'''simple docstring'''

import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCAmelCase_ : int						  =      logging.getLogger(__name__)


def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						List[str]				)	->							str:
      '''simple docstring'''


      lowercase_     =      np.argmax(__lowerCamelCase ,		axis=1				)
      return np.sum(outputs == labels				)


def __a (	__lowerCamelCase			:						List[Any]				)	->							int:
      '''simple docstring'''


      with open(__lowerCamelCase ,		encoding="utf_8"				) as f:
            lowercase_     =      csv.reader(__lowerCamelCase				)
            lowercase_     =      []
            next(__lowerCamelCase				)  # skip the first line
            for line in tqdm(__lowerCamelCase				):
                  output.append((" ".join(line[1:5]				), line[5], line[6], int(line[-1]				) - 1)				)
      return output


def __a (	__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						Any ,		__lowerCamelCase			:						List[Any] ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						str ,		__lowerCamelCase			:						Optional[Any]				)	->							Any:
      '''simple docstring'''


      lowercase_     =      []
      for dataset in encoded_datasets:
            lowercase_     =      len(__lowerCamelCase				)
            lowercase_     =      np.zeros((n_batch, 2, input_len) ,		dtype=np.intaa				)
            lowercase_     =      np.zeros((n_batch, 2) ,		dtype=np.intaa				)
            lowercase_     =      np.full((n_batch, 2, input_len) ,		fill_value=-100 ,		dtype=np.intaa				)
            lowercase_     =      np.zeros((n_batch,) ,		dtype=np.intaa				)
            for (
                i,
                (story, conta, conta, mc_label),
            ) in enumerate(__lowerCamelCase				):
                  lowercase_     =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowercase_     =      [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowercase_     =      with_conta
                  lowercase_     =      with_conta
                  lowercase_     =      len(__lowerCamelCase				) - 1
                  lowercase_     =      len(__lowerCamelCase				) - 1
                  lowercase_     =      with_conta
                  lowercase_     =      with_conta
                  lowercase_     =      mc_label
            lowercase_     =      (input_ids, mc_token_ids, lm_labels, mc_labels)
            tensor_datasets.append(tuple(torch.tensor(__lowerCamelCase				) for t in all_inputs				)				)
      return tensor_datasets


def __a (	)	->							int:
      '''simple docstring'''


      lowercase_     =      argparse.ArgumentParser()
      parser.add_argument("--model_name" ,		type=__lowerCamelCase ,		default="openai-gpt" ,		help="pretrained model name"				)
      parser.add_argument("--do_train" ,		action="store_true" ,		help="Whether to run training."				)
      parser.add_argument("--do_eval" ,		action="store_true" ,		help="Whether to run eval on the dev set."				)
      parser.add_argument(
          "--output_dir" ,		default=__lowerCamelCase ,		type=__lowerCamelCase ,		required=__lowerCamelCase ,		help="The output directory where the model predictions and checkpoints will be written." ,		)
      parser.add_argument("--train_dataset" ,		type=__lowerCamelCase ,		default=""				)
      parser.add_argument("--eval_dataset" ,		type=__lowerCamelCase ,		default=""				)
      parser.add_argument("--seed" ,		type=__lowerCamelCase ,		default=42				)
      parser.add_argument("--num_train_epochs" ,		type=__lowerCamelCase ,		default=3				)
      parser.add_argument("--train_batch_size" ,		type=__lowerCamelCase ,		default=8				)
      parser.add_argument("--eval_batch_size" ,		type=__lowerCamelCase ,		default=16				)
      parser.add_argument("--adam_epsilon" ,		default=1e-8 ,		type=__lowerCamelCase ,		help="Epsilon for Adam optimizer."				)
      parser.add_argument("--max_grad_norm" ,		type=__lowerCamelCase ,		default=1				)
      parser.add_argument(
          "--max_steps" ,		default=-1 ,		type=__lowerCamelCase ,		help=(
              "If > 0: set total number of training                         steps to perform. Override num_train_epochs."
          ) ,		)
      parser.add_argument(
          "--gradient_accumulation_steps" ,		type=__lowerCamelCase ,		default=1 ,		help="Number of updates steps to accumulate before                        performing a backward/update pass." ,		)
      parser.add_argument("--learning_rate" ,		type=__lowerCamelCase ,		default=6.2_5e-5				)
      parser.add_argument("--warmup_steps" ,		default=0 ,		type=__lowerCamelCase ,		help="Linear warmup over warmup_steps."				)
      parser.add_argument("--lr_schedule" ,		type=__lowerCamelCase ,		default="warmup_linear"				)
      parser.add_argument("--weight_decay" ,		type=__lowerCamelCase ,		default=0.0_1				)
      parser.add_argument("--lm_coef" ,		type=__lowerCamelCase ,		default=0.9				)
      parser.add_argument("--n_valid" ,		type=__lowerCamelCase ,		default=374				)

