





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

@require_fsdp
@require_cuda
class      lowerCAmelCase							(      __UpperCamelCase    ):
 def   A_			(   self						:   Union[str, Any]			)    ->			Optional[Any]:
     super().setUp()

     lowerCamelCase__					:   List[str]      				=	dict(
         ACCELERATE_USE_FSDP='true'    ,  MASTER_ADDR='localhost'    ,  MASTER_PORT='10999'    ,  RANK='0'    ,  LOCAL_RANK='0'    ,  WORLD_SIZE='1'    ,  )
 def   A_			(   self						:   List[str]			)    ->			List[Any]:
     from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

     for i, strategy in enumerate(UpperCAmelCase			):
         lowerCamelCase__					:   Tuple      				=	self.dist_env.copy()
         lowerCamelCase__					:   Tuple      				=	F"""{i + 1}"""
         lowerCamelCase__					:   List[str]      				=	strategy
         with mockenv_context(**UpperCAmelCase			):
             lowerCamelCase__					:   Any      				=	FullyShardedDataParallelPlugin()
             self.assertEqual(fsdp_plugin.sharding_strategy    ,  ShardingStrategy(i + 1			)			)
 def   A_			(   self						:   Optional[int]			)    ->			Union[str, Any]:
     from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

     for i, prefetch_policy in enumerate(UpperCAmelCase			):
         lowerCamelCase__					:   Optional[int]      				=	self.dist_env.copy()
         lowerCamelCase__					:   List[str]      				=	prefetch_policy
         with mockenv_context(**UpperCAmelCase			):
             lowerCamelCase__					:   Tuple      				=	FullyShardedDataParallelPlugin()
             if prefetch_policy == "NO_PREFETCH":
                 self.assertIsNone(fsdp_plugin.backward_prefetch			)
             else:
                 self.assertEqual(fsdp_plugin.backward_prefetch    ,  BackwardPrefetch(i + 1			)			)
 def   A_			(   self						:   Optional[int]			)    ->			Union[str, Any]:
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

     for i, state_dict_type in enumerate(UpperCAmelCase			):
         lowerCamelCase__					:   Union[str, Any]      				=	self.dist_env.copy()
         lowerCamelCase__					:   Dict      				=	state_dict_type
         with mockenv_context(**UpperCAmelCase			):
             lowerCamelCase__					:   Optional[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   A_			(   self						:   str			)    ->			int:
     lowerCamelCase__					:   List[str]      				=	AutoModel.from_pretrained(UpperCAmelCase			)
     for policy in FSDP_AUTO_WRAP_POLICY:
         lowerCamelCase__					:   Optional[Any]      				=	self.dist_env.copy()
         lowerCamelCase__					:   Optional[Any]      				=	policy
         if policy == "TRANSFORMER_BASED_WRAP":
             lowerCamelCase__					:   str      				=	'BertLayer'
         elif policy == "SIZE_BASED_WRAP":
             lowerCamelCase__					:   Dict      				=	'2000'
         with mockenv_context(**UpperCAmelCase			):
             lowerCamelCase__					:   Tuple      				=	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			)

     lowerCamelCase__					:   Union[str, Any]      				=	self.dist_env.copy()
     lowerCamelCase__					:   Any      				=	'TRANSFORMER_BASED_WRAP'
     lowerCamelCase__					:   List[str]      				=	'T5Layer'
     with mockenv_context(**UpperCAmelCase			):
         lowerCamelCase__					:   List[str]      				=	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			)			)

