






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()	)




'''simple docstring'''
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class    lowercase	(							A__					):

		"""simple docstring"""

		def __init__(		self    ,   UpperCamelCase_    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = False    ,   UpperCamelCase_ = None    ,   UpperCamelCase_ = None    ,   **UpperCamelCase_    ,   ):

							'''simple docstring'''


							super().__init__(
							    features=UpperCamelCase_    ,   cache_dir=UpperCamelCase_    ,   keep_in_memory=UpperCamelCase_    ,   streaming=UpperCamelCase_    ,   num_proc=UpperCamelCase_    ,   **UpperCamelCase_    ,   )
							UpperCamelCase__   :Any												= Generator(
							    cache_dir=UpperCamelCase_    ,   features=UpperCamelCase_    ,   generator=UpperCamelCase_    ,   gen_kwargs=UpperCamelCase_    ,   **UpperCamelCase_    ,   )




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							if self.streaming:
												UpperCamelCase__   :Optional[Any]												= self.builder.as_streaming_dataset(split='''train'''							)
							# Build regular (map-style) dataset
							else:
												UpperCamelCase__   :Optional[int]												= None
												UpperCamelCase__   :int												= None
												UpperCamelCase__   :Any												= None
												UpperCamelCase__   :Any												= None

												self.builder.download_and_prepare(
												    download_config=UpperCamelCase_    ,   download_mode=UpperCamelCase_    ,   verification_mode=UpperCamelCase_    ,   base_path=UpperCamelCase_    ,   num_proc=self.num_proc    ,   )
												UpperCamelCase__   :List[Any]												= self.builder.as_dataset(
												    split='''train'''    ,   verification_mode=UpperCamelCase_    ,   in_memory=self.keep_in_memory							)
							return dataset