

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_lowerCamelCase    :       Optional[int]        =							logging.get_logger(__name__)

_lowerCamelCase    :       List[str]        =							OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

_lowerCamelCase    :       List[Any]        =							_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)

def       a__					(		UpperCAmelCase							:      str      )  ->						Optional[Any]:






  for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
    if class_name in extractors:
      UpperCAmelCase	:							Optional[int]						=		model_type_to_module_name(_lowercase      )

      UpperCAmelCase	:							Tuple						=		importlib.import_module(f'''.{module_name}'''    ,			'''transformers.models'''      )
      try:
        return getattr(_lowercase    ,			_lowercase      )
      except AttributeError:
        continue

  for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
    if getattr(_lowercase    ,			'''__name__'''    ,			_lowercase      ) == class_name:
      return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
  UpperCAmelCase	:							str						=		importlib.import_module('''transformers'''      )
  if hasattr(_lowercase    ,			_lowercase      ):
    return getattr(_lowercase    ,			_lowercase      )

  return None

def       a__					(		UpperCAmelCase							:      Union[str, os.PathLike]    ,			UpperCAmelCase							:      Optional[Union[str, os.PathLike]] = None    ,			UpperCAmelCase							:      bool = False    ,			UpperCAmelCase							:      bool = False    ,			UpperCAmelCase							:      Optional[Dict[str, str]] = None    ,			UpperCAmelCase							:      Optional[Union[bool, str]] = None    ,			UpperCAmelCase							:      Optional[str] = None    ,			UpperCAmelCase							:      bool = False    ,			**UpperCAmelCase							:      Union[str, Any]    ,			)  ->						Tuple:
  UpperCAmelCase	:							str						=		get_file_from_repo(
      _lowercase    ,			_lowercase    ,			cache_dir=_lowercase    ,			force_download=_lowercase    ,			resume_download=_lowercase    ,			proxies=_lowercase    ,			use_auth_token=_lowercase    ,			revision=_lowercase    ,			local_files_only=_lowercase    ,			)
  if resolved_config_file is None:
    logger.info(
        '''Could not locate the image processor configuration file, will try to use the model config instead.'''      )
    return {}

  with open(_lowercase    ,			encoding='''utf-8'''      ) as reader:
    return json.load(_lowercase      )






class  __UpperCAmelCase					:



   def __init__(							self							:					int						):
     raise EnvironmentError(
         '''AutoImageProcessor is designed to be instantiated '''
         '''using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'''						)



   @classmethod
   @replace_list_option_in_docstrings(lowerCAmelCase__						)
   def 	__magic_name__   (							cls							:					Any,		__A							:					Tuple,		**__A							:					Dict						):
     UpperCAmelCase	:							int						=		kwargs.pop('''config''',		lowerCAmelCase__						)
     UpperCAmelCase	:							int						=		kwargs.pop('''trust_remote_code''',		lowerCAmelCase__						)
     UpperCAmelCase	:							List[str]						=		True

     UpperCAmelCase	:							Optional[int]						=		ImageProcessingMixin.get_image_processor_dict(lowerCAmelCase__,		**lowerCAmelCase__						)
     UpperCAmelCase	:							List[Any]						=		config_dict.get('''image_processor_type''',		lowerCAmelCase__						)
     UpperCAmelCase	:							Any						=		None
     if "AutoImageProcessor" in config_dict.get('''auto_map''',		{}						):
       UpperCAmelCase	:							Optional[int]						=		config_dict["auto_map"]["AutoImageProcessor"]

     # If we still don't have the image processor class, check if we're loading from a previous feature extractor config
     # and if so, infer the image processor class from there.
     if image_processor_class is None and image_processor_auto_map is None:
       UpperCAmelCase	:							List[Any]						=		config_dict.pop('''feature_extractor_type''',		lowerCAmelCase__						)
       if feature_extractor_class is not None:
         logger.warning(
             '''Could not find image processor class in the image processor config or the model config. Loading'''
             ''' based on pattern matching with the model\'s feature extractor configuration.'''						)
         UpperCAmelCase	:							Optional[int]						=		feature_extractor_class.replace('''FeatureExtractor''',		'''ImageProcessor'''						)
       if "AutoFeatureExtractor" in config_dict.get('''auto_map''',		{}						):
         UpperCAmelCase	:							List[str]						=		config_dict["auto_map"]["AutoFeatureExtractor"]
         UpperCAmelCase	:							Union[str, Any]						=		feature_extractor_auto_map.replace('''FeatureExtractor''',		'''ImageProcessor'''						)
         logger.warning(
             '''Could not find image processor auto map in the image processor config or the model config.'''
             ''' Loading based on pattern matching with the model\'s feature extractor configuration.'''						)

