'''simple docstring'''

import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_ : List[Any]						  =      logging.get_logger(__name__)

lowerCAmelCase_ : Union[str, Any]						  =      "▁"

lowerCAmelCase_ : List[str]						  =      {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowerCAmelCase_ : Dict						  =      {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowerCAmelCase_ : List[str]						  =      {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowerCAmelCase_ : Optional[Any]						  =      {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}





class 							lowercase						(						__SCREAMING_SNAKE_CASE ):
   lowerCamelCase_    					=VOCAB_FILES_NAMES
   lowerCamelCase_    					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCamelCase_    					=PRETRAINED_VOCAB_FILES_MAP
   lowerCamelCase_    					=['input_ids', 'attention_mask']

   lowerCamelCase_    					=[]
   lowerCamelCase_    					=[]


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	List[Any]=None   ,		__lowerCAmelCase				:	Optional[Any]="<s>"   ,		__lowerCAmelCase				:	Optional[int]="</s>"   ,		__lowerCAmelCase				:	Union[str, Any]="</s>"   ,		__lowerCAmelCase				:	str="<pad>"   ,		__lowerCAmelCase				:	Union[str, Any]="<unk>"   ,		__lowerCAmelCase				:	Union[str, Any]="m2m100"   ,		__lowerCAmelCase				:	Any = None   ,		__lowerCAmelCase				:	Dict=8   ,		**__lowerCAmelCase				:	Union[str, Any]   ,		)      ->					List[str]:
         lowercase_     =      {} if sp_model_kwargs is None else sp_model_kwargs

         lowercase_     =      language_codes
         lowercase_     =      FAIRSEQ_LANGUAGE_CODES[language_codes]
         lowercase_     =      {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

         lowercase_     =      kwargs.get("additional_special_tokens"   ,		[])
         kwargs["additional_special_tokens"] += [
             self.get_lang_token(_a)
             for lang_code in fairseq_language_code
             if self.get_lang_token(_a) not in kwargs["additional_special_tokens"]
         ]

         super().__init__(
             src_lang=_a   ,		tgt_lang=_a   ,		bos_token=_a   ,		eos_token=_a   ,		sep_token=_a   ,		unk_token=_a   ,		pad_token=_a   ,		language_codes=_a   ,		sp_model_kwargs=self.sp_model_kwargs   ,		num_madeup_words=_a   ,		**_a   ,		)

         lowercase_     =      vocab_file
         lowercase_     =      load_json(_a)
         lowercase_     =      {v: k for k, v in self.encoder.items()}
         lowercase_     =      spm_file
         lowercase_     =      load_spm(_a   ,		self.sp_model_kwargs)

         lowercase_     =      len(self.encoder)

         lowercase_     =      {
             self.get_lang_token(_a): self.encoder_size + i for i, lang_code in enumerate(_a)
         }
         lowercase_     =      {lang_code: self.encoder_size + i for i, lang_code in enumerate(_a)}
         lowercase_     =      {v: k for k, v in self.lang_token_to_id.items()}

         lowercase_     =      src_lang if src_lang is not None else "en"
         lowercase_     =      tgt_lang
         lowercase_     =      self.get_lang_id(self._src_lang)
         self.set_src_lang_special_tokens(self._src_lang)

         lowercase_     =      num_madeup_words


   @property
   def __UpperCAmelCase       (   self				:	int)      ->					Tuple:
         return len(self.encoder) + len(self.lang_token_to_id)


   @property
   def __UpperCAmelCase       (   self				:	int)      ->					Optional[int]:
         return self._src_lang


   @src_lang.setter
   def __UpperCAmelCase       (   self				:	Tuple   ,		__lowerCAmelCase				:	Optional[Any])      ->					List[str]:
         lowercase_     =      new_src_lang
         self.set_src_lang_special_tokens(self._src_lang)


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	int)      ->					Any:
         return self.sp_model.encode(_a   ,		out_type=_a)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Union[str, Any])      ->					Tuple:
         if token in self.lang_token_to_id:
               return self.lang_token_to_id[token]
         return self.encoder.get(_a   ,		self.encoder[self.unk_token])


   def __UpperCAmelCase       (   self				:	Dict   ,		__lowerCAmelCase				:	Any)      ->					Optional[int]:
         if index in self.id_to_lang_token:
               return self.id_to_lang_token[index]
         return self.decoder.get(_a   ,		self.unk_token)


   def __UpperCAmelCase       (   self				:	Any   ,		__lowerCAmelCase				:	Union[str, Any])      ->					Tuple:
         lowercase_     =      []
         lowercase_     =      ""
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     out_string += self.sp_model.decode(_a) + token
                     lowercase_     =      []
               else:
                     current_sub_tokens.append(_a)
         out_string += self.sp_model.decode(_a)
         return out_string.strip()


