




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)

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


@require_fsdp
@require_cuda
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






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





           '''simple docstring'''

           super().setUp()

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


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->List[str]:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

           for i, strategy in enumerate(UpperCAmelCase__):
                  A__       =    self.dist_env.copy()
                  A__       =    f"""{i + 1}"""
                  A__       =    strategy
                  with mockenv_context(**UpperCAmelCase__):
                         A__       =    FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.sharding_strategy					, ShardingStrategy(i + 1))


    def        SCREAMING_SNAKE_CASE							(			self      :	Union[str, Any])						->Tuple:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

           for i, prefetch_policy in enumerate(UpperCAmelCase__):
                  A__       =    self.dist_env.copy()
                  A__       =    prefetch_policy
                  with mockenv_context(**UpperCAmelCase__):
                         A__       =    FullyShardedDataParallelPlugin()
                         if prefetch_policy == "NO_PREFETCH":
                                self.assertIsNone(fsdp_plugin.backward_prefetch)
                         else:
                                self.assertEqual(fsdp_plugin.backward_prefetch					, BackwardPrefetch(i + 1))


    def        SCREAMING_SNAKE_CASE							(			self      :	Tuple)						->Dict:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

           for i, state_dict_type in enumerate(UpperCAmelCase__):
                  A__       =    self.dist_env.copy()
                  A__       =    state_dict_type
                  with mockenv_context(**UpperCAmelCase__):
                         A__       =    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        SCREAMING_SNAKE_CASE							(			self      :	str)						->int:





           '''simple docstring'''

           A__       =    AutoModel.from_pretrained(UpperCAmelCase__)
           for policy in FSDP_AUTO_WRAP_POLICY:
                  A__       =    self.dist_env.copy()
                  A__       =    policy
                  if policy == "TRANSFORMER_BASED_WRAP":
                         A__       =    '''BertLayer'''
                  elif policy == "SIZE_BASED_WRAP":
                         A__       =    '''2000'''
                  with mockenv_context(**UpperCAmelCase__):
                         A__       =    FullyShardedDataParallelPlugin()
                         fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase__)
                         if policy == "NO_WRAP":
                                self.assertIsNone(fsdp_plugin.auto_wrap_policy)
                         else:
                                self.assertIsNotNone(fsdp_plugin.auto_wrap_policy)

           A__       =    self.dist_env.copy()
           A__       =    '''TRANSFORMER_BASED_WRAP'''
           A__       =    '''T5Layer'''
           with mockenv_context(**UpperCAmelCase__):
                  A__       =    FullyShardedDataParallelPlugin()
                  with self.assertRaises(UpperCAmelCase__) as cm:
                         fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase__)
                  self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception))

           A__       =    self.dist_env.copy()
           A__       =    '''SIZE_BASED_WRAP'''
           A__       =    '''0'''
           with mockenv_context(**UpperCAmelCase__):
                  A__       =    FullyShardedDataParallelPlugin()
                  fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase__)
                  self.assertIsNone(fsdp_plugin.auto_wrap_policy)


    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->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:
                  A__       =    self.dist_env.copy()
                  A__       =    mp_dtype
                  with mockenv_context(**UpperCAmelCase__):
                         A__       =    Accelerator()
                         if mp_dtype == "fp16":
                                A__       =    torch.floataa
                         elif mp_dtype == "bf16":
                                A__       =    torch.bfloataa
                         A__       =    MixedPrecision(param_dtype=UpperCAmelCase__					, reduce_dtype=UpperCAmelCase__					, buffer_dtype=UpperCAmelCase__)
                         self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					, UpperCAmelCase__)
                         if mp_dtype == FPaa:
                                self.assertTrue(isinstance(accelerator.scaler					, UpperCAmelCase__))
                         elif mp_dtype == BFaa:
                                self.assertIsNone(accelerator.scaler)
                         AcceleratorState._reset_state(UpperCAmelCase__)





    def        SCREAMING_SNAKE_CASE							(			self      :	Dict)						->str:





           '''simple docstring'''

           from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


@require_fsdp
@require_multi_gpu
@slow
class 				UpperCamelCase_    ( UpperCAmelCase__     ):

    '''simple docstring'''






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





           '''simple docstring'''

           super().setUp()
           A__       =    0.82
           A__       =    [
               '''fsdp_shard_grad_op_transformer_based_wrap''',
               '''fsdp_full_shard_transformer_based_wrap''',
           ]
           A__       =    {
               '''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
           }
           A__       =    160
           A__       =    160

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


    def        SCREAMING_SNAKE_CASE							(			self      :	str)						->Any:





           '''simple docstring'''

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


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





           '''simple docstring'''

           A__       =    os.path.join(self.test_scripts_folder					, '''test_checkpointing.py''')
           A__       =    [
               '''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(UpperCAmelCase__):
                  A__       =    cmd.copy()
                  cmd_config.append(f"""--fsdp_sharding_strategy={i+1}""")
                  if strategy != "FULL_SHARD":
                         continue
                  A__       =    len(UpperCAmelCase__)
                  for state_dict_type in FSDP_STATE_DICT_TYPE:
                         A__       =    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(UpperCAmelCase__					, env=os.environ.copy())

                         A__       =    cmd_config[:-1]
                         A__       =    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(UpperCAmelCase__					, env=os.environ.copy())





    def        SCREAMING_SNAKE_CASE							(			self      :	Optional[int])						->int:





           '''simple docstring'''

           A__       =    os.path.join(self.test_scripts_folder					, '''test_peak_memory_usage.py''')
           A__       =    [
               '''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():
                  A__       =    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(UpperCAmelCase__):
                                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(UpperCAmelCase__					, env=os.environ.copy())






"""simple docstring"""
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline





