

import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
         CTRLForSequenceClassification,
         CTRLLMHeadModel,
         CTRLModel,
     )





class  __UpperCAmelCase					:



   def __init__(							self							:					Optional[Any],		__A							:					Any,		__A							:					Any=1_4,		__A							:					Union[str, Any]=7,		__A							:					List[str]=True,		__A							:					Optional[int]=True,		__A							:					Dict=True,		__A							:					str=True,		__A							:					str=True,		__A							:					Tuple=9_9,		__A							:					Any=3_2,		__A							:					Tuple=5,		__A							:					Optional[Any]=4,		__A							:					int=3_7,		__A							:					Optional[int]="gelu",		__A							:					List[str]=0.1,		__A							:					Optional[int]=0.1,		__A							:					Any=5_1_2,		__A							:					List[str]=1_6,		__A							:					Any=2,		__A							:					Any=0.0_2,		__A							:					Any=3,		__A							:					str=4,		__A							:					List[Any]=None,		):
     UpperCAmelCase	:							Tuple						=			parent
     UpperCAmelCase	:							Dict						=			batch_size
     UpperCAmelCase	:							str						=			seq_length
     UpperCAmelCase	:							Dict						=			is_training
     UpperCAmelCase	:							Dict						=			use_token_type_ids
     UpperCAmelCase	:							Union[str, Any]						=			use_input_mask
     UpperCAmelCase	:							List[Any]						=			use_labels
     UpperCAmelCase	:							List[str]						=			use_mc_token_ids
     UpperCAmelCase	:							Any						=			vocab_size
     UpperCAmelCase	:							List[Any]						=			hidden_size
     UpperCAmelCase	:							str						=			num_hidden_layers
     UpperCAmelCase	:							Any						=			num_attention_heads
     UpperCAmelCase	:							List[str]						=			intermediate_size
     UpperCAmelCase	:							List[str]						=			hidden_act
     UpperCAmelCase	:							Optional[Any]						=			hidden_dropout_prob
     UpperCAmelCase	:							int						=			attention_probs_dropout_prob
     UpperCAmelCase	:							Dict						=			max_position_embeddings
     UpperCAmelCase	:							Optional[Any]						=			type_vocab_size
     UpperCAmelCase	:							List[str]						=			type_sequence_label_size
     UpperCAmelCase	:							List[str]						=			initializer_range
     UpperCAmelCase	:							Dict						=			num_labels
     UpperCAmelCase	:							str						=			num_choices
     UpperCAmelCase	:							Any						=			scope
     UpperCAmelCase	:							str						=			self.vocab_size - 1



   def 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length],		self.vocab_size						)

     UpperCAmelCase	:							str						=			None
     if self.use_input_mask:
       UpperCAmelCase	:							int						=			random_attention_mask([self.batch_size, self.seq_length]						)

     UpperCAmelCase	:							List[str]						=			None
     if self.use_token_type_ids:
       UpperCAmelCase	:							Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length],		self.type_vocab_size						)

     UpperCAmelCase	:							List[Any]						=			None
     if self.use_mc_token_ids:
       UpperCAmelCase	:							List[str]						=			ids_tensor([self.batch_size, self.num_choices],		self.seq_length						)

     UpperCAmelCase	:							Union[str, Any]						=			None
     UpperCAmelCase	:							List[str]						=			None
     UpperCAmelCase	:							Any						=			None
     if self.use_labels:
       UpperCAmelCase	:							Union[str, Any]						=			ids_tensor([self.batch_size],		self.type_sequence_label_size						)
       UpperCAmelCase	:							Optional[Any]						=			ids_tensor([self.batch_size, self.seq_length],		self.num_labels						)
       UpperCAmelCase	:							str						=			ids_tensor([self.batch_size],		self.num_choices						)

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

     UpperCAmelCase	:							Optional[Any]						=			ids_tensor([self.num_hidden_layers, self.num_attention_heads],		2						)

     return (
         config,
         input_ids,
         input_mask,
         head_mask,
         token_type_ids,
         mc_token_ids,
         sequence_labels,
         token_labels,
         choice_labels,
     )



   def 	__magic_name__   (							self							:					List[str]						):
     return CTRLConfig(
         vocab_size=self.vocab_size,		n_embd=self.hidden_size,		n_layer=self.num_hidden_layers,		n_head=self.num_attention_heads,		n_positions=self.max_position_embeddings,		pad_token_id=self.pad_token_id,		)