        # If we don't find the image processor class in the image processor config, let's try the model config.
     if image_processor_class is None and image_processor_auto_map is None:
       if not isinstance(lowerCAmelCase__,		lowerCAmelCase__						):
         UpperCAmelCase	:							List[Any]						=		AutoConfig.from_pretrained(lowerCAmelCase__,		**lowerCAmelCase__						)
       # It could be in `config.image_processor_type``
       UpperCAmelCase	:							List[Any]						=		getattr(lowerCAmelCase__,		'''image_processor_type''',		lowerCAmelCase__						)
       if hasattr(lowerCAmelCase__,		'''auto_map'''						) and "AutoImageProcessor" in config.auto_map:
         UpperCAmelCase	:							Optional[Any]						=		config.auto_map["AutoImageProcessor"]

     if image_processor_class is not None:
       UpperCAmelCase	:							List[str]						=		image_processor_class_from_name(lowerCAmelCase__						)

     UpperCAmelCase	:							Optional[Any]						=		image_processor_auto_map is not None
     UpperCAmelCase	:							Tuple						=		image_processor_class is not None or type(lowerCAmelCase__						) in IMAGE_PROCESSOR_MAPPING
     UpperCAmelCase	:							int						=		resolve_trust_remote_code(
         lowerCAmelCase__,		lowerCAmelCase__,		lowerCAmelCase__,		lowerCAmelCase__						)

     if has_remote_code and trust_remote_code:
       UpperCAmelCase	:							int						=		get_class_from_dynamic_module(
           lowerCAmelCase__,		lowerCAmelCase__,		**lowerCAmelCase__						)
       UpperCAmelCase	:							Tuple						=		kwargs.pop('''code_revision''',		lowerCAmelCase__						)
       if os.path.isdir(lowerCAmelCase__						):
         image_processor_class.register_for_auto_class()
       return image_processor_class.from_dict(lowerCAmelCase__,		**lowerCAmelCase__						)
     elif image_processor_class is not None:
       return image_processor_class.from_dict(lowerCAmelCase__,		**lowerCAmelCase__						)
     # Last try: we use the IMAGE_PROCESSOR_MAPPING.
     elif type(lowerCAmelCase__						) in IMAGE_PROCESSOR_MAPPING:
       UpperCAmelCase	:							Dict						=		IMAGE_PROCESSOR_MAPPING[type(lowerCAmelCase__						)]
       return image_processor_class.from_dict(lowerCAmelCase__,		**lowerCAmelCase__						)

     raise ValueError(
         F'''Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '''
         F'''`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '''
         F'''`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()						)}'''						)





   @staticmethod
   def 	__magic_name__   (							__A							:					Any,		__A							:					Optional[int]						):
     IMAGE_PROCESSOR_MAPPING.register(lowerCAmelCase__,		lowerCAmelCase__						)








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    :       List[str]        =							"bert-base-cased"
_lowerCamelCase    :       str        =							"fp16"
_lowerCamelCase    :       Optional[int]        =							"bf16"
_lowerCamelCase    :       List[str]        =							[FPaa, BFaa]





@require_fsdp
@require_cuda
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					Tuple						):
     super().setUp()

     UpperCAmelCase	:							Optional[Any]						=			dict(
         ACCELERATE_USE_FSDP='''true''',		MASTER_ADDR='''localhost''',		MASTER_PORT='''10999''',		RANK='''0''',		LOCAL_RANK='''0''',		WORLD_SIZE='''1''',		)



   def 	__magic_name__   (							self							:					int						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

     for i, strategy in enumerate(__A						):
       UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
       UpperCAmelCase	:							Union[str, Any]						=			F'''{i + 1}'''
       UpperCAmelCase	:							Union[str, Any]						=			strategy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Union[str, Any]						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.sharding_strategy,		ShardingStrategy(i + 1						)						)



   def 	__magic_name__   (							self							:					Dict						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

     for i, prefetch_policy in enumerate(__A						):
       UpperCAmelCase	:							int						=			self.dist_env.copy()
       UpperCAmelCase	:							Dict						=			prefetch_policy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
         if prefetch_policy == "NO_PREFETCH":
           self.assertIsNone(fsdp_plugin.backward_prefetch						)
         else:
           self.assertEqual(fsdp_plugin.backward_prefetch,		BackwardPrefetch(i + 1						)						)



   def 	__magic_name__   (							self							:					Any						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

     for i, state_dict_type in enumerate(__A						):
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			state_dict_type
       with mockenv_context(**__A						):
         UpperCAmelCase	:							str						=			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 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			AutoModel.from_pretrained(__A						)
     for policy in FSDP_AUTO_WRAP_POLICY:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							List[Any]						=			policy
       if policy == "TRANSFORMER_BASED_WRAP":
         UpperCAmelCase	:							Tuple						=			'''BertLayer'''
       elif policy == "SIZE_BASED_WRAP":
         UpperCAmelCase	:							List[str]						=			'''2000'''
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[Any]						=			FullyShardedDataParallelPlugin()
         fsdp_plugin.set_auto_wrap_policy(__A						)
         if policy == "NO_WRAP":
           self.assertIsNone(fsdp_plugin.auto_wrap_policy						)
         else:
           self.assertIsNotNone(fsdp_plugin.auto_wrap_policy						)