     lowerCamelCase__					:   Dict      				=	self.dist_env.copy()
     lowerCamelCase__					:   Tuple      				=	'SIZE_BASED_WRAP'
     lowerCamelCase__					:   List[str]      				=	'0'
     with mockenv_context(**UpperCAmelCase			):
         lowerCamelCase__					:   Any      				=	FullyShardedDataParallelPlugin()
         fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase			)
         self.assertIsNone(fsdp_plugin.auto_wrap_policy			)
 def   A_			(   self						:   Dict			)    ->			Optional[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:
         lowerCamelCase__					:   List[Any]      				=	self.dist_env.copy()
         lowerCamelCase__					:   List[str]      				=	mp_dtype
         with mockenv_context(**UpperCAmelCase			):
             lowerCamelCase__					:   int      				=	Accelerator()
             if mp_dtype == "fp16":
                 lowerCamelCase__					:   int      				=	torch.floataa
             elif mp_dtype == "bf16":
                 lowerCamelCase__					:   int      				=	torch.bfloataa
             lowerCamelCase__					:   Dict      				=	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   A_			(   self						:   List[Any]			)    ->			Any:
     from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


@require_fsdp
@require_multi_gpu
@slow
class      lowerCAmelCase							(      __UpperCamelCase    ):
 def   A_			(   self						:   Optional[Any]			)    ->			str:
     super().setUp()
     lowerCamelCase__					:   Union[str, Any]      				=	0.8_2
     lowerCamelCase__					:   Dict      				=	[
         'fsdp_shard_grad_op_transformer_based_wrap',
         'fsdp_full_shard_transformer_based_wrap',
     ]
     lowerCamelCase__					:   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
     }
     lowerCamelCase__					:   List[str]      				=	160
     lowerCamelCase__					:   Any      				=	160

     lowerCamelCase__					:   List[str]      				=	inspect.getfile(accelerate.test_utils			)
     lowerCamelCase__					:   Union[str, Any]      				=	os.path.sep.join(mod_file.split(os.path.sep			)[:-1] + ['scripts', 'external_deps']			)
 def   A_			(   self						:   Optional[int]			)    ->			Any:
     lowerCamelCase__					:   Optional[Any]      				=	os.path.join(self.test_scripts_folder    ,  'test_performance.py'			)
     lowerCamelCase__					:   Tuple      				=	['accelerate', 'launch', '--num_processes=2', '--num_machines=1', '--machine_rank=0', '--use_fsdp']
     for config in self.performance_configs:
         lowerCamelCase__					:   Union[str, Any]      				=	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   A_			(   self						:   int			)    ->			Optional[Any]:
     lowerCamelCase__					:   Tuple      				=	os.path.join(self.test_scripts_folder    ,  'test_checkpointing.py'			)
     lowerCamelCase__					:   List[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(UpperCAmelCase			):
         lowerCamelCase__					:   List[Any]      				=	cmd.copy()
         cmd_config.append(F"""--fsdp_sharding_strategy={i+1}"""			)
         if strategy != "FULL_SHARD":
             continue
         lowerCamelCase__					:   Optional[Any]      				=	len(UpperCAmelCase			)
         for state_dict_type in FSDP_STATE_DICT_TYPE:
             lowerCamelCase__					:   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(UpperCAmelCase    ,  env=os.environ.copy()			)

             lowerCamelCase__					:   Union[str, Any]      				=	cmd_config[:-1]
             lowerCamelCase__					:   Dict      				=	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   A_			(   self						:   List[Any]			)    ->			List[str]:
     lowerCamelCase__					:   Dict      				=	os.path.join(self.test_scripts_folder    ,  'test_peak_memory_usage.py'			)
     lowerCamelCase__					:   Optional[int]      				=	[
         '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():
         lowerCamelCase__					:   List[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(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()			)












from pathlib import Path

import fire


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[int]:
    lowerCamelCase__					:   Union[str, Any]      				=	Path(_UpperCAmelCase				)
    lowerCamelCase__					:   int      				=	Path(_UpperCAmelCase				)
    dest_dir.mkdir(exist_ok=_UpperCAmelCase				)
    for path in src_dir.iterdir():
        lowerCamelCase__					:   Tuple      				=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
        lowerCamelCase__					:   Optional[int]      				=	dest_dir.joinpath(path.name				)
        print(_UpperCAmelCase				)
        dest_path.open('w'				).write('\n'.join(_UpperCAmelCase				)				)


if __name__ == "__main__":
    fire.Fire(minify)





