

import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
     from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image





class  __UpperCAmelCase					:



   def __init__(							self							:					Any,		__A							:					Optional[Any],		__A							:					Optional[Any]=3,		__A							:					Union[str, Any]=3_2,		__A							:					Optional[int]=3,		__A							:					str=1_0,		__A							:					Union[str, Any]=[8, 1_6, 3_2, 6_4],		__A							:					List[str]=[1, 1, 2, 1],		__A							:					Dict=True,		__A							:					List[Any]=True,		__A							:					int="relu",		__A							:					Optional[Any]=3,		__A							:					Any=None,		__A							:					Any=["stage2", "stage3", "stage4"],		__A							:					Optional[int]=[2, 3, 4],		__A							:					Any=1,		):
     UpperCAmelCase	:							Optional[int]						=		parent
     UpperCAmelCase	:							Union[str, Any]						=		batch_size
     UpperCAmelCase	:							Optional[int]						=		image_size
     UpperCAmelCase	:							List[Any]						=		num_channels
     UpperCAmelCase	:							Optional[Any]						=		embeddings_size
     UpperCAmelCase	:							List[Any]						=		hidden_sizes
     UpperCAmelCase	:							Any						=		depths
     UpperCAmelCase	:							int						=		is_training
     UpperCAmelCase	:							Optional[int]						=		use_labels
     UpperCAmelCase	:							Optional[Any]						=		hidden_act
     UpperCAmelCase	:							Any						=		num_labels
     UpperCAmelCase	:							List[Any]						=		scope
     UpperCAmelCase	:							int						=		len(__A						)
     UpperCAmelCase	:							Union[str, Any]						=		out_features
     UpperCAmelCase	:							List[str]						=		out_indices
     UpperCAmelCase	:							Tuple						=		num_groups



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Any						=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

     UpperCAmelCase	:							Dict						=		None
     if self.use_labels:
       UpperCAmelCase	:							List[str]						=		ids_tensor([self.batch_size],		self.num_labels						)

     UpperCAmelCase	:							List[str]						=		self.get_config()

     return config, pixel_values, labels



   def 	__magic_name__   (							self							:					Tuple						):
     return BitConfig(
         num_channels=self.num_channels,		embeddings_size=self.embeddings_size,		hidden_sizes=self.hidden_sizes,		depths=self.depths,		hidden_act=self.hidden_act,		num_labels=self.num_labels,		out_features=self.out_features,		out_indices=self.out_indices,		num_groups=self.num_groups,		)



   def 	__magic_name__   (							self							:					int,		__A							:					str,		__A							:					List[Any],		__A							:					Any						):
     UpperCAmelCase	:							Optional[int]						=		BitModel(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Optional[int]						=		model(__A						)
     self.parent.assertEqual(
         result.last_hidden_state.shape,		(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2),		)



   def 	__magic_name__   (							self							:					List[Any],		__A							:					Any,		__A							:					Union[str, Any],		__A							:					Dict						):
     UpperCAmelCase	:							Optional[Any]						=		self.num_labels
     UpperCAmelCase	:							Union[str, Any]						=		BitForImageClassification(__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Optional[int]						=		model(__A,		labels=__A						)
     self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.num_labels)						)



   def 	__magic_name__   (							self							:					Optional[int],		__A							:					Any,		__A							:					List[str],		__A							:					str						):
     UpperCAmelCase	:							Any						=		BitBackbone(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Tuple						=		model(__A						)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps						),		len(config.out_features						)						)
     self.parent.assertListEqual(list(result.feature_maps[0].shape						),		[self.batch_size, self.hidden_sizes[1], 4, 4]						)

     # verify channels
     self.parent.assertEqual(len(model.channels						),		len(config.out_features						)						)
     self.parent.assertListEqual(model.channels,		config.hidden_sizes[1:]						)

     # verify backbone works with out_features=None
     UpperCAmelCase	:							List[str]						=		None
     UpperCAmelCase	:							Optional[Any]						=		BitBackbone(config=__A						)
     model.to(__A						)
     model.eval()
     UpperCAmelCase	:							Tuple						=		model(__A						)

     # verify feature maps
     self.parent.assertEqual(len(result.feature_maps						),		1						)
     self.parent.assertListEqual(list(result.feature_maps[0].shape						),		[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

     # verify channels
     self.parent.assertEqual(len(model.channels						),		1						)
     self.parent.assertListEqual(model.channels,		[config.hidden_sizes[-1]]						)





   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							str						=		self.prepare_config_and_inputs()
     UpperCAmelCase	:							Union[str, Any]						=		config_and_inputs
     UpperCAmelCase	:							Union[str, Any]						=		{'''pixel_values''': pixel_values}
     return config, inputs_dict






