
'''simple docstring'''



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)

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




@require_fsdp
@require_cuda
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												super().setUp()

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

												for i, state_dict_type in enumerate(_lowercase				):
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			state_dict_type
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			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  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			AutoModel.from_pretrained(_lowercase				)
												for policy in FSDP_AUTO_WRAP_POLICY:
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			policy
																		if policy == "TRANSFORMER_BASED_WRAP":
																								_lowerCAmelCase  						=			"""BertLayer"""
																		elif policy == "SIZE_BASED_WRAP":
																								_lowerCAmelCase  						=			"""2000"""
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																								fsdp_plugin.set_auto_wrap_policy(_lowercase				)
																								if policy == "NO_WRAP":
																														self.assertIsNone(fsdp_plugin.auto_wrap_policy				)
																								else:
																														self.assertIsNotNone(fsdp_plugin.auto_wrap_policy				)

												_lowerCAmelCase  						=			self.dist_env.copy()
												_lowerCAmelCase  						=			"""TRANSFORMER_BASED_WRAP"""
												_lowerCAmelCase  						=			"""T5Layer"""
												with mockenv_context(**_lowercase				):
																		_lowerCAmelCase  						=			FullyShardedDataParallelPlugin()
																		with self.assertRaises(_lowercase				) as cm:
																								fsdp_plugin.set_auto_wrap_policy(_lowercase				)
																		self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception				)				)

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




						def  _lowercase     ( self				):

												"""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:
																		_lowerCAmelCase  						=			self.dist_env.copy()
																		_lowerCAmelCase  						=			mp_dtype
																		with mockenv_context(**_lowercase				):
																								_lowerCAmelCase  						=			Accelerator()
																								if mp_dtype == "fp16":
																														_lowerCAmelCase  						=			torch.floataa
																								elif mp_dtype == "bf16":
																														_lowerCAmelCase  						=			torch.bfloataa
																								_lowerCAmelCase  						=			MixedPrecision(param_dtype=_lowercase   ,       reduce_dtype=_lowercase   ,       buffer_dtype=_lowercase				)
																								self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy   ,       _lowercase				)
																								if mp_dtype == FPaa:
																														self.assertTrue(isinstance(accelerator.scaler   ,       _lowercase				)				)
																								elif mp_dtype == BFaa:
																														self.assertIsNone(accelerator.scaler				)
																								AcceleratorState._reset_state(_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self				):

												"""simple docstring"""



												super().setUp()
												_lowerCAmelCase  						=			0.82
												_lowerCAmelCase  						=			[
												    """fsdp_shard_grad_op_transformer_based_wrap""",
												    """fsdp_full_shard_transformer_based_wrap""",
												]
												_lowerCAmelCase  						=			{
												    """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
												}
												_lowerCAmelCase  						=			160
												_lowerCAmelCase  						=			160

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




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			os.path.join(self.test_scripts_folder   ,       """test_performance.py"""				)
												_lowerCAmelCase  						=			["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
												for config in self.performance_configs:
																		_lowerCAmelCase  						=			cmd.copy()
																		for i, strategy in enumerate(_lowercase				):
																								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(_lowercase   ,       env=os.environ.copy()				)




						def  _lowercase     ( self				):

												"""simple docstring"""



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

																								_lowerCAmelCase  						=			cmd_config[:-1]
																								_lowerCAmelCase  						=			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(_lowercase   ,       env=os.environ.copy()				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			os.path.join(self.test_scripts_folder   ,       """test_peak_memory_usage.py"""				)
												_lowerCAmelCase  						=			[
												    """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  						=			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(_lowercase				):
																														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(_lowercase   ,       env=os.environ.copy()				)


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase    =		{
    """configuration_table_transformer""": [
        """TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TableTransformerConfig""",
        """TableTransformerOnnxConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TableTransformerForObjectDetection""",
						    """TableTransformerModel""",
						    """TableTransformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_table_transformer import (
						    TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    TableTransformerConfig,
						    TableTransformerOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_table_transformer import (
												    TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TableTransformerForObjectDetection,
												    TableTransformerModel,
												    TableTransformerPreTrainedModel,
												)

else:
						import sys

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