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

     model(__A,		token_type_ids=__A,		head_mask=__A						)
     model(__A,		token_type_ids=__A						)
     UpperCAmelCase	:							Tuple						=			model(__A						)
     self.parent.assertEqual(result.last_hidden_state.shape,		(self.batch_size, self.seq_length, self.hidden_size)						)
     self.parent.assertEqual(len(result.past_key_values						),		config.n_layer						)



   def 	__magic_name__   (							self							:					str,		__A							:					Dict,		__A							:					str,		__A							:					List[str],		__A							:					Optional[int],		__A							:					Union[str, Any],		*__A							:					Union[str, Any]						):
     UpperCAmelCase	:							List[str]						=			CTRLLMHeadModel(__A						)
     model.to(__A						)
     model.eval()

     UpperCAmelCase	:							str						=			model(__A,		token_type_ids=__A,		labels=__A						)
     self.parent.assertEqual(result.loss.shape,		()						)
     self.parent.assertEqual(result.logits.shape,		(self.batch_size, self.seq_length, self.vocab_size)						)



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							List[str]						=			self.prepare_config_and_inputs()

     (
         (
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						(
         UpperCAmelCase
     )			,						
     )	:							Dict						=			config_and_inputs

     UpperCAmelCase	:							Any						=			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

     return config, inputs_dict





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






@require_torch
class  __UpperCAmelCase					(	lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__      ,  unittest.TestCase						):
   UpperCamelCase       					=					(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
   UpperCamelCase       					=					(CTRLLMHeadModel,) if is_torch_available() else ()
   UpperCamelCase       					=					(
       {
           """feature-extraction""": CTRLModel,
           """text-classification""": CTRLForSequenceClassification,
           """text-generation""": CTRLLMHeadModel,
           """zero-shot""": CTRLForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   UpperCamelCase       					=					True
   UpperCamelCase       					=					False
   UpperCamelCase       					=					False



   def 	__magic_name__   (							self							:					Tuple,		__A							:					List[Any],		__A							:					Optional[int],		__A							:					Optional[int],		__A							:					List[Any],		__A							:					Optional[Any]						):
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
       # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
       # `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
       # config could not be created.
       return True

     return False



   def 	__magic_name__   (							self							:					Optional[int]						):
     UpperCAmelCase	:							Union[str, Any]						=			CTRLModelTester(self						)
     UpperCAmelCase	:							Union[str, Any]						=			ConfigTester(self,		config_class=__A,		n_embd=3_7						)



   def 	__magic_name__   (							self							:					Tuple						):
     super().tearDown()
     # clean-up as much as possible GPU memory occupied by PyTorch
     gc.collect()
     torch.cuda.empty_cache()



   def 	__magic_name__   (							self							:					Dict						):
     self.config_tester.run_common_tests()



   def 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							str						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_ctrl_model(*__A						)



   def 	__magic_name__   (							self							:					Dict						):
     UpperCAmelCase	:							Tuple						=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head_model(*__A						)



   @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     pass



   @slow
   def 	__magic_name__   (							self							:					Union[str, Any]						):
     for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:							Optional[int]						=			CTRLModel.from_pretrained(__A						)
       self.assertIsNotNone(__A						)





   @unittest.skip('''The model doesn\'t support left padding'''						)  # and it's not used enough to be worth fixing :)
   def 	__magic_name__   (							self							:					List[Any]						):
     pass






@require_torch
class  __UpperCAmelCase					(	unittest.TestCase						):



   def 	__magic_name__   (							self							:					Union[str, Any]						):
     super().tearDown()
     # clean-up as much as possible GPU memory occupied by PyTorch
     gc.collect()
     torch.cuda.empty_cache()





   @slow
   def 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Optional[Any]						=			CTRLLMHeadModel.from_pretrained('''ctrl'''						)
     model.to(__A						)
     UpperCAmelCase	:							Union[str, Any]						=			torch.tensor(
         [[1_1_8_5_9, 0, 1_6_1_1, 8]],		dtype=torch.long,		device=__A						)  # Legal the president is
     UpperCAmelCase	:							Union[str, Any]						=			[
         1_1_8_5_9,
         0,
         1_6_1_1,
         8,
         5,
         1_5_0,
         2_6_4_4_9,
         2,
         1_9,
         3_4_8,
         4_6_9,
         3,
         2_5_9_5,
         4_8,
         2_0_7_4_0,
         2_4_6_5_3_3,
         2_4_6_5_3_3,
         1_9,
         3_0,
         5,
     ]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

     UpperCAmelCase	:							str						=			model.generate(__A,		do_sample=__A						)
     self.assertListEqual(output_ids[0].tolist(),		__A						)








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()						)