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   UpperCamelCase       					=					(
       {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
       if is_torch_available()
       else {}
   )

   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Optional[int]						=		BitModelTester(self						)
     UpperCAmelCase	:							Any						=		ConfigTester(self,		config_class=__A,		has_text_modality=__A						)



   def 	__magic_name__   (							self							:					List[str]						):
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return



   @unittest.skip(reason='''Bit does not output attentions'''						)
   def 	__magic_name__   (							self							:					List[Any]						):
     pass



   @unittest.skip(reason='''Bit does not use inputs_embeds'''						)
   def 	__magic_name__   (							self							:					Dict						):
     pass



   @unittest.skip(reason='''Bit does not support input and output embeddings'''						)
   def 	__magic_name__   (							self							:					List[Any]						):
     pass



   def 	__magic_name__   (							self							:					List[Any]						):
     UpperCAmelCase	:							Any						=		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       UpperCAmelCase	:							Optional[Any]						=		model_class(__A						)
       UpperCAmelCase	:							List[str]						=		inspect.signature(model.forward						)
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       UpperCAmelCase	:							List[str]						=		[*signature.parameters.keys()]

       UpperCAmelCase	:							List[Any]						=		['''pixel_values''']
       self.assertListEqual(arg_names[:1],		__A						)



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     UpperCAmelCase	:							Optional[int]						=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							List[str]						=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*__A						)



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=		self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       UpperCAmelCase	:							Dict						=		model_class(config=__A						)
       for name, module in model.named_modules():
         if isinstance(__A,		(nn.BatchNormad, nn.GroupNorm)						):
           self.assertTrue(
               torch.all(module.weight == 1						),		msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',		)
           self.assertTrue(
               torch.all(module.bias == 0						),		msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',		)



   def 	__magic_name__   (							self							:					Dict						):
     def check_hidden_states_output(__A							:					List[Any],		__A							:					Optional[int],		__A							:					int						):
       UpperCAmelCase	:							int						=		model_class(__A						)
       model.to(__A						)
       model.eval()

       with torch.no_grad():
         UpperCAmelCase	:							Any						=		model(**self._prepare_for_class(__A,		__A						)						)

       UpperCAmelCase	:							Union[str, Any]						=		outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       UpperCAmelCase	:							Any						=		self.model_tester.num_stages
       self.assertEqual(len(__A						),		expected_num_stages + 1						)

       # Bit's feature maps are of shape (batch_size, num_channels, height, width)
       self.assertListEqual(
           list(hidden_states[0].shape[-2:]						),		[self.model_tester.image_size // 4, self.model_tester.image_size // 4],		)

     UpperCAmelCase	:							Dict						=		self.model_tester.prepare_config_and_inputs_for_common()
     UpperCAmelCase	:							Union[str, Any]						=		['''preactivation''', '''bottleneck''']
     for model_class in self.all_model_classes:
       for layer_type in layers_type:
         UpperCAmelCase	:							int						=		layer_type
         UpperCAmelCase	:							List[Any]						=		True
         check_hidden_states_output(__A,		__A,		__A						)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         UpperCAmelCase	:							Dict						=		True

         check_hidden_states_output(__A,		__A,		__A						)



   @unittest.skip(reason='''Bit does not use feedforward chunking'''						)
   def 	__magic_name__   (							self							:					List[str]						):
     pass



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							str						=		self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*__A						)





   @slow
   def 	__magic_name__   (							self							:					List[Any]						):
     for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:							List[str]						=		BitModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)






def       a__					(		)  ->						Union[str, Any]:
  UpperCAmelCase	:							Optional[int]						=		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
  return image





@require_torch
@require_vision
class  __UpperCAmelCase					(	unittest.TestCase						):



   @cached_property
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     return (
         BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						) if is_vision_available() else None
     )





   @slow
   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							List[Any]						=		BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__A						)

     UpperCAmelCase	:							str						=		self.default_image_processor
     UpperCAmelCase	:							Dict						=		prepare_img()
     UpperCAmelCase	:							Tuple						=		image_processor(images=__A,		return_tensors='''pt'''						).to(__A						)

     # forward pass
     with torch.no_grad():
       UpperCAmelCase	:							List[Any]						=		model(**__A						)

     # verify the logits
     UpperCAmelCase	:							Dict						=		torch.Size((1, 1_0_0_0)						)
     self.assertEqual(outputs.logits.shape,		__A						)

     UpperCAmelCase	:							int						=		torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]						).to(__A						)

     self.assertTrue(torch.allclose(outputs.logits[0, :3],		__A,		atol=1E-4						)						)






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(BitBackbone,) if is_torch_available() else ()
   UpperCamelCase       					=					BitConfig

   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							int						=		BitModelTester(self						)








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()						)