      parser.add_argument("--server_ip" ,		type=__lowerCamelCase ,		default="" ,		help="Can be used for distant debugging."				)
      parser.add_argument("--server_port" ,		type=__lowerCamelCase ,		default="" ,		help="Can be used for distant debugging."				)
      lowercase_     =      parser.parse_args()
      print(__lowerCamelCase				)

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print("Waiting for debugger attach"				)
            ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,		redirect_output=__lowerCamelCase				)
            ptvsd.wait_for_attach()

      random.seed(args.seed				)
      np.random.seed(args.seed				)
      torch.manual_seed(args.seed				)
      torch.cuda.manual_seed_all(args.seed				)

      lowercase_     =      torch.device("cuda" if torch.cuda.is_available() else "cpu"				)
      lowercase_     =      torch.cuda.device_count()
      logger.info("device: {}, n_gpu {}".format(__lowerCamelCase ,		__lowerCamelCase				)				)

      if not args.do_train and not args.do_eval:
            raise ValueError("At least one of `do_train` or `do_eval` must be True."				)

      if not os.path.exists(args.output_dir				):
            os.makedirs(args.output_dir				)

      # Load tokenizer and model
      # This loading functions also add new tokens and embeddings called `special tokens`
      # These new embeddings will be fine-tuned on the RocStories dataset
      lowercase_     =      ["_start_", "_delimiter_", "_classify_"]
      lowercase_     =      OpenAIGPTTokenizer.from_pretrained(args.model_name				)
      tokenizer.add_tokens(__lowerCamelCase				)
      lowercase_     =      tokenizer.convert_tokens_to_ids(__lowerCamelCase				)
      lowercase_     =      OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name				)
      model.resize_token_embeddings(len(__lowerCamelCase				)				)
      model.to(__lowerCamelCase				)

      # Load and encode the datasets
      def tokenize_and_encode(__lowerCamelCase			:						Tuple				):
            if isinstance(__lowerCamelCase ,		__lowerCamelCase				):
                  return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCamelCase				)				)
            elif isinstance(__lowerCamelCase ,		__lowerCamelCase				):
                  return obj
            return [tokenize_and_encode(__lowerCamelCase				) for o in obj]

      logger.info("Encoding dataset..."				)
      lowercase_     =      load_rocstories_dataset(args.train_dataset				)
      lowercase_     =      load_rocstories_dataset(args.eval_dataset				)
      lowercase_     =      (train_dataset, eval_dataset)
      lowercase_     =      tokenize_and_encode(__lowerCamelCase				)

      # Compute the max input length for the Transformer
      lowercase_     =      model.config.n_positions // 2 - 2
      lowercase_     =      max(
          len(story[:max_length]				) + max(len(conta[:max_length]				) ,		len(conta[:max_length]				)				) + 3
          for dataset in encoded_datasets
          for story, conta, conta, _ in dataset				)
      lowercase_     =      min(__lowerCamelCase ,		model.config.n_positions				)  # Max size of input for the pre-trained model

      # Prepare inputs tensors and dataloaders
      lowercase_     =      pre_process_datasets(__lowerCamelCase ,		__lowerCamelCase ,		__lowerCamelCase ,		*__lowerCamelCase				)
      lowercase_ ,							lowercase_     =      tensor_datasets[0], tensor_datasets[1]

      lowercase_     =      TensorDataset(*__lowerCamelCase				)
      lowercase_     =      RandomSampler(__lowerCamelCase				)
      lowercase_     =      DataLoader(__lowerCamelCase ,		sampler=__lowerCamelCase ,		batch_size=args.train_batch_size				)

      lowercase_     =      TensorDataset(*__lowerCamelCase				)
      lowercase_     =      SequentialSampler(__lowerCamelCase				)
      lowercase_     =      DataLoader(__lowerCamelCase ,		sampler=__lowerCamelCase ,		batch_size=args.eval_batch_size				)

