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)

_SCREAMING_SNAKE_CASE										=						'bert-base-cased'
_SCREAMING_SNAKE_CASE										=						'fp16'
_SCREAMING_SNAKE_CASE										=						'bf16'
_SCREAMING_SNAKE_CASE										=						[FPaa, BFaa]



@require_fsdp
@require_cuda
class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        super().setUp()

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



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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



 def      UpperCAmelCase							(					self							)  ->					Any:
        from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

        for i, prefetch_policy in enumerate(lowerCAmelCase_							):
               _A        =  self.dist_env.copy()
               _A        =  prefetch_policy
               with mockenv_context(**lowerCAmelCase_							):
                      _A        =  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							)  ->					Dict:
        from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

        for i, state_dict_type in enumerate(lowerCAmelCase_							):
               _A        =  self.dist_env.copy()
               _A        =  state_dict_type
               with mockenv_context(**lowerCAmelCase_							):
                      _A        =  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							)  ->					Optional[Any]:
        _A        =  AutoModel.from_pretrained(lowerCAmelCase_							)
        for policy in FSDP_AUTO_WRAP_POLICY:
               _A        =  self.dist_env.copy()
               _A        =  policy
               if policy == "TRANSFORMER_BASED_WRAP":
                      _A        =  """BertLayer"""
               elif policy == "SIZE_BASED_WRAP":
                      _A        =  """2000"""
               with mockenv_context(**lowerCAmelCase_							):
                      _A        =  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							)

        _A        =  self.dist_env.copy()
        _A        =  """TRANSFORMER_BASED_WRAP"""
        _A        =  """T5Layer"""
        with mockenv_context(**lowerCAmelCase_							):
               _A        =  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							)							)

        _A        =  self.dist_env.copy()
        _A        =  """SIZE_BASED_WRAP"""
        _A        =  """0"""
        with mockenv_context(**lowerCAmelCase_							):
               _A        =  FullyShardedDataParallelPlugin()
               fsdp_plugin.set_auto_wrap_policy(lowerCAmelCase_							)
               self.assertIsNone(fsdp_plugin.auto_wrap_policy							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        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        =  self.dist_env.copy()
               _A        =  mp_dtype
               with mockenv_context(**lowerCAmelCase_							):
                      _A        =  Accelerator()
                      if mp_dtype == "fp16":
                             _A        =  torch.floataa
                      elif mp_dtype == "bf16":
                             _A        =  torch.bfloataa
                      _A        =  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							)  ->					Dict:
        from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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



@require_fsdp
@require_multi_gpu
@slow
class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        super().setUp()
        _A        =  0.82
        _A        =  [
            """fsdp_shard_grad_op_transformer_based_wrap""",
            """fsdp_full_shard_transformer_based_wrap""",
        ]
        _A        =  {
            """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
        }
        _A        =  1_60
        _A        =  1_60

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



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  os.path.join(self.test_scripts_folder		,				"""test_performance.py"""							)
        _A        =  ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
        for config in self.performance_configs:
               _A        =  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							)  ->					List[Any]:
        _A        =  os.path.join(self.test_scripts_folder		,				"""test_checkpointing.py"""							)
        _A        =  [
            """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_							):
               _A        =  cmd.copy()
               cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''							)
               if strategy != "FULL_SHARD":
                      continue
               _A        =  len(lowerCAmelCase_							)
               for state_dict_type in FSDP_STATE_DICT_TYPE:
                      _A        =  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()							)

                      _A        =  cmd_config[:-1]
                      _A        =  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							)  ->					Optional[Any]:
        _A        =  os.path.join(self.test_scripts_folder		,				"""test_peak_memory_usage.py"""							)
        _A        =  [
            """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        =  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()							)





from math import isqrt






def  snake_case  (       snake_case__			:int)						->   bool:
       return all(number % divisor != 0 for divisor in range(2   ,	isqrt(snake_case__) + 1))






def  snake_case  (       snake_case__			:int = 10**6)						->   int:
       _A        =  0
       _A        =  1
       _A        =  7
       while prime_candidate < max_prime:
              primes_count += is_prime(snake_case__)

              cube_index += 1
              prime_candidate += 6 * cube_index

       return primes_count


if __name__ == "__main__":
    print(F'''{solution() = }''')




