





'''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)

__UpperCAmelCase            =       """bert-base-cased"""
__UpperCAmelCase            =       """fp16"""
__UpperCAmelCase            =       """bf16"""
__UpperCAmelCase            =       [FPaa, BFaa]




@require_fsdp
@require_cuda
class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							super().setUp()

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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

							for i, strategy in enumerate(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				int		=			self.dist_env.copy()
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			f'''{i + 1}'''
									SCREAMING_SNAKE_CASE :				Tuple		=			strategy
									with mockenv_context(**lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			FullyShardedDataParallelPlugin()
											self.assertEqual(fsdp_plugin.sharding_strategy      ,						ShardingStrategy(i + 1						)						)


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''simple docstring'''



							from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

							for i, prefetch_policy in enumerate(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				str		=			self.dist_env.copy()
									SCREAMING_SNAKE_CASE :				str		=			prefetch_policy
									with mockenv_context(**lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Optional[Any]		=			FullyShardedDataParallelPlugin()
											if prefetch_policy == "NO_PREFETCH":
													self.assertIsNone(fsdp_plugin.backward_prefetch						)
											else:
													self.assertEqual(fsdp_plugin.backward_prefetch      ,						BackwardPrefetch(i + 1						)						)


					def lowerCamelCase_			(						self				:			Any						):

							'''simple docstring'''



							from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

							for i, state_dict_type in enumerate(lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Any		=			self.dist_env.copy()
									SCREAMING_SNAKE_CASE :				Dict		=			state_dict_type
									with mockenv_context(**lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Tuple		=			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 lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



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

							SCREAMING_SNAKE_CASE :				int		=			self.dist_env.copy()
							SCREAMING_SNAKE_CASE :				str		=			"""TRANSFORMER_BASED_WRAP"""
							SCREAMING_SNAKE_CASE :				List[str]		=			"""T5Layer"""
							with mockenv_context(**lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				int		=			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						)						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			self.dist_env.copy()
							SCREAMING_SNAKE_CASE :				List[str]		=			"""SIZE_BASED_WRAP"""
							SCREAMING_SNAKE_CASE :				List[str]		=			"""0"""
							with mockenv_context(**lowerCamelCase_						):
									SCREAMING_SNAKE_CASE :				Dict		=			FullyShardedDataParallelPlugin()
									fsdp_plugin.set_auto_wrap_policy(lowerCamelCase_						)
									self.assertIsNone(fsdp_plugin.auto_wrap_policy						)


					def lowerCamelCase_			(						self				:			Union[str, 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:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			self.dist_env.copy()
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			mp_dtype
									with mockenv_context(**lowerCamelCase_						):
											SCREAMING_SNAKE_CASE :				Dict		=			Accelerator()
											if mp_dtype == "fp16":
													SCREAMING_SNAKE_CASE :				Union[str, Any]		=			torch.floataa
											elif mp_dtype == "bf16":
													SCREAMING_SNAKE_CASE :				str		=			torch.bfloataa
											SCREAMING_SNAKE_CASE :				Any		=			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 lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							super().setUp()
							SCREAMING_SNAKE_CASE :				Any		=			0.82
							SCREAMING_SNAKE_CASE :				Tuple		=			[
							    """fsdp_shard_grad_op_transformer_based_wrap""",
							    """fsdp_full_shard_transformer_based_wrap""",
							]
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			{
							    """multi_gpu_fp16""": 32_00,
							    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 20_00,
							    """fsdp_full_shard_transformer_based_wrap_fp16""": 19_00,
							    # 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
							}
							SCREAMING_SNAKE_CASE :				List[str]		=			1_60
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			1_60

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


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			os.path.join(self.test_scripts_folder      ,						"""test_performance.py"""						)
							SCREAMING_SNAKE_CASE :				Tuple		=			["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
							for config in self.performance_configs:
									SCREAMING_SNAKE_CASE :				Dict		=			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 lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



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

											SCREAMING_SNAKE_CASE :				int		=			cmd_config[:-1]
											SCREAMING_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 lowerCamelCase_			(						self				:			Optional[int]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			os.path.join(self.test_scripts_folder      ,						"""test_peak_memory_usage.py"""						)
							SCREAMING_SNAKE_CASE :				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():
									SCREAMING_SNAKE_CASE :				Optional[int]		=			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()						)










'''simple docstring'''




from __future__ import annotations

from statistics import mean



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			[0] * no_of_processes
		SCREAMING_SNAKE_CASE :				int		=			[0] * no_of_processes

		# Initialize remaining_time to waiting_time.

		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Optional[int]		=			burst_time[i]
		SCREAMING_SNAKE_CASE :				list[int]		=			[]

		SCREAMING_SNAKE_CASE :				List[str]		=			0
		SCREAMING_SNAKE_CASE :				List[Any]		=			0

		# When processes are not completed,
		# A process whose arrival time has passed \
		# and has remaining execution time is put into the ready_process.
		# The shortest process in the ready_process, target_process is executed.

		while completed != no_of_processes:
				SCREAMING_SNAKE_CASE :				Optional[int]		=			[]
				SCREAMING_SNAKE_CASE :				Any		=			-1

				for i in range(lowerCamelCase_	):
						if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
								ready_process.append(lowerCamelCase_	)

				if len(lowerCamelCase_	) > 0:
						SCREAMING_SNAKE_CASE :				Union[str, Any]		=			ready_process[0]
						for i in ready_process:
								if remaining_time[i] < remaining_time[target_process]:
										SCREAMING_SNAKE_CASE :				Any		=			i
						total_time += burst_time[target_process]
						completed += 1
						SCREAMING_SNAKE_CASE :				Any		=			0
						SCREAMING_SNAKE_CASE :				Optional[int]		=			(
						    total_time - arrival_time[target_process] - burst_time[target_process]
						)
				else:
						total_time += 1

		return waiting_time



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			[0] * no_of_processes
		for i in range(lowerCamelCase_	):
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			burst_time[i] + waiting_time[i]
		return turn_around_time


if __name__ == "__main__":
							print("""[TEST CASE 01]""")

							__UpperCAmelCase            =       4
							__UpperCAmelCase            =       [2, 5, 3, 7]
							__UpperCAmelCase            =       [0, 0, 0, 0]
							__UpperCAmelCase            =       calculate_waitingtime(arrival_time, burst_time, no_of_processes)
							__UpperCAmelCase            =       calculate_turnaroundtime(
							    burst_time, no_of_processes, waiting_time
							)

							# Printing the Result
							print("""PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time""")
							for i, process_id in enumerate(list(range(1, 5))):
														print(
														    f'''{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t'''
														    f'''{waiting_time[i]}\t\t\t\t{turn_around_time[i]}'''
														)
							print(f'''\nAverage waiting time = {mean(waiting_time):.5f}''')
							print(f'''Average turnaround time = {mean(turn_around_time):.5f}''')