      # Prepare optimizer
      if args.do_train:
            if args.max_steps > 0:
                  lowercase_     =      args.max_steps
                  lowercase_     =      args.max_steps // (len(__lowerCamelCase				) // args.gradient_accumulation_steps) + 1
            else:
                  lowercase_     =      len(__lowerCamelCase				) // args.gradient_accumulation_steps * args.num_train_epochs

            lowercase_     =      list(model.named_parameters()				)
            lowercase_     =      ["bias", "LayerNorm.bias", "LayerNorm.weight"]
            lowercase_     =      [
                {
                    "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay				)],
                    "weight_decay": args.weight_decay,
                },
                {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay				)], "weight_decay": 0.0},
            ]
            lowercase_     =      AdamW(__lowerCamelCase ,		lr=args.learning_rate ,		eps=args.adam_epsilon				)
            lowercase_     =      get_linear_schedule_with_warmup(
                __lowerCamelCase ,		num_warmup_steps=args.warmup_steps ,		num_training_steps=__lowerCamelCase				)

      if args.do_train:
            lowercase_ ,							lowercase_ ,							lowercase_     =      0, 0, None
            model.train()
            for _ in trange(int(args.num_train_epochs				) ,		desc="Epoch"				):
                  lowercase_     =      0
                  lowercase_     =      0
                  lowercase_     =      tqdm(__lowerCamelCase ,		desc="Training"				)
                  for step, batch in enumerate(__lowerCamelCase				):
                        lowercase_     =      tuple(t.to(__lowerCamelCase				) for t in batch				)
                        lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_     =      batch
                        lowercase_     =      model(__lowerCamelCase ,		mc_token_ids=__lowerCamelCase ,		lm_labels=__lowerCamelCase ,		mc_labels=__lowerCamelCase				)
                        lowercase_     =      args.lm_coef * losses[0] + losses[1]
                        loss.backward()
                        optimizer.step()
                        scheduler.step()
                        optimizer.zero_grad()
                        tr_loss += loss.item()
                        lowercase_     =      (
                            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                        )
                        nb_tr_steps += 1
                        lowercase_     =      "Training loss: {:.2e} lr: {:.2e}".format(__lowerCamelCase ,		scheduler.get_lr()[0]				)

    # Save a trained model
      if args.do_train:
            # Save a trained model, configuration and tokenizer
            lowercase_     =      model.module if hasattr(__lowerCamelCase ,		"module"				) else model  # Only save the model itself

            # If we save using the predefined names, we can load using `from_pretrained`
            lowercase_     =      os.path.join(args.output_dir ,		__lowerCamelCase				)
            lowercase_     =      os.path.join(args.output_dir ,		__lowerCamelCase				)

            torch.save(model_to_save.state_dict() ,		__lowerCamelCase				)
            model_to_save.config.to_json_file(__lowerCamelCase				)
            tokenizer.save_vocabulary(args.output_dir				)

            # Load a trained model and vocabulary that you have fine-tuned
            lowercase_     =      OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir				)
            lowercase_     =      OpenAIGPTTokenizer.from_pretrained(args.output_dir				)
            model.to(__lowerCamelCase				)

      if args.do_eval:
            model.eval()
            lowercase_ ,							lowercase_     =      0, 0
            lowercase_ ,							lowercase_     =      0, 0
            for batch in tqdm(__lowerCamelCase ,		desc="Evaluating"				):
                  lowercase_     =      tuple(t.to(__lowerCamelCase				) for t in batch				)
                  lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_     =      batch
                  with torch.no_grad():
                        lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_     =      model(
                            __lowerCamelCase ,		mc_token_ids=__lowerCamelCase ,		lm_labels=__lowerCamelCase ,		mc_labels=__lowerCamelCase				)

                  lowercase_     =      mc_logits.detach().cpu().numpy()
                  lowercase_     =      mc_labels.to("cpu"				).numpy()
                  lowercase_     =      accuracy(__lowerCamelCase ,		__lowerCamelCase				)

