



'''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)

snake_case_			:     Optional[Any]  	=  'bert-base-cased'
snake_case_			:     Any  	=  'fp16'
snake_case_			:     Optional[Any]  	=  'bf16'
snake_case_			:     Dict  	=  [FPaa, BFaa]






@require_fsdp
@require_cuda
class 							lowercase__     (							__UpperCamelCase       ):





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							super().setUp()

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





	def      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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





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







							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

							for i, prefetch_policy in enumerate(_lowerCAmelCase			):
													_UpperCamelCase :		int			    =       self.dist_env.copy()
													_UpperCamelCase :		int			    =       prefetch_policy
													with mockenv_context(**_lowerCAmelCase			):
																			_UpperCamelCase :		Union[str, Any]			    =       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 :    str			):







							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

							for i, state_dict_type in enumerate(_lowerCAmelCase			):
													_UpperCamelCase :		Union[str, Any]			    =       self.dist_env.copy()
													_UpperCamelCase :		Any			    =       state_dict_type
													with mockenv_context(**_lowerCAmelCase			):
																			_UpperCamelCase :		List[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 :    List[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       AutoModel.from_pretrained(_lowerCAmelCase			)
							for policy in FSDP_AUTO_WRAP_POLICY:
													_UpperCamelCase :		str			    =       self.dist_env.copy()
													_UpperCamelCase :		List[str]			    =       policy
													if policy == "TRANSFORMER_BASED_WRAP":
																			_UpperCamelCase :		List[str]			    =       """BertLayer"""
													elif policy == "SIZE_BASED_WRAP":
																			_UpperCamelCase :		str			    =       """2000"""
													with mockenv_context(**_lowerCAmelCase			):
																			_UpperCamelCase :		List[Any]			    =       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			)

							_UpperCamelCase :		int			    =       self.dist_env.copy()
							_UpperCamelCase :		Any			    =       """TRANSFORMER_BASED_WRAP"""
							_UpperCamelCase :		Optional[Any]			    =       """T5Layer"""
							with mockenv_context(**_lowerCAmelCase			):
													_UpperCamelCase :		str			    =       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			)			)

							_UpperCamelCase :		Any			    =       self.dist_env.copy()
							_UpperCamelCase :		str			    =       """SIZE_BASED_WRAP"""
							_UpperCamelCase :		Optional[int]			    =       """0"""
							with mockenv_context(**_lowerCAmelCase			):
													_UpperCamelCase :		Tuple			    =       FullyShardedDataParallelPlugin()
													fsdp_plugin.set_auto_wrap_policy(_lowerCAmelCase			)
													self.assertIsNone(fsdp_plugin.auto_wrap_policy			)





	def      UpperCamelCase_  (			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:
													_UpperCamelCase :		int			    =       self.dist_env.copy()
													_UpperCamelCase :		Any			    =       mp_dtype
													with mockenv_context(**_lowerCAmelCase			):
																			_UpperCamelCase :		Optional[Any]			    =       Accelerator()
																			if mp_dtype == "fp16":
																									_UpperCamelCase :		Tuple			    =       torch.floataa
																			elif mp_dtype == "bf16":
																									_UpperCamelCase :		Tuple			    =       torch.bfloataa
																			_UpperCamelCase :		List[str]			    =       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      UpperCamelCase_  (			self :    Optional[int]			):







							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class 							lowercase__     (							__UpperCamelCase       ):





	def      UpperCamelCase_  (			self :    Tuple			):







							'''simple docstring'''
							super().setUp()
							_UpperCamelCase :		Optional[Any]			    =       0.8_2
							_UpperCamelCase :		List[Any]			    =       [
							    """fsdp_shard_grad_op_transformer_based_wrap""",
							    """fsdp_full_shard_transformer_based_wrap""",
							]
							_UpperCamelCase :		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
							}
							_UpperCamelCase :		List[str]			    =       160
							_UpperCamelCase :		Dict			    =       160

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





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		List[str]			    =       os.path.join(self.test_scripts_folder			,'test_performance.py'			)
							_UpperCamelCase :		Union[str, Any]			    =       ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
							for config in self.performance_configs:
													_UpperCamelCase :		Tuple			    =       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      UpperCamelCase_  (			self :    Optional[Any]			):







