





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

@require_fsdp
@require_cuda
class        snake_case_    (		__lowercase						):






  def 				UpperCAmelCase__						(							self			:					str      )->Optional[Any]:






   '''simple docstring'''

   super().setUp()

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






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Optional[Any]:






   '''simple docstring'''

   from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Any:






   '''simple docstring'''

   from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

   for i, prefetch_policy in enumerate(__a      ):
    __lowerCAmelCase					:	Optional[int]      =       self.dist_env.copy()
    __lowerCAmelCase					:	Optional[int]      =       prefetch_policy
    with mockenv_context(**__a      ):
     __lowerCAmelCase					:	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			:					Dict      )->Optional[Any]:






   '''simple docstring'''

   from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

   for i, state_dict_type in enumerate(__a      ):
    __lowerCAmelCase					:	List[Any]      =       self.dist_env.copy()
    __lowerCAmelCase					:	List[str]      =       state_dict_type
    with mockenv_context(**__a      ):
     __lowerCAmelCase					:	Optional[Any]      =       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			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       AutoModel.from_pretrained(__a      )
   for policy in FSDP_AUTO_WRAP_POLICY:
    __lowerCAmelCase					:	str      =       self.dist_env.copy()
    __lowerCAmelCase					:	str      =       policy
    if policy == "TRANSFORMER_BASED_WRAP":
     __lowerCAmelCase					:	Optional[Any]      =       """BertLayer"""
    elif policy == "SIZE_BASED_WRAP":
     __lowerCAmelCase					:	List[Any]      =       """2000"""
    with mockenv_context(**__a      ):
     __lowerCAmelCase					:	Union[str, 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      )

   __lowerCAmelCase					:	List[Any]      =       self.dist_env.copy()
   __lowerCAmelCase					:	Any      =       """TRANSFORMER_BASED_WRAP"""
   __lowerCAmelCase					:	List[str]      =       """T5Layer"""
   with mockenv_context(**__a      ):
    __lowerCAmelCase					:	str      =       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      )      )

   __lowerCAmelCase					:	Tuple      =       self.dist_env.copy()
   __lowerCAmelCase					:	Tuple      =       """SIZE_BASED_WRAP"""
   __lowerCAmelCase					:	List[str]      =       """0"""
   with mockenv_context(**__a      ):
    __lowerCAmelCase					:	int      =       FullyShardedDataParallelPlugin()
    fsdp_plugin.set_auto_wrap_policy(__a      )
    self.assertIsNone(fsdp_plugin.auto_wrap_policy      )






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Any:






   '''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:
    __lowerCAmelCase					:	List[Any]      =       self.dist_env.copy()
    __lowerCAmelCase					:	Tuple      =       mp_dtype
    with mockenv_context(**__a      ):
     __lowerCAmelCase					:	int      =       Accelerator()
     if mp_dtype == "fp16":
      __lowerCAmelCase					:	List[Any]      =       torch.floataa
     elif mp_dtype == "bf16":
      __lowerCAmelCase					:	Any      =       torch.bfloataa
     __lowerCAmelCase					:	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 				UpperCAmelCase__						(							self			:					Dict      )->Tuple:






   '''simple docstring'''

   from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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



@require_fsdp
@require_multi_gpu
@slow
class        snake_case_    (		__lowercase						):






  def 				UpperCAmelCase__						(							self			:					Tuple      )->Tuple:






   '''simple docstring'''

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

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






  def 				UpperCAmelCase__						(							self			:					List[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       os.path.join(self.test_scripts_folder       ,  """test_performance.py"""      )
   __lowerCAmelCase					:	Union[str, Any]      =       ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
   for config in self.performance_configs:
    __lowerCAmelCase					:	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 				UpperCAmelCase__						(							self			:					Optional[int]      )->Tuple:






   '''simple docstring'''

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

     __lowerCAmelCase					:	Optional[int]      =       cmd_config[:-1]
     __lowerCAmelCase					:	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(__a       ,  env=os.environ.copy()      )






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       os.path.join(self.test_scripts_folder       ,  """test_peak_memory_usage.py"""      )
   __lowerCAmelCase					:	Optional[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():
    __lowerCAmelCase					:	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(__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()      )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

      _UpperCAmelCase             =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)