




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)

_lowerCAmelCase						:   Union[str, Any]		=   "bert-base-cased"
_lowerCAmelCase						:   Dict		=   "fp16"
_lowerCAmelCase						:   Dict		=   "bf16"
_lowerCAmelCase						:   List[Any]		=   [FPaa, BFaa]



@require_fsdp
@require_cuda
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			List[str]:
       super().setUp()

       UpperCAmelCase__							 =						dict(
           ACCELERATE_USE_FSDP="true" ,      MASTER_ADDR="localhost" ,      MASTER_PORT="10999" ,      RANK="0" ,      LOCAL_RANK="0" ,      WORLD_SIZE="1" ,      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

       for i, strategy in enumerate(lowerCamelCase      ):
        UpperCAmelCase__							 =						self.dist_env.copy()
        UpperCAmelCase__							 =						f'''{i + 1}'''
        UpperCAmelCase__							 =						strategy
        with mockenv_context(**lowerCamelCase      ):
         UpperCAmelCase__							 =						FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.sharding_strategy ,      ShardingStrategy(i + 1      )      )
      def 	UpperCAmelCase_						(			self   :Optional[Any]      )     ->			Union[str, Any]:
       from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

       for i, prefetch_policy in enumerate(lowerCamelCase      ):
        UpperCAmelCase__							 =						self.dist_env.copy()
        UpperCAmelCase__							 =						prefetch_policy
        with mockenv_context(**lowerCamelCase      ):
         UpperCAmelCase__							 =						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   :List[str]      )     ->			Any:
       from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

       for i, state_dict_type in enumerate(lowerCamelCase      ):
        UpperCAmelCase__							 =						self.dist_env.copy()
        UpperCAmelCase__							 =						state_dict_type
        with mockenv_context(**lowerCamelCase      ):
         UpperCAmelCase__							 =						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   :Tuple      )     ->			Any:
       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase      )
       for policy in FSDP_AUTO_WRAP_POLICY:
        UpperCAmelCase__							 =						self.dist_env.copy()
        UpperCAmelCase__							 =						policy
        if policy == "TRANSFORMER_BASED_WRAP":
         UpperCAmelCase__							 =						"BertLayer"
        elif policy == "SIZE_BASED_WRAP":
         UpperCAmelCase__							 =						"2000"
        with mockenv_context(**lowerCamelCase      ):
         UpperCAmelCase__							 =						FullyShardedDataParallelPlugin()
         fsdp_plugin.set_auto_wrap_policy(lowerCamelCase      )
         if policy == "NO_WRAP":
          self.assertIsNone(fsdp_plugin.auto_wrap_policy      )
         else:
          self.assertIsNotNone(fsdp_plugin.auto_wrap_policy      )

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

       UpperCAmelCase__							 =						self.dist_env.copy()
       UpperCAmelCase__							 =						"SIZE_BASED_WRAP"
       UpperCAmelCase__							 =						"0"
       with mockenv_context(**lowerCamelCase      ):
        UpperCAmelCase__							 =						FullyShardedDataParallelPlugin()
        fsdp_plugin.set_auto_wrap_policy(lowerCamelCase      )
        self.assertIsNone(fsdp_plugin.auto_wrap_policy      )
      def 	UpperCAmelCase_						(			self   :Optional[int]      )     ->			Tuple:
       from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
       from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

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







      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Optional[int]:
       from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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



@require_fsdp
@require_multi_gpu
@slow
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			str:
       super().setUp()
       UpperCAmelCase__							 =						0.82
       UpperCAmelCase__							 =						[
           "fsdp_shard_grad_op_transformer_based_wrap",
           "fsdp_full_shard_transformer_based_wrap",
       ]
       UpperCAmelCase__							 =						{
           "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
       }
       UpperCAmelCase__							 =						160
       UpperCAmelCase__							 =						160

