






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)

a__       =     """bert-base-cased"""
a__       =     """fp16"""
a__       =     """bf16"""
a__       =     [FPaa, BFaa]


@require_fsdp
@require_cuda
class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											super().setUp()

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



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

											for i, prefetch_policy in enumerate(UpperCamelCase__):
																		snake_case__	  =       self.dist_env.copy()
																		snake_case__	  =       prefetch_policy
																		with mockenv_context(**UpperCamelCase__):
																									snake_case__	  =       FullyShardedDataParallelPlugin()
																									if prefetch_policy == "NO_PREFETCH":
																																self.assertIsNone(fsdp_plugin.backward_prefetch)
																									else:
																																self.assertEqual(fsdp_plugin.backward_prefetch			,		BackwardPrefetch(i + 1))



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

											for i, state_dict_type in enumerate(UpperCamelCase__):
																		snake_case__	  =       self.dist_env.copy()
																		snake_case__	  =       state_dict_type
																		with mockenv_context(**UpperCamelCase__):
																									snake_case__	  =       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 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       AutoModel.from_pretrained(UpperCamelCase__)
											for policy in FSDP_AUTO_WRAP_POLICY:
																		snake_case__	  =       self.dist_env.copy()
																		snake_case__	  =       policy
																		if policy == "TRANSFORMER_BASED_WRAP":
																									snake_case__	  =       """BertLayer"""
																		elif policy == "SIZE_BASED_WRAP":
																									snake_case__	  =       """2000"""
																		with mockenv_context(**UpperCamelCase__):
																									snake_case__	  =       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)

											snake_case__	  =       self.dist_env.copy()
											snake_case__	  =       """TRANSFORMER_BASED_WRAP"""
											snake_case__	  =       """T5Layer"""
											with mockenv_context(**UpperCamelCase__):
																		snake_case__	  =       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))

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



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''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:
																		snake_case__	  =       self.dist_env.copy()
																		snake_case__	  =       mp_dtype
																		with mockenv_context(**UpperCamelCase__):
																									snake_case__	  =       Accelerator()
																									if mp_dtype == "fp16":
																																snake_case__	  =       torch.floataa
																									elif mp_dtype == "bf16":
																																snake_case__	  =       torch.bfloataa
																									snake_case__	  =       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 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


@require_fsdp
@require_multi_gpu
@slow
class    _lowerCAmelCase			(		lowercase_      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											super().setUp()
											snake_case__	  =       0.82
											snake_case__	  =       [
											    """fsdp_shard_grad_op_transformer_based_wrap""",
											    """fsdp_full_shard_transformer_based_wrap""",
											]
											snake_case__	  =       {
											    """multi_gpu_fp16""": 3_2_0_0,
											    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2_0_0_0,
											    """fsdp_full_shard_transformer_based_wrap_fp16""": 1_9_0_0,
											    # 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
											}
											snake_case__	  =       1_6_0
											snake_case__	  =       1_6_0

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



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       os.path.join(self.test_scripts_folder			,		"""test_performance.py""")
											snake_case__	  =       ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
											for config in self.performance_configs:
																		snake_case__	  =       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 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       os.path.join(self.test_scripts_folder			,		"""test_checkpointing.py""")
											snake_case__	  =       [
											    """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__):
																		snake_case__	  =       cmd.copy()
																		cmd_config.append(F'''--fsdp_sharding_strategy={i+1}''')
																		if strategy != "FULL_SHARD":
																									continue
																		snake_case__	  =       len(UpperCamelCase__)
																		for state_dict_type in FSDP_STATE_DICT_TYPE:
																									snake_case__	  =       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())

																									snake_case__	  =       cmd_config[:-1]
																									snake_case__	  =       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 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       os.path.join(self.test_scripts_folder			,		"""test_peak_memory_usage.py""")
											snake_case__	  =       [
											    """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():
																		snake_case__	  =       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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_lilt""": ["""LILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LiltConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """LILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """LiltForQuestionAnswering""",
	    """LiltForSequenceClassification""",
	    """LiltForTokenClassification""",
	    """LiltModel""",
	    """LiltPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lilt import (
		    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    LiltForQuestionAnswering,
		    LiltForSequenceClassification,
		    LiltForTokenClassification,
		    LiltModel,
		    LiltPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
