'''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)

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







@require_fsdp
@require_cuda
class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	Dict							)  ->    int:
  super().setUp()

  __lowerCAmelCase											=						dict(
      ACCELERATE_USE_FSDP="""true"""      ,       MASTER_ADDR="""localhost"""      ,       MASTER_PORT="""10999"""      ,       RANK="""0"""      ,       LOCAL_RANK="""0"""      ,       WORLD_SIZE="""1"""      ,       )
 def a					(      self   :	Union[str, Any]							)  ->    Any:
  from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

  for i, strategy in enumerate(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						self.dist_env.copy()
   __lowerCAmelCase											=						f"""{i + 1}"""
   __lowerCAmelCase											=						strategy
   with mockenv_context(**SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						FullyShardedDataParallelPlugin()
    self.assertEqual(fsdp_plugin.sharding_strategy      ,       ShardingStrategy(i + 1							)							)
 def a					(      self   :	Dict							)  ->    List[Any]:
  from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

  for i, prefetch_policy in enumerate(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						self.dist_env.copy()
   __lowerCAmelCase											=						prefetch_policy
   with mockenv_context(**SCREAMING_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 a					(      self   :	Optional[int]							)  ->    List[Any]:
  from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

  for i, state_dict_type in enumerate(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						self.dist_env.copy()
   __lowerCAmelCase											=						state_dict_type
   with mockenv_context(**SCREAMING_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 a					(      self   :	Dict							)  ->    str:
  __lowerCAmelCase											=						AutoModel.from_pretrained(SCREAMING_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(**SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						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							)

  __lowerCAmelCase											=						self.dist_env.copy()
  __lowerCAmelCase											=						"""TRANSFORMER_BASED_WRAP"""
  __lowerCAmelCase											=						"""T5Layer"""
  with mockenv_context(**SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						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							)							)

  __lowerCAmelCase											=						self.dist_env.copy()
  __lowerCAmelCase											=						"""SIZE_BASED_WRAP"""
  __lowerCAmelCase											=						"""0"""
  with mockenv_context(**SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						FullyShardedDataParallelPlugin()
   fsdp_plugin.set_auto_wrap_policy(SCREAMING_SNAKE_CASE__							)
   self.assertIsNone(fsdp_plugin.auto_wrap_policy							)
 def a					(      self   :	Optional[int]							)  ->    Optional[Any]:
  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(**SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						Accelerator()
    if mp_dtype == "fp16":
     __lowerCAmelCase											=						torch.floataa
    elif mp_dtype == "bf16":
     __lowerCAmelCase											=						torch.bfloataa
    __lowerCAmelCase											=						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 a					(      self   :	Tuple							)  ->    Dict:
  from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	Union[str, Any]							)  ->    Dict:
  super().setUp()
  __lowerCAmelCase											=						0.8_2
  __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 a					(      self   :	int							)  ->    List[Any]:
  __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(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 a					(      self   :	int							)  ->    Dict:
  __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(SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase											=						cmd.copy()
   cmd_config.append(f"""--fsdp_sharding_strategy={i+1}"""							)
   if strategy != "FULL_SHARD":
    continue
   __lowerCAmelCase											=						len(SCREAMING_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(SCREAMING_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(SCREAMING_SNAKE_CASE__      ,       env=os.environ.copy()							)





 def a					(      self   :	Any							)  ->    Tuple:
  __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(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 torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Optional[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						[
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """decoder.output_projection.weight""",
     """_float_tensor""",
     """encoder.embed_positions._float_tensor""",
     """decoder.embed_positions._float_tensor""",
 ]
 for k in ignore_keys:
  state_dict.pop(snake_case_						,		snake_case_      )







def      UpperCamelCase_						(					snake_case_       :       Optional[Any]      )					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase							,       __lowerCAmelCase											=						emb.weight.shape
 __lowerCAmelCase											=						nn.Linear(snake_case_						,		snake_case_						,		bias=snake_case_      )
 __lowerCAmelCase											=						emb.weight.data
 return lin_layer







def      UpperCamelCase_						(					snake_case_       :       Any      )					->     Any:






 '''simple docstring'''

 __lowerCAmelCase											=						torch.load(snake_case_						,		map_location="""cpu"""      )
 __lowerCAmelCase											=						mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
 __lowerCAmelCase											=						mam_aaa["""model"""]
 remove_ignore_keys_(snake_case_      )
 __lowerCAmelCase											=						state_dict["""encoder.embed_tokens.weight"""].shape[0]

 __lowerCAmelCase											=						MaMaaaConfig(
     vocab_size=snake_case_						,		max_position_embeddings=10_24						,		encoder_layers=args.encoder_layers						,		decoder_layers=args.decoder_layers						,		encoder_attention_heads=args.encoder_attention_heads						,		decoder_attention_heads=args.decoder_attention_heads						,		encoder_ffn_dim=args.encoder_ffn_embed_dim						,		decoder_ffn_dim=args.decoder_ffn_embed_dim						,		d_model=args.encoder_embed_dim						,		encoder_layerdrop=args.encoder_layerdrop						,		decoder_layerdrop=args.decoder_layerdrop						,		dropout=args.dropout						,		attention_dropout=args.attention_dropout						,		activation_dropout=args.activation_dropout						,		activation_function="""relu"""						,		)

 __lowerCAmelCase											=						state_dict["""decoder.embed_tokens.weight"""]
 __lowerCAmelCase											=						MaMaaaForConditionalGeneration(snake_case_      )
 model.model.load_state_dict(snake_case_						,		strict=snake_case_      )
 __lowerCAmelCase											=						make_linear_from_emb(model.model.shared      )

 return model


if __name__ == "__main__":
 _A			:      Union[str, Any]					      =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
 parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
 _A			:      str					      =				parser.parse_args()
 _A			:      Optional[int]					      =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
 model.save_pretrained(args.pytorch_dump_folder_path)