       UpperCAmelCase__							 =						inspect.getfile(accelerate.test_utils      )
       UpperCAmelCase__							 =						os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["scripts", "external_deps"]      )
      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Dict:
       UpperCAmelCase__							 =						os.path.join(self.test_scripts_folder ,      "test_performance.py"      )
       UpperCAmelCase__							 =						["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
       for config in self.performance_configs:
        UpperCAmelCase__							 =						cmd.copy()
        for i, strategy in enumerate(lowerCamelCase      ):
         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(lowerCamelCase ,      env=os.environ.copy()      )
      def 	UpperCAmelCase_						(			self   :Tuple      )     ->			Dict:
       UpperCAmelCase__							 =						os.path.join(self.test_scripts_folder ,      "test_checkpointing.py"      )
       UpperCAmelCase__							 =						[
           "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(lowerCamelCase      ):
        UpperCAmelCase__							 =						cmd.copy()
        cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''      )
        if strategy != "FULL_SHARD":
         continue
        UpperCAmelCase__							 =						len(lowerCamelCase      )
        for state_dict_type in FSDP_STATE_DICT_TYPE:
         UpperCAmelCase__							 =						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(lowerCamelCase ,      env=os.environ.copy()      )

         UpperCAmelCase__							 =						cmd_config[:-1]
         UpperCAmelCase__							 =						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(lowerCamelCase ,      env=os.environ.copy()      )







      def 	UpperCAmelCase_						(			self   :List[Any]      )     ->			Dict:
       UpperCAmelCase__							 =						os.path.join(self.test_scripts_folder ,      "test_peak_memory_usage.py"      )
       UpperCAmelCase__							 =						[
           "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():
        UpperCAmelCase__							 =						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(lowerCamelCase      ):
          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(lowerCamelCase ,      env=os.environ.copy()      )











import torch

from transformers import AutoModel
class 			_UpperCamelCase      (				torch.nn.Module		):
      def __init__(			self   :str ,      lowerCamelCase   :Tuple="sayef/fsner-bert-base-uncased"      )     ->			int:
       super(lowerCamelCase ,      self      ).__init__()

       UpperCAmelCase__							 =						AutoModel.from_pretrained(lowerCamelCase ,      return_dict=lowerCamelCase      )
       UpperCAmelCase__							 =						torch.nn.CosineSimilarity(3 ,      1e-08      )
       UpperCAmelCase__							 =						torch.nn.Softmax(dim=1      )
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      **lowerCamelCase   :Tuple      )     ->			Dict:
       return self.bert(**lowerCamelCase      ).last_hidden_state
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       return token_embeddings.sum(2 ,      keepdim=lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Dict ,      lowerCamelCase   :List[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]=1      )     ->			Dict:
       return self.softmax(T * self.cos(lowerCamelCase ,      lowerCamelCase      )      )







      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :Any ,      lowerCamelCase   :Any      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						W_supports["sizes"].tolist()
       UpperCAmelCase__							 =						W_supports["start_token_id"].item()
       UpperCAmelCase__							 =						W_supports["end_token_id"].item()

       del W_supports["sizes"]
       del W_supports["start_token_id"]
       del W_supports["end_token_id"]

       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )
       UpperCAmelCase__							 =						self.BERT(**lowerCamelCase      )

       UpperCAmelCase__							 =						None
       UpperCAmelCase__							 =						None

       UpperCAmelCase__							 =						W_supports["input_ids"] == start_token_id
       UpperCAmelCase__							 =						W_supports["input_ids"] == end_token_id

       for i, size in enumerate(lowerCamelCase      ):
        if i == 0:
         UpperCAmelCase__							 =						0
        else:
         UpperCAmelCase__							 =						support_sizes[i - 1]

        UpperCAmelCase__							 =						S[s : s + size][start_token_masks[s : s + size]]
        UpperCAmelCase__							 =						S[s : s + size][end_token_masks[s : s + size]]

        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_start.T      ).sum(1      ).softmax(0      )
        UpperCAmelCase__							 =						torch.matmul(q[i] ,      s_end.T      ).sum(1      ).softmax(0      )

        if p_starts is not None:
         UpperCAmelCase__							 =						torch.vstack((p_starts, p_start)      )
         UpperCAmelCase__							 =						torch.vstack((p_ends, p_end)      )
        else:
         UpperCAmelCase__							 =						p_start
         UpperCAmelCase__							 =						p_end

       return p_starts, p_ends





