






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_ :						int      =      '''bert-base-cased'''
UpperCAmelCase_ :						Any      =      '''fp16'''
UpperCAmelCase_ :						str      =      '''bf16'''
UpperCAmelCase_ :						int      =      [FPaa, BFaa]

@require_fsdp
@require_cuda
class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def 						_A (		self				:					List[Any]	):
		super().setUp()

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





	def 						_A (		self				:					List[str]	):
		from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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





	def 						_A (		self				:					str	):
		from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

		for i, prefetch_policy in enumerate(__lowerCamelCase	):
			UpperCamelCase     :str          =	self.dist_env.copy()
			UpperCamelCase     :List[Any]          =	prefetch_policy
			with mockenv_context(**__lowerCamelCase	):
				UpperCamelCase     :Optional[int]          =	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				:					Union[str, Any]	):
		from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

		for i, state_dict_type in enumerate(__lowerCamelCase	):
			UpperCamelCase     :Any          =	self.dist_env.copy()
			UpperCamelCase     :Tuple          =	state_dict_type
			with mockenv_context(**__lowerCamelCase	):
				UpperCamelCase     :Dict          =	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				:					Tuple	):
		UpperCamelCase     :int          =	AutoModel.from_pretrained(__lowerCamelCase	)
		for policy in FSDP_AUTO_WRAP_POLICY:
			UpperCamelCase     :Any          =	self.dist_env.copy()
			UpperCamelCase     :Dict          =	policy
			if policy == "TRANSFORMER_BASED_WRAP":
				UpperCamelCase     :Any          =	"""BertLayer"""
			elif policy == "SIZE_BASED_WRAP":
				UpperCamelCase     :Optional[Any]          =	"""2000"""
			with mockenv_context(**__lowerCamelCase	):
				UpperCamelCase     :int          =	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     :List[Any]          =	self.dist_env.copy()
		UpperCamelCase     :Optional[int]          =	"""TRANSFORMER_BASED_WRAP"""
		UpperCamelCase     :int          =	"""T5Layer"""
		with mockenv_context(**__lowerCamelCase	):
			UpperCamelCase     :Any          =	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     :List[str]          =	self.dist_env.copy()
		UpperCamelCase     :str          =	"""SIZE_BASED_WRAP"""
		UpperCamelCase     :int          =	"""0"""
		with mockenv_context(**__lowerCamelCase	):
			UpperCamelCase     :Optional[Any]          =	FullyShardedDataParallelPlugin()
			fsdp_plugin.set_auto_wrap_policy(__lowerCamelCase	)
			self.assertIsNone(fsdp_plugin.auto_wrap_policy	)





	def 						_A (		self				:					str	):
		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     :List[Any]          =	self.dist_env.copy()
			UpperCamelCase     :Union[str, Any]          =	mp_dtype
			with mockenv_context(**__lowerCamelCase	):
				UpperCamelCase     :Dict          =	Accelerator()
				if mp_dtype == "fp16":
					UpperCamelCase     :int          =	torch.floataa
				elif mp_dtype == "bf16":
					UpperCamelCase     :Union[str, Any]          =	torch.bfloataa
				UpperCamelCase     :Dict          =	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 						_A (		self				:					Dict	):
		from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

		for flag in [True, False]:
			UpperCamelCase     :Union[str, Any]          =	self.dist_env.copy()
			UpperCamelCase     :Union[str, Any]          =	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     _SCREAMING_SNAKE_CASE       (			_a      ):





	def 						_A (		self				:					List[Any]	):
		super().setUp()
		UpperCamelCase     :Optional[int]          =	0.82
		UpperCamelCase     :Any          =	[
		    """fsdp_shard_grad_op_transformer_based_wrap""",
		    """fsdp_full_shard_transformer_based_wrap""",
		]
		UpperCamelCase     :List[Any]          =	{
		    """multi_gpu_fp16""": 3_200,
		    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2_000,
		    """fsdp_full_shard_transformer_based_wrap_fp16""": 1_900,
		    # 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     :Optional[int]          =	160
		UpperCamelCase     :Union[str, Any]          =	160

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





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








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_ :						Any      =      '''Create a default config file for Accelerate with only a few flags set.'''

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]="no"	,			__magic_name__					:  str = default_json_config_file	,			__magic_name__					:  bool = False					)		->			str:






	"""simple docstring"""



	UpperCamelCase     :Any          =	Path(__magic_name__					)
	path.parent.mkdir(parents=__magic_name__	,			exist_ok=__magic_name__					)
	if path.exists():
		print(
		    f"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""					)
		return False
	UpperCamelCase     :Dict          =	mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    f"""`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"""					)
	UpperCamelCase     :Optional[Any]          =	{
	    """compute_environment""": """LOCAL_MACHINE""",
	    """mixed_precision""": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCamelCase     :Union[str, Any]          =	torch.cuda.device_count()
		UpperCamelCase     :List[Any]          =	num_gpus
		UpperCamelCase     :Dict          =	False
		if num_gpus > 1:
			UpperCamelCase     :Any          =	"""MULTI_GPU"""
		else:
			UpperCamelCase     :Any          =	"""NO"""
	elif is_xpu_available() and use_xpu:
		UpperCamelCase     :Optional[Any]          =	torch.xpu.device_count()
		UpperCamelCase     :Optional[int]          =	num_xpus
		UpperCamelCase     :int          =	False
		if num_xpus > 1:
			UpperCamelCase     :Union[str, Any]          =	"""MULTI_XPU"""
		else:
			UpperCamelCase     :Union[str, Any]          =	"""NO"""
	elif is_npu_available():
		UpperCamelCase     :List[Any]          =	torch.npu.device_count()
		UpperCamelCase     :Optional[Any]          =	num_npus
		UpperCamelCase     :Tuple          =	False
		if num_npus > 1:
			UpperCamelCase     :Optional[Any]          =	"""MULTI_NPU"""
		else:
			UpperCamelCase     :List[Any]          =	"""NO"""
	else:
		UpperCamelCase     :Any          =	0
		UpperCamelCase     :Optional[Any]          =	True
		UpperCamelCase     :Optional[Any]          =	1
		UpperCamelCase     :List[str]          =	"""NO"""
	UpperCamelCase     :int          =	ClusterConfig(**__magic_name__					)
	config.to_json_file(__magic_name__					)
	return path

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Tuple					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	parser.add_parser("""default"""	,			parents=__magic_name__	,			help=__magic_name__	,			formatter_class=__magic_name__					)
	parser.add_argument(
	    """--config_file"""	,			default=__magic_name__	,			help=(
	        """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )	,			dest="""save_location"""	,			)

	parser.add_argument(
	    """--mixed_precision"""	,			choices=["""no""", """fp16""", """bf16"""]	,			type=__magic_name__	,			help="""Whether or not to use mixed precision training. """
	    """Choose between FP16 and BF16 (bfloat16) training. """
	    """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,			default="""no"""	,			)
	parser.set_defaults(func=__magic_name__					)
	return parser

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[Any]					)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Optional[Any]          =	write_basic_config(args.mixed_precision	,			args.save_location					)
	if config_file:
		print(f"""accelerate configuration saved at {config_file}"""					)