     UpperCAmelCase	:							List[str]						=			self.dist_env.copy()
     UpperCAmelCase	:							Tuple						=			'''TRANSFORMER_BASED_WRAP'''
     UpperCAmelCase	:							Optional[Any]						=			'''T5Layer'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							int						=			FullyShardedDataParallelPlugin()
       with self.assertRaises(__A						) as cm:
         fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception						)						)

     UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
     UpperCAmelCase	:							str						=			'''SIZE_BASED_WRAP'''
     UpperCAmelCase	:							str						=			'''0'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
       fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertIsNone(fsdp_plugin.auto_wrap_policy						)



   def 	__magic_name__   (							self							:					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:
       UpperCAmelCase	:							List[Any]						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			mp_dtype
       with mockenv_context(**__A						):
         UpperCAmelCase	:							int						=			Accelerator()
         if mp_dtype == "fp16":
           UpperCAmelCase	:							Any						=			torch.floataa
         elif mp_dtype == "bf16":
           UpperCAmelCase	:							Any						=			torch.bfloataa
         UpperCAmelCase	:							Optional[Any]						=			MixedPrecision(param_dtype=__A,		reduce_dtype=__A,		buffer_dtype=__A						)
         self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy,		__A						)
         if mp_dtype == FPaa:
           self.assertTrue(isinstance(accelerator.scaler,		__A						)						)
         elif mp_dtype == BFaa:
           self.assertIsNone(accelerator.scaler						)
         AcceleratorState._reset_state(__A						)





   def 	__magic_name__   (							self							:					Optional[int]						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

     for flag in [True, False]:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			str(__A						).lower()
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Union[str, Any]						=			FullyShardedDataParallelPlugin()
         self.assertEqual(fsdp_plugin.cpu_offload,		CPUOffload(offload_params=__A						)						)






@require_fsdp
@require_multi_gpu
@slow
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					List[Any]						):
     super().setUp()
     UpperCAmelCase	:							int						=			0.8_2
     UpperCAmelCase	:							List[str]						=			[
         '''fsdp_shard_grad_op_transformer_based_wrap''',
         '''fsdp_full_shard_transformer_based_wrap''',
     ]
     UpperCAmelCase	:							int						=			{
         '''multi_gpu_fp16''': 3_2_0_0,
         '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
         '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
         # 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	:							List[str]						=			1_6_0
     UpperCAmelCase	:							Optional[int]						=			1_6_0

     UpperCAmelCase	:							Union[str, Any]						=			inspect.getfile(accelerate.test_utils						)
     UpperCAmelCase	:							Dict						=			os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''external_deps''']						)



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							str						=			os.path.join(self.test_scripts_folder,		'''test_performance.py'''						)
     UpperCAmelCase	:							Any						=			['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
     for config in self.performance_configs:
       UpperCAmelCase	:							Union[str, Any]						=			cmd.copy()
       for i, strategy in enumerate(__A						):
         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(__A,		env=os.environ.copy()						)



   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Dict						=			os.path.join(self.test_scripts_folder,		'''test_checkpointing.py'''						)
     UpperCAmelCase	:							Optional[int]						=			[
         '''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(__A						):
       UpperCAmelCase	:							Optional[Any]						=			cmd.copy()
       cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''						)
       if strategy != "FULL_SHARD":
         continue
       UpperCAmelCase	:							Any						=			len(__A						)
       for state_dict_type in FSDP_STATE_DICT_TYPE:
         UpperCAmelCase	:							List[Any]						=			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(__A,		env=os.environ.copy()						)

         UpperCAmelCase	:							Tuple						=			cmd_config[:-1]
         UpperCAmelCase	:							List[str]						=			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(__A,		env=os.environ.copy()						)





   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Optional[Any]						=			os.path.join(self.test_scripts_folder,		'''test_peak_memory_usage.py'''						)
     UpperCAmelCase	:							str						=			[
         '''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	:							List[Any]						=			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(__A						):
           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(__A,		env=os.environ.copy()						)