							'''simple docstring'''
							_UpperCamelCase :		Any			    =       os.path.join(self.test_scripts_folder			,'test_checkpointing.py'			)
							_UpperCamelCase :		Dict			    =       [
							    """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			):
													_UpperCamelCase :		int			    =       cmd.copy()
													cmd_config.append(F'--fsdp_sharding_strategy={i+1}'			)
													if strategy != "FULL_SHARD":
																			continue
													_UpperCamelCase :		List[Any]			    =       len(_lowerCAmelCase			)
													for state_dict_type in FSDP_STATE_DICT_TYPE:
																			_UpperCamelCase :		List[str]			    =       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()			)

																			_UpperCamelCase :		Optional[int]			    =       cmd_config[:-1]
																			_UpperCamelCase :		Union[str, Any]			    =       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      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[int]			    =       os.path.join(self.test_scripts_folder			,'test_peak_memory_usage.py'			)
							_UpperCamelCase :		Dict			    =       [
							    """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():
													_UpperCamelCase :		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(_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'''
class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    Optional[int]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       None
							_UpperCamelCase :		List[Any]			    =       None
							_UpperCamelCase :		int			    =       graph

							self._normalize_graph(lowerCamelCase__			,lowerCamelCase__			)
							_UpperCamelCase :		List[str]			    =       len(lowerCamelCase__			)
							_UpperCamelCase :		Tuple			    =       None





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Optional[Any]			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							if sources is int:
													_UpperCamelCase :		Optional[int]			    =       [sources]
							if sinks is int:
													_UpperCamelCase :		Union[str, Any]			    =       [sinks]

							if len(lowerCamelCase__			) == 0 or len(lowerCamelCase__			) == 0:
													return

							_UpperCamelCase :		List[str]			    =       sources[0]
							_UpperCamelCase :		str			    =       sinks[0]

							# make fake vertex if there are more
							# than one source or sink
							if len(lowerCamelCase__			) > 1 or len(lowerCamelCase__			) > 1:
													_UpperCamelCase :		Dict			    =       0
													for i in sources:
																			max_input_flow += sum(self.graph[i]			)

													_UpperCamelCase :		Tuple			    =       len(self.graph			) + 1
													for room in self.graph:
																			room.insert(0			,0			)
													self.graph.insert(0			,[0] * size			)
													for i in sources:
																			_UpperCamelCase :		List[Any]			    =       max_input_flow
													_UpperCamelCase :		Tuple			    =       0

													_UpperCamelCase :		int			    =       len(self.graph			) + 1
													for room in self.graph:
																			room.append(0			)
													self.graph.append([0] * size			)
													for i in sinks:
																			_UpperCamelCase :		Optional[int]			    =       max_input_flow
													_UpperCamelCase :		Optional[int]			    =       size - 1





	def      UpperCamelCase_  (			self :    List[str]			):







							'''simple docstring'''
							if self.maximum_flow_algorithm is None:
													raise Exception('You need to set maximum flow algorithm before.'			)
							if self.source_index is None or self.sink_index is None:
													return 0

							self.maximum_flow_algorithm.execute()
							return self.maximum_flow_algorithm.getMaximumFlow()





	def      UpperCamelCase_  (			self :    Any			,lowerCamelCase__ :    Any			):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       algorithm(self			)






class 							lowercase__     :





	def __init__(			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Optional[Any]			    =       flow_network
							_UpperCamelCase :		List[str]			    =       flow_network.verticesCount
							_UpperCamelCase :		List[str]			    =       flow_network.sourceIndex
							_UpperCamelCase :		Any			    =       flow_network.sinkIndex
							# it's just a reference, so you shouldn't change
							# it in your algorithms, use deep copy before doing that
							_UpperCamelCase :		List[Any]			    =       flow_network.graph
							_UpperCamelCase :		Any			    =       False





	def      UpperCamelCase_  (			self :    str			):







							'''simple docstring'''
							if not self.executed:
													self._algorithm()
													_UpperCamelCase :		Any			    =       True





	def      UpperCamelCase_  (			self :    Any			):







							'''simple docstring'''
							pass






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)
							# use this to save your result
							_UpperCamelCase :		Tuple			    =       -1





	def      UpperCamelCase_  (			self :    Dict			):







							'''simple docstring'''
							if not self.executed:
													raise Exception('You should execute algorithm before using its result!'			)

							return self.maximum_flow






class 							lowercase__     (							lowercase       ):





	def __init__(			self :    Optional[Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							super().__init__(lowerCamelCase__			)

