
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)

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




@require_fsdp
@require_cuda
class        UpperCAmelCase_			( _a     ):


  '''simple docstring'''







  def 							_lowercase							( self			: Optional[int]    )			->	Optional[Any]:







    """simple docstring"""






    super().setUp()

    __magic_name__				     =       dict(
        ACCELERATE_USE_FSDP="""true"""			,							MASTER_ADDR="""localhost"""			,							MASTER_PORT="""10999"""			,							RANK="""0"""			,							LOCAL_RANK="""0"""			,							WORLD_SIZE="""1"""			,							)
  def 							_lowercase							( self			: Union[str, Any]    )			->	str:







    """simple docstring"""






    from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

    for i, strategy in enumerate(snake_case_    ):
      __magic_name__				     =       self.dist_env.copy()
      __magic_name__				     =       F'''{i + 1}'''
      __magic_name__				     =       strategy
      with mockenv_context(**snake_case_    ):
        __magic_name__				     =       FullyShardedDataParallelPlugin()
        self.assertEqual(fsdp_plugin.sharding_strategy			,							ShardingStrategy(i + 1    )    )
  def 							_lowercase							( self			: List[str]    )			->	str:







    """simple docstring"""






    from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

    for i, prefetch_policy in enumerate(snake_case_    ):
      __magic_name__				     =       self.dist_env.copy()
      __magic_name__				     =       prefetch_policy
      with mockenv_context(**snake_case_    ):
        __magic_name__				     =       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			: Dict    )			->	Dict:







    """simple docstring"""






    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

    for i, state_dict_type in enumerate(snake_case_    ):
      __magic_name__				     =       self.dist_env.copy()
      __magic_name__				     =       state_dict_type
      with mockenv_context(**snake_case_    ):
        __magic_name__				     =       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			: Tuple    )			->	Optional[int]:







    """simple docstring"""






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

    __magic_name__				     =       self.dist_env.copy()
    __magic_name__				     =       """TRANSFORMER_BASED_WRAP"""
    __magic_name__				     =       """T5Layer"""
    with mockenv_context(**snake_case_    ):
      __magic_name__				     =       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    )    )

    __magic_name__				     =       self.dist_env.copy()
    __magic_name__				     =       """SIZE_BASED_WRAP"""
    __magic_name__				     =       """0"""
    with mockenv_context(**snake_case_    ):
      __magic_name__				     =       FullyShardedDataParallelPlugin()
      fsdp_plugin.set_auto_wrap_policy(snake_case_    )
      self.assertIsNone(fsdp_plugin.auto_wrap_policy    )
  def 							_lowercase							( self			: Optional[int]    )			->	List[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:
      __magic_name__				     =       self.dist_env.copy()
      __magic_name__				     =       mp_dtype
      with mockenv_context(**snake_case_    ):
        __magic_name__				     =       Accelerator()
        if mp_dtype == "fp16":
          __magic_name__				     =       torch.floataa
        elif mp_dtype == "bf16":
          __magic_name__				     =       torch.bfloataa
        __magic_name__				     =       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 							_lowercase							( self			: Tuple    )			->	int:







    """simple docstring"""






    from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







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


  '''simple docstring'''







  def 							_lowercase							( self			: List[str]    )			->	int:







    """simple docstring"""






    super().setUp()
    __magic_name__				     =       0.82
    __magic_name__				     =       [
        """fsdp_shard_grad_op_transformer_based_wrap""",
        """fsdp_full_shard_transformer_based_wrap""",
    ]
    __magic_name__				     =       {
        """multi_gpu_fp16""": 3200,
        """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2000,
        """fsdp_full_shard_transformer_based_wrap_fp16""": 1900,
        # 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
    }
    __magic_name__				     =       160
    __magic_name__				     =       160

    __magic_name__				     =       inspect.getfile(accelerate.test_utils    )
    __magic_name__				     =       os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """external_deps"""]    )
  def 							_lowercase							( self			: Any    )			->	Tuple:







    """simple docstring"""






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







    """simple docstring"""






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

        __magic_name__				     =       cmd_config[:-1]
        __magic_name__				     =       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 							_lowercase							( self			: List[str]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       os.path.join(self.test_scripts_folder			,							"""test_peak_memory_usage.py"""    )
    __magic_name__				     =       [
        """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():
      __magic_name__				     =       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()    )






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a          =		logging.get_logger(__name__)

__a          =		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		"glpn"




    def __init__(    self					:     Optional[Any] ,     snake_case_					:     List[str]=3 ,     snake_case_					:     Dict=4 ,     snake_case_					:     List[Any]=[2, 2, 2, 2] ,     snake_case_					:     int=[8, 4, 2, 1] ,     snake_case_					:     List[str]=[32, 64, 160, 256] ,     snake_case_					:     Tuple=[7, 3, 3, 3] ,     snake_case_					:     List[Any]=[4, 2, 2, 2] ,     snake_case_					:     Tuple=[1, 2, 5, 8] ,     snake_case_					:     List[str]=[4, 4, 4, 4] ,     snake_case_					:     Optional[int]="gelu" ,     snake_case_					:     Dict=0.0 ,     snake_case_					:     Union[str, Any]=0.0 ,     snake_case_					:     List[Any]=0.02 ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Any=1E-6 ,     snake_case_					:     Dict=64 ,     snake_case_					:     Tuple=10 ,     snake_case_					:     List[Any]=-1 ,     **snake_case_					:     Optional[Any] ,     ):
          super().__init__(**snake_case_						)

          snake_case__  :    Optional[Any]    						=      num_channels
          snake_case__  :    Dict    						=      num_encoder_blocks
          snake_case__  :    Tuple    						=      depths
          snake_case__  :    Union[str, Any]    						=      sr_ratios
          snake_case__  :    Tuple    						=      hidden_sizes
          snake_case__  :    Optional[Any]    						=      patch_sizes
          snake_case__  :    int    						=      strides
          snake_case__  :    List[Any]    						=      mlp_ratios
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    Dict    						=      hidden_act
          snake_case__  :    int    						=      hidden_dropout_prob
          snake_case__  :    Optional[Any]    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      initializer_range
          snake_case__  :    List[str]    						=      drop_path_rate
          snake_case__  :    int    						=      layer_norm_eps
          snake_case__  :    Tuple    						=      decoder_hidden_size
          snake_case__  :    List[Any]    						=      max_depth
          snake_case__  :    Dict    						=      head_in_index