                  eval_loss += mc_loss.mean().item()
                  eval_accuracy += tmp_eval_accuracy

                  nb_eval_examples += input_ids.size(0				)
                  nb_eval_steps += 1

            lowercase_     =      eval_loss / nb_eval_steps
            lowercase_     =      eval_accuracy / nb_eval_examples
            lowercase_     =      tr_loss / nb_tr_steps if args.do_train else None
            lowercase_     =      {"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

            lowercase_     =      os.path.join(args.output_dir ,		"eval_results.txt"				)
            with open(__lowerCamelCase ,		"w"				) as writer:
                  logger.info("***** Eval results *****"				)
                  for key in sorted(result.keys()				):
                        logger.info("  %s = %s" ,		__lowerCamelCase ,		str(result[key]				)				)
                        writer.write("%s = %s\n" % (key, str(result[key]				))				)


if __name__ == "__main__":
       main()







'''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)

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





@require_fsdp
@require_cuda
class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	str)      ->					Union[str, Any]:
         super().setUp()

         lowercase_     =      dict(
             ACCELERATE_USE_FSDP="true"   ,		MASTER_ADDR="localhost"   ,		MASTER_PORT="10999"   ,		RANK="0"   ,		LOCAL_RANK="0"   ,		WORLD_SIZE="1"   ,		)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[Any]:
         from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					List[str]:
         from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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

         for i, state_dict_type in enumerate(__lowerCAmelCase):
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      state_dict_type
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      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				:	Optional[int])      ->					List[Any]:
         lowercase_     =      AutoModel.from_pretrained(__lowerCAmelCase)
         for policy in FSDP_AUTO_WRAP_POLICY:
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      policy
               if policy == "TRANSFORMER_BASED_WRAP":
                     lowercase_     =      "BertLayer"
               elif policy == "SIZE_BASED_WRAP":
                     lowercase_     =      "2000"
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      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)

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

         lowercase_     =      self.dist_env.copy()
         lowercase_     =      "SIZE_BASED_WRAP"
         lowercase_     =      "0"
         with mockenv_context(**__lowerCAmelCase):
               lowercase_     =      FullyShardedDataParallelPlugin()
               fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase)
               self.assertIsNone(fsdp_plugin.auto_wrap_policy)


   def __UpperCAmelCase       (   self				:	Union[str, Any])      ->					int:
         from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
         from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

         for mp_dtype in dtypes:
               lowercase_     =      self.dist_env.copy()
               lowercase_     =      mp_dtype
               with mockenv_context(**__lowerCAmelCase):
                     lowercase_     =      Accelerator()
                     if mp_dtype == "fp16":
                           lowercase_     =      torch.floataa
                     elif mp_dtype == "bf16":
                           lowercase_     =      torch.bfloataa
                     lowercase_     =      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				:	List[str])      ->					Dict:
         from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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





@require_fsdp
@require_multi_gpu
@slow
class 							lowercase						(						__lowerCamelCase ):


   def __UpperCAmelCase       (   self				:	Optional[int])      ->					str:
         super().setUp()
         lowercase_     =      0.82
         lowercase_     =      [
             "fsdp_shard_grad_op_transformer_based_wrap",
             "fsdp_full_shard_transformer_based_wrap",
         ]
         lowercase_     =      {
             "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
         }
         lowercase_     =      160
         lowercase_     =      160

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


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					Optional[Any]:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_performance.py")
         lowercase_     =      ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
         for config in self.performance_configs:
               lowercase_     =      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				:	Dict)      ->					Dict:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_checkpointing.py")
         lowercase_     =      [
             "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):
               lowercase_     =      cmd.copy()
               cmd_config.append(F'--fsdp_sharding_strategy={i+1}')
               if strategy != "FULL_SHARD":
                     continue
               lowercase_     =      len(__lowerCAmelCase)
               for state_dict_type in FSDP_STATE_DICT_TYPE:
                     lowercase_     =      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())

                     lowercase_     =      cmd_config[:-1]
                     lowercase_     =      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				:	Optional[int])      ->					int:
         lowercase_     =      os.path.join(self.test_scripts_folder   ,		"test_peak_memory_usage.py")
         lowercase_     =      [
             "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():
               lowercase_     =      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())