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	List[str]   ,		__lowerCAmelCase				:	Any = None   ,		__lowerCAmelCase				:	Optional[int] = False)      ->					List[Any]:

         if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_a   ,		token_ids_a=_a   ,		already_has_special_tokens=_a)

         lowercase_     =      [1] * len(self.prefix_tokens)
         lowercase_     =      [1] * len(self.suffix_tokens)
         if token_ids_a is None:
               return prefix_ones + ([0] * len(_a)) + suffix_ones
         return prefix_ones + ([0] * len(_a)) + ([0] * len(_a)) + suffix_ones


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	List[str] = None)      ->					Any:
         if token_ids_a is None:
               return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


   def __UpperCAmelCase       (   self				:	int)      ->					Union[str, Any]:
         lowercase_     =      {self.convert_ids_to_tokens(_a): i for i in range(self.vocab_size)}
         vocab.update(self.added_tokens_encoder)
         return vocab


   def __getstate__(   self				:	Any)      ->					str:
         lowercase_     =      self.__dict__.copy()
         lowercase_     =      None
         return state


   def __setstate__(   self				:	List[Any]   ,		__lowerCAmelCase				:	Any)      ->					List[Any]:
         lowercase_     =      d

         # for backward compatibility
         if not hasattr(self   ,		"sp_model_kwargs"):
               lowercase_     =      {}

         lowercase_     =      load_spm(self.spm_file   ,		self.sp_model_kwargs)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	List[Any] = None)      ->					str:
         lowercase_     =      Path(_a)
         if not save_dir.is_dir():
               raise OSError(F'{save_directory} should be a directory')
         lowercase_     =      save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
         )
         lowercase_     =      save_dir / (
             (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
         )

         save_json(self.encoder   ,		_a)

         if os.path.abspath(self.spm_file) != os.path.abspath(_a) and os.path.isfile(self.spm_file):
               copyfile(self.spm_file   ,		_a)
         elif not os.path.isfile(self.spm_file):
               with open(_a   ,		"wb") as fi:
                     lowercase_     =      self.sp_model.serialized_model_proto()
                     fi.write(_a)

         return (str(_a), str(_a))


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	List[Any]   ,		__lowerCAmelCase				:	Tuple = "en"   ,		__lowerCAmelCase				:	Dict = None   ,		__lowerCAmelCase				:	Tuple = "ro"   ,		**__lowerCAmelCase				:	List[Any]   ,		)      ->					Tuple:
         lowercase_     =      src_lang
         lowercase_     =      tgt_lang
         self.set_src_lang_special_tokens(self.src_lang)
         return super().prepare_seqaseq_batch(_a   ,		_a   ,		**_a)


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	str   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[int]   ,		**__lowerCAmelCase				:	Optional[int])      ->					Optional[Any]:
         if src_lang is None or tgt_lang is None:
               raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
         lowercase_     =      src_lang
         lowercase_     =      self(_a   ,		add_special_tokens=_a   ,		**_a)
         lowercase_     =      self.get_lang_id(_a)
         lowercase_     =      tgt_lang_id
         return inputs


   def __UpperCAmelCase       (   self				:	Dict)      ->					List[Any]:
         self.set_src_lang_special_tokens(self.src_lang)


   def __UpperCAmelCase       (   self				:	Optional[Any])      ->					List[Any]:
         self.set_tgt_lang_special_tokens(self.tgt_lang)


   def __UpperCAmelCase       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	Optional[int])      ->					Optional[int]:
         lowercase_     =      self.get_lang_token(_a)
         lowercase_     =      self.lang_token_to_id[lang_token]
         lowercase_     =      [self.cur_lang_id]
         lowercase_     =      [self.eos_token_id]


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	int)      ->					List[str]:
         lowercase_     =      self.get_lang_token(_a)
         lowercase_     =      self.lang_token_to_id[lang_token]
         lowercase_     =      [self.cur_lang_id]
         lowercase_     =      [self.eos_token_id]


   def __UpperCAmelCase       (   self				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any])      ->					Tuple:
         return self.lang_code_to_token[lang]


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	List[str])      ->					int:
         lowercase_     =      self.get_lang_token(_a)
         return self.lang_token_to_id[lang_token]




def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						Dict[str, Any]				)	->							sentencepiece.SentencePieceProcessor:
      '''simple docstring'''


      lowercase_     =      sentencepiece.SentencePieceProcessor(**lowerCAmelCase__				)
      spm.Load(str(lowerCAmelCase__				)				)
      return spm


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


      with open(lowerCAmelCase__ ,		"r"				) as f:
            return json.load(lowerCAmelCase__				)






def __a (	__lowerCamelCase			:						str ,		__lowerCamelCase			:						str				)	->							None:
      '''simple docstring'''


      with open(lowerCAmelCase__ ,		"w"				) as f:
            json.dump(lowerCAmelCase__ ,		lowerCAmelCase__ ,		indent=2				)







'''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())






