






"""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__					: Dict=	"""bert-base-cased"""
A__					: Any=	"""fp16"""
A__					: Union[str, Any]=	"""bf16"""
A__					: List[Any]=	[FPaa, BFaa]
@require_fsdp
@require_cuda
class    __lowerCamelCase    (  __lowerCamelCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:

       '''simple docstring'''





       super().setUp()

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

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

       '''simple docstring'''





       from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:

       '''simple docstring'''





       from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

       for i, prefetch_policy in enumerate(UpperCAmelCase_      ):
           UpperCamelCase__         =    self.dist_env.copy()
           UpperCamelCase__         =    prefetch_policy
           with mockenv_context(**UpperCAmelCase_      ):
               UpperCamelCase__         =    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      )		->       Any:

       '''simple docstring'''





       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

       for i, state_dict_type in enumerate(UpperCAmelCase_      ):
           UpperCamelCase__         =    self.dist_env.copy()
           UpperCamelCase__         =    state_dict_type
           with mockenv_context(**UpperCAmelCase_      ):
               UpperCamelCase__         =    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      )		->       Union[str, Any]:

       '''simple docstring'''





       UpperCamelCase__         =    AutoModel.from_pretrained(UpperCAmelCase_      )
       for policy in FSDP_AUTO_WRAP_POLICY:
           UpperCamelCase__         =    self.dist_env.copy()
           UpperCamelCase__         =    policy
           if policy == "TRANSFORMER_BASED_WRAP":
               UpperCamelCase__         =    'BertLayer'
           elif policy == "SIZE_BASED_WRAP":
               UpperCamelCase__         =    '2000'
           with mockenv_context(**UpperCAmelCase_      ):
               UpperCamelCase__         =    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      )

       UpperCamelCase__         =    self.dist_env.copy()
       UpperCamelCase__         =    'TRANSFORMER_BASED_WRAP'
       UpperCamelCase__         =    'T5Layer'
       with mockenv_context(**UpperCAmelCase_      ):
           UpperCamelCase__         =    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      )      )

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

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       str:

       '''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__         =    self.dist_env.copy()
           UpperCamelCase__         =    mp_dtype
           with mockenv_context(**UpperCAmelCase_      ):
               UpperCamelCase__         =    Accelerator()
               if mp_dtype == "fp16":
                   UpperCamelCase__         =    torch.floataa
               elif mp_dtype == "bf16":
                   UpperCamelCase__         =    torch.bfloataa
               UpperCamelCase__         =    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      )		->       List[str]:

       '''simple docstring'''





       from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class    __lowerCamelCase    (  __lowerCamelCase  ):

   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Optional[Any]:

       '''simple docstring'''





       super().setUp()
       UpperCamelCase__         =    0.82
       UpperCamelCase__         =    [
           'fsdp_shard_grad_op_transformer_based_wrap',
           'fsdp_full_shard_transformer_based_wrap',
       ]
       UpperCamelCase__         =    {
           '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__         =    160
       UpperCamelCase__         =    160

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

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

       '''simple docstring'''





       UpperCamelCase__         =    os.path.join(self.test_scripts_folder ,				'test_performance.py'      )
       UpperCamelCase__         =    ['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
       for config in self.performance_configs:
           UpperCamelCase__         =    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]:

       '''simple docstring'''





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

               UpperCamelCase__         =    cmd_config[:-1]
               UpperCamelCase__         =    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      )		->       int:

       '''simple docstring'''





       UpperCamelCase__         =    os.path.join(self.test_scripts_folder ,				'test_peak_memory_usage.py'      )
       UpperCamelCase__         =    [
           '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__         =    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"""





A__					: Tuple=	"""Alexander Joslin"""

import operator as op

from .stack import Stack







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    {'*': op.mul, '/': op.truediv, '+': op.add, '-': op.sub}

    UpperCamelCase__         =    Stack()
    UpperCamelCase__         =    Stack()

    for i in equation:
        if i.isdigit():
            # RULE 1
            operand_stack.push(int(SCREAMING_SNAKE_CASE					)					)
        elif i in operators:
            # RULE 2
            operator_stack.push(SCREAMING_SNAKE_CASE					)
        elif i == ")":
            # RULE 4
            UpperCamelCase__         =    operator_stack.peek()
            operator_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()
            UpperCamelCase__         =    operand_stack.peek()
            operand_stack.pop()

            UpperCamelCase__         =    operators[opr](SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            operand_stack.push(SCREAMING_SNAKE_CASE					)

    # RULE 5
    return operand_stack.peek()


if __name__ == "__main__":
  A__					: int=	"""(5 + ((4 * 2) * (2 + 3)))"""
  # answer = 45
  print(F"""{equation} = {dijkstras_two_stack_algorithm(equation)}""")