							_UpperCamelCase :		Dict			    =       [[0] * self.verticies_count for i in range(self.verticies_count			)]

							_UpperCamelCase :		Optional[Any]			    =       [0] * self.verticies_count
							_UpperCamelCase :		List[str]			    =       [0] * self.verticies_count





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







							'''simple docstring'''
							_UpperCamelCase :		Dict			    =       self.verticies_count

							# push some substance to graph
							for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index]			):
													self.preflow[self.source_index][nextvertex_index] += bandwidth
													self.preflow[nextvertex_index][self.source_index] -= bandwidth
													self.excesses[nextvertex_index] += bandwidth

							# Relabel-to-front selection rule
							_UpperCamelCase :		List[str]			    =       [
							    i
							    for i in range(self.verticies_count			)
							    if i != self.source_index and i != self.sink_index
							]

							# move through list
							_UpperCamelCase :		int			    =       0
							while i < len(lowerCamelCase__			):
													_UpperCamelCase :		List[Any]			    =       vertices_list[i]
													_UpperCamelCase :		str			    =       self.heights[vertex_index]
													self.process_vertex(lowerCamelCase__			)
													if self.heights[vertex_index] > previous_height:
																			# if it was relabeled, swap elements
																			# and start from 0 index
																			vertices_list.insert(0			,vertices_list.pop(lowerCamelCase__			)			)
																			_UpperCamelCase :		Dict			    =       0
													else:
																			i += 1

							_UpperCamelCase :		Optional[Any]			    =       sum(self.preflow[self.source_index]			)





	def      UpperCamelCase_  (			self :    Union[str, Any]			,lowerCamelCase__ :    Dict			):







							'''simple docstring'''
							while self.excesses[vertex_index] > 0:
													for neighbour_index in range(self.verticies_count			):
																			# if it's neighbour and current vertex is higher
																			if (
																			    self.graph[vertex_index][neighbour_index]
																			    - self.preflow[vertex_index][neighbour_index]
																			    > 0
																			    and self.heights[vertex_index] > self.heights[neighbour_index]
																			):
																									self.push(lowerCamelCase__			,lowerCamelCase__			)

													self.relabel(lowerCamelCase__			)





	def      UpperCamelCase_  (			self :    Tuple			,lowerCamelCase__ :    List[Any]			,lowerCamelCase__ :    List[str]			):







							'''simple docstring'''
							_UpperCamelCase :		Union[str, Any]			    =       min(
							    self.excesses[from_index]			,self.graph[from_index][to_index] - self.preflow[from_index][to_index]			,)
							self.preflow[from_index][to_index] += preflow_delta
							self.preflow[to_index][from_index] -= preflow_delta
							self.excesses[from_index] -= preflow_delta
							self.excesses[to_index] += preflow_delta





	def      UpperCamelCase_  (			self :    List[str]			,lowerCamelCase__ :    Optional[int]			):







							'''simple docstring'''
							_UpperCamelCase :		Tuple			    =       None
							for to_index in range(self.verticies_count			):
													if (
													    self.graph[vertex_index][to_index]
													    - self.preflow[vertex_index][to_index]
													    > 0
													) and (min_height is None or self.heights[to_index] < min_height):
																			_UpperCamelCase :		List[Any]			    =       self.heights[to_index]

							if min_height is not None:
													_UpperCamelCase :		Any			    =       min_height + 1


if __name__ == "__main__":
					snake_case_			:     List[str]  	=  [0]
					snake_case_			:     int  	=  [3]
					# graph = [
					#     [0, 0, 4, 6, 0, 0],
					#     [0, 0, 5, 2, 0, 0],
					#     [0, 0, 0, 0, 4, 4],
					#     [0, 0, 0, 0, 6, 6],
					#     [0, 0, 0, 0, 0, 0],
					#     [0, 0, 0, 0, 0, 0],
					# ]
					snake_case_			:     int  	=  [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]

					# prepare our network
					snake_case_			:     List[Any]  	=  FlowNetwork(graph, entrances, exits)
					# set algorithm
					flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
					# and calculate
					snake_case_			:     Tuple  	=  flow_network.find_maximum_flow()

					print(F"""maximum flow is {maximum_flow}""")


