
"""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(4_2)

UpperCAmelCase__      :    Any    							= 'bert-base-cased'
UpperCAmelCase__      :    int    							= 'fp16'
UpperCAmelCase__      :    Dict    							= 'bf16'
UpperCAmelCase__      :    List[str]    							= [FPaa, BFaa]



@require_fsdp
@require_cuda
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											super().setUp()

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   dict(
											    ACCELERATE_USE_FSDP="""true"""   ,       MASTER_ADDR="""localhost"""   ,       MASTER_PORT="""10999"""   ,       RANK="""0"""   ,       LOCAL_RANK="""0"""   ,       WORLD_SIZE="""1"""   ,       )



				def 	__magic_name__    (self    )					->		Any:



											"""simple docstring"""




											from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

											for i, strategy in enumerate(SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   self.dist_env.copy()
																		SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   F'''{i + 1}'''
																		SCREAMING_SNAKE_CASE__						:       int           =   strategy
																		with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Tuple           =   FullyShardedDataParallelPlugin()
																									self.assertEqual(fsdp_plugin.sharding_strategy   ,       ShardingStrategy(i + 1    )    )



				def 	__magic_name__    (self    )					->		Tuple:



											"""simple docstring"""




											from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

											for i, prefetch_policy in enumerate(SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.dist_env.copy()
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   prefetch_policy
																		with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       int           =   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(SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   self.dist_env.copy()
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   state_dict_type
																		with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Union[str, 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 	__magic_name__    (self    )					->		List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   AutoModel.from_pretrained(SCREAMING_SNAKE_CASE__    )
											for policy in FSDP_AUTO_WRAP_POLICY:
																		SCREAMING_SNAKE_CASE__						:       int           =   self.dist_env.copy()
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   policy
																		if policy == "TRANSFORMER_BASED_WRAP":
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """BertLayer"""
																		elif policy == "SIZE_BASED_WRAP":
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """2000"""
																		with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       str           =   FullyShardedDataParallelPlugin()
																									fsdp_plugin.set_auto_wrap_policy(SCREAMING_SNAKE_CASE__    )
																									if policy == "NO_WRAP":
																																self.assertIsNone(fsdp_plugin.auto_wrap_policy    )
																									else:
																																self.assertIsNotNone(fsdp_plugin.auto_wrap_policy    )

											SCREAMING_SNAKE_CASE__						:       List[str]           =   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__						:       str           =   """TRANSFORMER_BASED_WRAP"""
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   """T5Layer"""
											with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   FullyShardedDataParallelPlugin()
																		with self.assertRaises(SCREAMING_SNAKE_CASE__    ) as cm:
																									fsdp_plugin.set_auto_wrap_policy(SCREAMING_SNAKE_CASE__    )
																		self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception    )    )

											SCREAMING_SNAKE_CASE__						:       Dict           =   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   """SIZE_BASED_WRAP"""
											SCREAMING_SNAKE_CASE__						:       int           =   """0"""
											with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																		SCREAMING_SNAKE_CASE__						:       str           =   FullyShardedDataParallelPlugin()
																		fsdp_plugin.set_auto_wrap_policy(SCREAMING_SNAKE_CASE__    )
																		self.assertIsNone(fsdp_plugin.auto_wrap_policy    )



				def 	__magic_name__    (self    )					->		int:



											"""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:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.dist_env.copy()
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   mp_dtype
																		with mockenv_context(**SCREAMING_SNAKE_CASE__    ):
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   Accelerator()
																									if mp_dtype == "fp16":
																																SCREAMING_SNAKE_CASE__						:       str           =   torch.floataa
																									elif mp_dtype == "bf16":
																																SCREAMING_SNAKE_CASE__						:       int           =   torch.bfloataa
																									SCREAMING_SNAKE_CASE__						:       List[Any]           =   MixedPrecision(param_dtype=SCREAMING_SNAKE_CASE__   ,       reduce_dtype=SCREAMING_SNAKE_CASE__   ,       buffer_dtype=SCREAMING_SNAKE_CASE__    )
																									self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy   ,       SCREAMING_SNAKE_CASE__    )
																									if mp_dtype == FPaa:
																																self.assertTrue(isinstance(accelerator.scaler   ,       SCREAMING_SNAKE_CASE__    )    )
																									elif mp_dtype == BFaa:
																																self.assertIsNone(accelerator.scaler    )
																									AcceleratorState._reset_state(SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				def 	__magic_name__    (self    )					->		int:



