





"""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)

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







@require_fsdp
@require_cuda
class       a							(   a__  ):



 def    UpperCamelCase__  (     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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

  for i, state_dict_type in enumerate(_snake_case     ):
   lowerCAmelCase    				= self.dist_env.copy()
   lowerCAmelCase    				= state_dict_type
   with mockenv_context(**_snake_case     ):
    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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoModel.from_pretrained(_snake_case     )
  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(**_snake_case     ):
    lowerCAmelCase    				= FullyShardedDataParallelPlugin()
    fsdp_plugin.set_auto_wrap_policy(_snake_case     )
    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(**_snake_case     ):
   lowerCAmelCase    				= FullyShardedDataParallelPlugin()
   with self.assertRaises(_snake_case     ) as cm:
    fsdp_plugin.set_auto_wrap_policy(_snake_case     )
   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(**_snake_case     ):
   lowerCAmelCase    				= FullyShardedDataParallelPlugin()
   fsdp_plugin.set_auto_wrap_policy(_snake_case     )
   self.assertIsNone(fsdp_plugin.auto_wrap_policy     )



 def    UpperCamelCase__  (     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(**_snake_case     ):
    lowerCAmelCase    				= Accelerator()
    if mp_dtype == "fp16":
     lowerCAmelCase    				= torch.floataa
    elif mp_dtype == "bf16":
     lowerCAmelCase    				= torch.bfloataa
    lowerCAmelCase    				= MixedPrecision(param_dtype=_snake_case				,       reduce_dtype=_snake_case				,       buffer_dtype=_snake_case     )
    self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy				,       _snake_case     )
    if mp_dtype == FPaa:
     self.assertTrue(isinstance(accelerator.scaler				,       _snake_case     )     )
    elif mp_dtype == BFaa:
     self.assertIsNone(accelerator.scaler     )
    AcceleratorState._reset_state(_snake_case     )



 def    UpperCamelCase__  (     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(_snake_case     ).lower()
   with mockenv_context(**_snake_case     ):
    lowerCAmelCase    				= FullyShardedDataParallelPlugin()
    self.assertEqual(fsdp_plugin.cpu_offload				,       CPUOffload(offload_params=_snake_case     )     )







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



 def    UpperCamelCase__  (     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': 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
  }
  lowerCAmelCase    				= 1_60
  lowerCAmelCase    				= 1_60

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



 def    UpperCamelCase__  (     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(_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(_snake_case				,       env=os.environ.copy()     )



 def    UpperCamelCase__  (     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(_snake_case     ):
   lowerCAmelCase    				= cmd.copy()
   cmd_config.append(F'--fsdp_sharding_strategy={i+1}'     )
   if strategy != "FULL_SHARD":
    continue
   lowerCAmelCase    				= len(_snake_case     )
   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(_snake_case				,       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(_snake_case				,       env=os.environ.copy()     )



 def    UpperCamelCase__  (     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(_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(_snake_case				,       env=os.environ.copy()     )









import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)
			if "model" in sd.keys():
						__lowercase			       = torch.load(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# pop unnecessary weights
			__lowercase			       = [
			    '''decoder.version''',
			    '''decoder.output_projection.weight''',
			]
			for key in keys_to_delete:
						if key in sd:
									sd.pop(_UpperCamelCase	)

			__lowercase			       = {
			    '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
			    '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
			    '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
			    '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
			}
			for old_key, new_key in keys_to_rename.items():
						if old_key in sd:
									__lowercase			       = sd.pop(_UpperCamelCase	)

			__lowercase			       = list(sd.keys()	)
			for key in keys:
						if ".qkv_proj." in key:
									__lowercase			       = sd[key]
									# We split QKV in separate Q,K,V

									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.q_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.k_proj.'''	)
									__lowercase			       = key.replace('''.qkv_proj.'''							,      '''.v_proj.'''	)

									__lowercase			       = value.shape[0]
									assert depth % 3 == 0
									# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
									# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

									__lowercase			       = q
									__lowercase			       = k
									__lowercase			       = v
									del sd[key]

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

			# Check results
			Path(_UpperCamelCase	).mkdir(exist_ok=_UpperCamelCase	)
			model.save_pretrained(_UpperCamelCase	)


if __name__ == "__main__":
	a			:			List[Any]												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fairseq_path''',
	    type=str,
	    help=(
	        '''path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:'''
	        ''' https://huggingface.co/models?other=opt_metasq'''
	    ),
	)
	parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
	parser.add_argument('''--hf_config''', default=None, type=str, help='''Define HF config.''')
	a			:			Any												=					parser.parse_args()
	convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


