



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)

a__			=		'''bert-base-cased'''
a__			=		'''fp16'''
a__			=		'''bf16'''
a__			=		[FPaa, BFaa]







@require_fsdp
@require_cuda
class 						snake_case  (		_a     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	List[Any]) -> List[str]:

    """simple docstring"""







    super().setUp()

    _snake_case			:     Tuple 		=    dict(
        ACCELERATE_USE_FSDP="""true""" ,						MASTER_ADDR="""localhost""" ,						MASTER_PORT="""10999""" ,						RANK="""0""" ,						LOCAL_RANK="""0""" ,						WORLD_SIZE="""1""" ,						)

   def    UpperCamelCase_     (    self							:	List[str]) -> List[Any]:

    """simple docstring"""







    from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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

   def    UpperCamelCase_     (    self							:	str) -> int:

    """simple docstring"""







    from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

    for i, prefetch_policy in enumerate(__lowerCamelCase):
     _snake_case			:     str 		=    self.dist_env.copy()
     _snake_case			:     List[Any] 		=    prefetch_policy
     with mockenv_context(**__lowerCamelCase):
      _snake_case			:     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    UpperCamelCase_     (    self							:	Union[str, Any]) -> Union[str, Any]:

    """simple docstring"""







    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

    for i, state_dict_type in enumerate(__lowerCamelCase):
     _snake_case			:     Any 		=    self.dist_env.copy()
     _snake_case			:     Tuple 		=    state_dict_type
     with mockenv_context(**__lowerCamelCase):
      _snake_case			:     Dict 		=    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    UpperCamelCase_     (    self							:	Tuple) -> str:

    """simple docstring"""







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

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

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

   def    UpperCamelCase_     (    self							:	str) -> Optional[int]:

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


   def    UpperCamelCase_     (    self							:	Dict) -> int:

    """simple docstring"""







    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class 						snake_case  (		_a     ):



   '''simple docstring'''






   def    UpperCamelCase_     (    self							:	List[Any]) -> Optional[Any]:

    """simple docstring"""







    super().setUp()
    _snake_case			:     Optional[int] 		=    0.82
    _snake_case			:     Any 		=    [
        """fsdp_shard_grad_op_transformer_based_wrap""",
        """fsdp_full_shard_transformer_based_wrap""",
    ]
    _snake_case			:     List[Any] 		=    {
        """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
    }
    _snake_case			:     Optional[int] 		=    160
    _snake_case			:     Union[str, Any] 		=    160

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

   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Optional[Any]:

    """simple docstring"""







    _snake_case			:     Optional[int] 		=    os.path.join(self.test_scripts_folder ,						"""test_performance.py""")
    _snake_case			:     Any 		=    ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
    for config in self.performance_configs:
     _snake_case			:     Optional[Any] 		=    cmd.copy()
     for i, strategy in enumerate(__lowerCamelCase):
      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(__lowerCamelCase ,						env=os.environ.copy())

   def    UpperCamelCase_     (    self							:	int) -> Tuple:

    """simple docstring"""







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

      _snake_case			:     Optional[Any] 		=    cmd_config[:-1]
      _snake_case			:     int 		=    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(__lowerCamelCase ,						env=os.environ.copy())


   def    UpperCamelCase_     (    self							:	Optional[Any]) -> Optional[Any]:

    """simple docstring"""







    _snake_case			:     List[Any] 		=    os.path.join(self.test_scripts_folder ,						"""test_peak_memory_usage.py""")
    _snake_case			:     Union[str, Any] 		=    [
        """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():
     _snake_case			:     List[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(__lowerCamelCase):
       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(__lowerCamelCase ,						env=os.environ.copy())








def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  list[int]					)		->			bool:






	"""simple docstring"""



	if graph[path[curr_ind - 1]][next_ver] == 0:
		return False

	# 2. Validate that next vertex is not already in path
	return not any(vertex == next_ver for vertex in path					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  list[int]	,			__magic_name__					:  int					)		->			bool:






	"""simple docstring"""



	if curr_ind == len(__magic_name__					):
		# return whether path exists between current and starting vertices
		return graph[path[curr_ind - 1]][path[0]] == 1

	# Recursive Step
	for next_ver in range(0	,			len(__magic_name__					)					):
		if valid_connection(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					):
			# Insert current vertex  into path as next transition
			UpperCamelCase     :str          =	next_ver
			# Validate created path
			if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			curr_ind + 1					):
				return True
			# Backtrack
			UpperCamelCase     :Union[str, Any]          =	-1
	return False

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int = 0					)		->			list[int]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[-1] * (len(__magic_name__					) + 1)
	# initialize start and end of path with starting index
	UpperCamelCase     :Any          =	start_index
	# evaluate and if we find answer return path either return empty array
	return path if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			1					) else []