											"""simple docstring"""




											super().setUp()
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   0.82
											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [
											    """fsdp_shard_grad_op_transformer_based_wrap""",
											    """fsdp_full_shard_transformer_based_wrap""",
											]
											SCREAMING_SNAKE_CASE__						:       Dict           =   {
											    """multi_gpu_fp16""": 32_00,
											    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 20_00,
											    """fsdp_full_shard_transformer_based_wrap_fp16""": 19_00,
											    # 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
											}
											SCREAMING_SNAKE_CASE__						:       List[str]           =   1_60
											SCREAMING_SNAKE_CASE__						:       Any           =   1_60

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



				def 	__magic_name__    (self    )					->		Union[str, Any]:



											"""simple docstring"""




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



				def 	__magic_name__    (self    )					->		Dict:



											"""simple docstring"""




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

																									SCREAMING_SNAKE_CASE__						:       Dict           =   cmd_config[:-1]
																									SCREAMING_SNAKE_CASE__						:       Any           =   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(SCREAMING_SNAKE_CASE__   ,       env=os.environ.copy()    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   os.path.join(self.test_scripts_folder   ,       """test_peak_memory_usage.py"""    )
											SCREAMING_SNAKE_CASE__						:       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():
																		SCREAMING_SNAKE_CASE__						:       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(SCREAMING_SNAKE_CASE__    ):
																																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(SCREAMING_SNAKE_CASE__   ,       env=os.environ.copy()    )






"""simple docstring"""




import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCAmelCase__      :    List[str]    							= '.'

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
UpperCAmelCase__      :    List[Any]    							= [
    'Assert',
    'AssignVariableOp',
    'EmptyTensorList',
    'MergeV2Checkpoints',
    'ReadVariableOp',
    'ResourceGather',
    'RestoreV2',
    'SaveV2',
    'ShardedFilename',
    'StatefulPartitionedCall',
    'StaticRegexFullMatch',
    'VarHandleOp',
]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

							with open(os.path.join(_snake_case   ,"""utils"""   ,"""tf_ops"""   ,"""onnx.json"""						)						) as f:
														SCREAMING_SNAKE_CASE__						:       Any           =   json.load(_snake_case						)["""opsets"""]

							for i in range(1   ,opset + 1						):
														onnx_ops.extend(onnx_opsets[str(_snake_case						)]						)

							with open(_snake_case   ,"""rb"""						) as f:
														saved_model.ParseFromString(f.read()						)

							SCREAMING_SNAKE_CASE__						:       List[str]           =   set()

							# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
							for meta_graph in saved_model.meta_graphs:
														# Add operations in the graph definition
														model_op_names.update(node.op for node in meta_graph.graph_def.node						)

														# Go through the functions in the graph definition
														for func in meta_graph.graph_def.library.function:
																					# Add operations in each function
																					model_op_names.update(node.op for node in func.node_def						)

    # Convert to list, sorted if you want
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   []

							for op in model_op_names:
														if op not in onnx_ops and op not in INTERNAL_OPS:
																					incompatible_ops.append(_snake_case						)

							if strict and len(_snake_case						) > 0:
														raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
							elif len(_snake_case						) > 0:
														print(f'''Found the following incompatible ops for the opset {opset}:'''						)
														print(*_snake_case   ,sep="""\n"""						)
							else:
														print(f'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[int]    							= argparse.ArgumentParser()
					parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
					parser.add_argument(
					    '--opset', default=1_2, type=int, help='The ONNX opset against which the model has to be tested.'
					)
					parser.add_argument(
					    '--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
					)
					parser.add_argument(
					    '--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
					)
					UpperCAmelCase__      :    Dict    							= parser.parse_args()

					if args.framework == "onnx":
										onnx_compliancy(args.saved_model_path, args.strict, args.opset)




