




"""simple docstring"""
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_				:    Any										=     '''bert-base-cased'''
a_				:    int										=     '''fp16'''
a_				:    Union[str, Any]										=     '''bf16'''
a_				:    Union[str, Any]										=     [FPaa, BFaa]

@require_fsdp
@require_cuda
class __lowercase(      lowercase__				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								super().setUp()

								__lowerCamelCase					:	str	       =				dict(
								    ACCELERATE_USE_FSDP='true'						,	MASTER_ADDR='localhost'						,	MASTER_PORT='10999'						,	RANK='0'						,	LOCAL_RANK='0'						,	WORLD_SIZE='1'						,	)
					def     snake_case_				(    self     ):
								from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

								for i, strategy in enumerate(__a     ):
											__lowerCamelCase					:	Any	       =				self.dist_env.copy()
											__lowerCamelCase					:	Optional[Any]	       =				f'''{i + 1}'''
											__lowerCamelCase					:	Any	       =				strategy
											with mockenv_context(**__a     ):
														__lowerCamelCase					:	List[Any]	       =				FullyShardedDataParallelPlugin()
														self.assertEqual(fsdp_plugin.sharding_strategy						,	ShardingStrategy(i + 1     )     )
					def     snake_case_				(    self     ):
								from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

								for i, prefetch_policy in enumerate(__a     ):
											__lowerCamelCase					:	int	       =				self.dist_env.copy()
											__lowerCamelCase					:	Union[str, Any]	       =				prefetch_policy
											with mockenv_context(**__a     ):
														__lowerCamelCase					:	List[str]	       =				FullyShardedDataParallelPlugin()
														if prefetch_policy == "NO_PREFETCH":
																	self.assertIsNone(fsdp_plugin.backward_prefetch     )
														else:
																	self.assertEqual(fsdp_plugin.backward_prefetch						,	BackwardPrefetch(i + 1     )     )
					def     snake_case_				(    self     ):
								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					:	Optional[Any]	       =				state_dict_type
											with mockenv_context(**__a     ):
														__lowerCamelCase					:	Optional[int]	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	List[str]	       =				AutoModel.from_pretrained(__a     )
								for policy in FSDP_AUTO_WRAP_POLICY:
											__lowerCamelCase					:	Optional[int]	       =				self.dist_env.copy()
											__lowerCamelCase					:	Tuple	       =				policy
											if policy == "TRANSFORMER_BASED_WRAP":
														__lowerCamelCase					:	List[str]	       =				'BertLayer'
											elif policy == "SIZE_BASED_WRAP":
														__lowerCamelCase					:	str	       =				'2000'
											with mockenv_context(**__a     ):
														__lowerCamelCase					:	Tuple	       =				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					:	Tuple	       =				self.dist_env.copy()
								__lowerCamelCase					:	List[str]	       =				'TRANSFORMER_BASED_WRAP'
								__lowerCamelCase					:	Optional[Any]	       =				'T5Layer'
								with mockenv_context(**__a     ):
											__lowerCamelCase					:	int	       =				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					:	List[str]	       =				self.dist_env.copy()
								__lowerCamelCase					:	Optional[Any]	       =				'SIZE_BASED_WRAP'
								__lowerCamelCase					:	int	       =				'0'
								with mockenv_context(**__a     ):
											__lowerCamelCase					:	List[Any]	       =				FullyShardedDataParallelPlugin()
											fsdp_plugin.set_auto_wrap_policy(__a     )
											self.assertIsNone(fsdp_plugin.auto_wrap_policy     )
					def     snake_case_				(    self     ):
								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					:	Any	       =				mp_dtype
											with mockenv_context(**__a     ):
														__lowerCamelCase					:	List[str]	       =				Accelerator()
														if mp_dtype == "fp16":
																	__lowerCamelCase					:	Tuple	       =				torch.floataa
														elif mp_dtype == "bf16":
																	__lowerCamelCase					:	Union[str, Any]	       =				torch.bfloataa
														__lowerCamelCase					:	Union[str, Any]	       =				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     snake_case_				(    self     ):
								from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

								for flag in [True, False]:
											__lowerCamelCase					:	Any	       =				self.dist_env.copy()
											__lowerCamelCase					:	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 __lowercase(      lowercase__				):




					'''simple docstring'''


					def     snake_case_				(    self     ):
								super().setUp()
								__lowerCamelCase					:	Tuple	       =				0.82
								__lowerCamelCase					:	Union[str, Any]	       =				[
								    'fsdp_shard_grad_op_transformer_based_wrap',
								    'fsdp_full_shard_transformer_based_wrap',
								]
								__lowerCamelCase					:	int	       =				{
								    '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					:	Optional[int]	       =				160
								__lowerCamelCase					:	Optional[Any]	       =				160

								__lowerCamelCase					:	int	       =				inspect.getfile(accelerate.test_utils     )
								__lowerCamelCase					:	Union[str, Any]	       =				os.path.sep.join(mod_file.split(os.path.sep     )[:-1] + ['scripts', 'external_deps']     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	Union[str, Any]	       =				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					:	Dict	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				os.path.join(self.test_scripts_folder						,	'test_checkpointing.py'     )
								__lowerCamelCase					:	int	       =				[
								    '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					:	Optional[Any]	       =				cmd.copy()
											cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''     )
											if strategy != "FULL_SHARD":
														continue
											__lowerCamelCase					:	Union[str, Any]	       =				len(__a     )
											for state_dict_type in FSDP_STATE_DICT_TYPE:
														__lowerCamelCase					:	Optional[Any]	       =				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					:	Union[str, Any]	       =				cmd_config[:-1]
														__lowerCamelCase					:	Optional[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     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				os.path.join(self.test_scripts_folder						,	'test_peak_memory_usage.py'     )
								__lowerCamelCase					:	Tuple	       =				[
								    '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					:	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()     )









"""simple docstring"""
import random





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    List[str]							,				A__:    Union[str, Any]     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				a[left_index]
			__lowerCamelCase					:	int	       =				left_index + 1
			for j in range(left_index + 1							,				A__     ):
						if a[j] < pivot:
									__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				a[i], a[j]
									i += 1
			__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				a[i - 1], a[left_index]
			return i - 1





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Tuple							,				A__:    Tuple     )  ->     Dict:
			if left < right:
						__lowerCamelCase					:	Optional[int]	       =				random.randint(A__							,				right - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				(
						    a[left],
						    a[pivot],
						)  # switches the pivot with the left most bound
						__lowerCamelCase					:	Union[str, Any]	       =				partition(A__							,				A__							,				A__     )
						quick_sort_random(
						    A__							,				A__							,				A__     )  # recursive quicksort to the left of the pivot point
						quick_sort_random(
						    A__							,				pivot_index + 1							,				A__     )  # recursive quicksort to the right of the pivot point





def 						UpperCAmelCase				(			)  ->     int:
			__lowerCamelCase					:	Dict	       =				input('Enter numbers separated by a comma:\n'     ).strip()
			__lowerCamelCase					:	int	       =				[int(A__     ) for item in user_input.split(','     )]

			quick_sort_random(A__							,				0							,				len(A__     )     )

			print(A__     )


if __name__ == "__main__":
					main()



