snake_case	)
          self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception	)	)

       UpperCAmelCase_							:      Dict						=   self.dist_env.copy()
       UpperCAmelCase_							:      int						=   '''SIZE_BASED_WRAP'''
       UpperCAmelCase_							:      Any						=   '''0'''
       with mockenv_context(**__snake_case	):
          UpperCAmelCase_							:      List[str]						=   FullyShardedDataParallelPlugin()
          fsdp_plugin.set_auto_wrap_policy(__snake_case	)
          self.assertIsNone(fsdp_plugin.auto_wrap_policy	)





    def 					_lowerCamelCase (	self				:   Tuple	):





       '''simple docstring'''
       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_							:      Tuple						=   self.dist_env.copy()
          UpperCAmelCase_							:      str						=   mp_dtype
          with mockenv_context(**__snake_case	):
             UpperCAmelCase_							:      List[Any]						=   Accelerator()
             if mp_dtype == "fp16":
                UpperCAmelCase_							:      str						=   torch.floataa
             elif mp_dtype == "bf16":
                UpperCAmelCase_							:      Tuple						=   torch.bfloataa
             UpperCAmelCase_							:      List[Any]						=   MixedPrecision(param_dtype=__snake_case       ,   reduce_dtype=__snake_case       ,   buffer_dtype=__snake_case	)
             self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy       ,   __snake_case	)
             if mp_dtype == FPaa:
                self.assertTrue(isinstance(accelerator.scaler       ,   __snake_case	)	)
             elif mp_dtype == BFaa:
                self.assertIsNone(accelerator.scaler	)
             AcceleratorState._reset_state(__snake_case	)





    def 					_lowerCamelCase (	self				:   str	):





       '''simple docstring'''
       from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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





@require_fsdp
@require_multi_gpu
@slow
class  lowerCAmelCase__(    snake_case__						):






    '''simple docstring'''





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       super().setUp()
       UpperCAmelCase_							:      Dict						=   0.82
       UpperCAmelCase_							:      str						=   [
           '''fsdp_shard_grad_op_transformer_based_wrap''',
           '''fsdp_full_shard_transformer_based_wrap''',
       ]
       UpperCAmelCase_							:      List[Any]						=   {
           '''multi_gpu_fp16''': 3_200,
           '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_000,
           '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_900,
           # 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_							:      Optional[int]						=   160
       UpperCAmelCase_							:      str						=   160

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





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[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(__snake_case	):
             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(__snake_case       ,   env=os.environ.copy()	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   os.path.join(self.test_scripts_folder       ,   '''test_checkpointing.py'''	)
       UpperCAmelCase_							:      Optional[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(__snake_case	):
          UpperCAmelCase_							:      Union[str, Any]						=   cmd.copy()
          cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''	)
          if strategy != "FULL_SHARD":
             continue
          UpperCAmelCase_							:      List[str]						=   len(__snake_case	)
          for state_dict_type in FSDP_STATE_DICT_TYPE:
             UpperCAmelCase_							:      List[str]						=   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(__snake_case       ,   env=os.environ.copy()	)

             UpperCAmelCase_							:      List[Any]						=   cmd_config[:-1]
             UpperCAmelCase_							:      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(__snake_case       ,   env=os.environ.copy()	)





    def 					_lowerCamelCase (	self				:   Union[str, Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   os.path.join(self.test_scripts_folder       ,   '''test_peak_memory_usage.py'''	)
       UpperCAmelCase_							:      int						=   [
           '''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_							:      Optional[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(__snake_case	):
                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(__snake_case       ,   env=os.environ.copy()	)



def      snake_case_						( __lowercase       ):
   return " ".join(
       ''''''.join(word[::-1]       ) if len(__lowercase       ) > 4 else word for word in sentence.split()       )


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       print(reverse_long_words('Hey wollef sroirraw'))