





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(4_2)

lowercase      :       str									=  """bert-base-cased"""
lowercase      :       List[Any]									=  """fp16"""
lowercase      :       List[str]									=  """bf16"""
lowercase      :       List[str]									=  [FPaa, BFaa]





@require_fsdp
@require_cuda
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



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







          '''simple docstring'''

          super().setUp()

          a__						:	Optional[int] =				dict(
              ACCELERATE_USE_FSDP='true'					,  MASTER_ADDR='localhost'					,  MASTER_PORT='10999'					,  RANK='0'					,  LOCAL_RANK='0'					,  WORLD_SIZE='1'					,  )

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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

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







          '''simple docstring'''

          from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

          for i, prefetch_policy in enumerate(lowercase):
                 a__						:	Optional[int] =				self.dist_env.copy()
                 a__						:	List[Any] =				prefetch_policy
                 with mockenv_context(**lowercase):
                        a__						:	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 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

          for i, state_dict_type in enumerate(lowercase):
                 a__						:	Optional[int] =				self.dist_env.copy()
                 a__						:	List[Any] =				state_dict_type
                 with mockenv_context(**lowercase):
                        a__						:	List[str] =				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 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	int =				AutoModel.from_pretrained(lowercase)
          for policy in FSDP_AUTO_WRAP_POLICY:
                 a__						:	List[str] =				self.dist_env.copy()
                 a__						:	Tuple =				policy
                 if policy == "TRANSFORMER_BASED_WRAP":
                        a__						:	Union[str, Any] =				'BertLayer'
                 elif policy == "SIZE_BASED_WRAP":
                        a__						:	int =				'2000'
                 with mockenv_context(**lowercase):
                        a__						:	Tuple =				FullyShardedDataParallelPlugin()
                        fsdp_plugin.set_auto_wrap_policy(lowercase)
                        if policy == "NO_WRAP":
                               self.assertIsNone(fsdp_plugin.auto_wrap_policy)
                        else:
                               self.assertIsNotNone(fsdp_plugin.auto_wrap_policy)

          a__						:	Tuple =				self.dist_env.copy()
          a__						:	Tuple =				'TRANSFORMER_BASED_WRAP'
          a__						:	Union[str, Any] =				'T5Layer'
          with mockenv_context(**lowercase):
                 a__						:	Any =				FullyShardedDataParallelPlugin()
                 with self.assertRaises(lowercase) as cm:
                        fsdp_plugin.set_auto_wrap_policy(lowercase)
                 self.assertTrue('Could not find the transformer layer class to wrap in the model.' in str(cm.exception))

          a__						:	Dict =				self.dist_env.copy()
          a__						:	str =				'SIZE_BASED_WRAP'
          a__						:	Tuple =				'0'
          with mockenv_context(**lowercase):
                 a__						:	Union[str, Any] =				FullyShardedDataParallelPlugin()
                 fsdp_plugin.set_auto_wrap_policy(lowercase)
                 self.assertIsNone(fsdp_plugin.auto_wrap_policy)

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







          '''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:
                 a__						:	Tuple =				self.dist_env.copy()
                 a__						:	int =				mp_dtype
                 with mockenv_context(**lowercase):
                        a__						:	Tuple =				Accelerator()
                        if mp_dtype == "fp16":
                               a__						:	Any =				torch.floataa
                        elif mp_dtype == "bf16":
                               a__						:	Union[str, Any] =				torch.bfloataa
                        a__						:	Union[str, Any] =				MixedPrecision(param_dtype=lowercase					,  reduce_dtype=lowercase					,  buffer_dtype=lowercase)
                        self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					,  lowercase)
                        if mp_dtype == FPaa:
                               self.assertTrue(isinstance(accelerator.scaler					,  lowercase))
                        elif mp_dtype == BFaa:
                               self.assertIsNone(accelerator.scaler)
                        AcceleratorState._reset_state(lowercase)

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







          '''simple docstring'''

          from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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





@require_fsdp
@require_multi_gpu
@slow
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          super().setUp()
          a__						:	List[str] =				0.82
          a__						:	List[Any] =				[
              'fsdp_shard_grad_op_transformer_based_wrap',
              'fsdp_full_shard_transformer_based_wrap',
          ]
          a__						:	List[str] =				{
              '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__						:	List[str] =				160
          a__						:	Union[str, Any] =				160

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

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Any =				os.path.join(self.test_scripts_folder					,  'test_performance.py')
          a__						:	Any =				['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
          for config in self.performance_configs:
                 a__						:	Optional[int] =				cmd.copy()
                 for i, strategy in enumerate(lowercase):
                        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(lowercase					,  env=os.environ.copy())

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

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

                        a__						:	List[Any] =				cmd_config[:-1]
                        a__						:	str =				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(lowercase					,  env=os.environ.copy())

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	Optional[Any] =				os.path.join(self.test_scripts_folder					,  'test_peak_memory_usage.py')
          a__						:	List[str] =				[
              '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__						:	Union[str, 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(lowercase):
                               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(lowercase					,  env=os.environ.copy())









import math
import random



def A_	(		A__		,      A__ = False				)  ->		float:
       if deriv:
              return value * (1 - value)
       return 1 / (1 + math.exp(-value				))


# Initial Value
lowercase      :       Optional[Any]									=  0.02



def A_	(		A__		,      A__				)  ->		float:
       a__						:	int =				float(2 * (random.randint(1		,      100				)) - 1				)

       for _ in range(A__				):
              # Forward propagation
              a__						:	Union[str, Any] =				sigmoid_function(INITIAL_VALUE * weight				)
              # How much did we miss?
              a__						:	Optional[Any] =				(expected / 100) - layer_a
              # Error delta
              a__						:	Optional[int] =				layer_1_error * sigmoid_function(A__		,      A__				)
              # Update weight
              weight += INITIAL_VALUE * layer_1_delta

       return layer_a * 100


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    lowercase      :       Dict									=  int(input("""Expected value: """))
    lowercase      :       Dict									=  int(input("""Number of propagations: """))
    print(forward_propagation(expected, number_propagations))


