


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)

_lowercase							:   Dict						=						"bert-base-cased"
_lowercase							:   Optional[int]						=						"fp16"
_lowercase							:   Dict						=						"bf16"
_lowercase							:   List[Any]						=						[FPaa, BFaa]






@require_fsdp
@require_cuda
class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					Optional[Any]:
					super().setUp()

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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

				def   _UpperCAmelCase       (    self )						->					List[Any]:
					from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

					for i, prefetch_policy in enumerate(a__ ):
						A             =					self.dist_env.copy()
						A             =					prefetch_policy
						with mockenv_context(**a__ ):
							A             =					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 )						->					Any:
					from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

					for i, state_dict_type in enumerate(a__ ):
						A             =					self.dist_env.copy()
						A             =					state_dict_type
						with mockenv_context(**a__ ):
							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   _UpperCAmelCase       (    self )						->					Optional[int]:
					A             =					AutoModel.from_pretrained(a__ )
					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(**a__ ):
							A             =					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 )

					A             =					self.dist_env.copy()
					A             =					"""TRANSFORMER_BASED_WRAP"""
					A             =					"""T5Layer"""
					with mockenv_context(**a__ ):
						A             =					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 ) )

					A             =					self.dist_env.copy()
					A             =					"""SIZE_BASED_WRAP"""
					A             =					"""0"""
					with mockenv_context(**a__ ):
						A             =					FullyShardedDataParallelPlugin()
						fsdp_plugin.set_auto_wrap_policy(a__ )
						self.assertIsNone(fsdp_plugin.auto_wrap_policy )

				def   _UpperCAmelCase       (    self )						->					List[str]:
					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(**a__ ):
							A             =					Accelerator()
							if mp_dtype == "fp16":
								A             =					torch.floataa
							elif mp_dtype == "bf16":
								A             =					torch.bfloataa
							A             =					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:
					from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def   _UpperCAmelCase       (    self )						->					List[str]:
					super().setUp()
					A             =					0.82
					A             =					[
					    """fsdp_shard_grad_op_transformer_based_wrap""",
					    """fsdp_full_shard_transformer_based_wrap""",
					]
					A             =					{
					    """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
					}
					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   _UpperCAmelCase       (    self )						->					Optional[int]:
					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(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 )						->					Tuple:
					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(a__ ):
						A             =					cmd.copy()
						cmd_config.append(f'--fsdp_sharding_strategy={i+1}' )
						if strategy != "FULL_SHARD":
							continue
						A             =					len(a__ )
						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(a__					, 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(a__					, env=os.environ.copy() )

				def   _UpperCAmelCase       (    self )						->					Dict:
					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(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 ..utils import DummyObject, requires_backends






class    _UpperCamelCase     (       metaclass=__snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['note_seq']

				def __init__(    self					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(self					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Dict:
					requires_backends(cls					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(cls					, ["""note_seq"""] )



