

import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


_lowerCamelCase    :       Optional[Any]        =							"base_with_context"

def       a__					(		UpperCAmelCase							:      List[Any]    ,			UpperCAmelCase							:      int      )  ->						int:
  UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']      )      )
  UpperCAmelCase	:							Dict						=			nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,			requires_grad=UpperCAmelCase      )
  for lyr_num, lyr in enumerate(model.encoders      ):
    UpperCAmelCase	:							str						=			weights[f'''layers_{lyr_num}''']
    UpperCAmelCase	:							Optional[Any]						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

    UpperCAmelCase	:							int						=			ly_weight['''attention''']
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Union[str, Any]						=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
    UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

    UpperCAmelCase	:							Optional[Any]						=			nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Tuple						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

  UpperCAmelCase	:							str						=			nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )
  return model

def       a__					(		UpperCAmelCase							:      Dict    ,			UpperCAmelCase							:      Optional[int]      )  ->						Any:
  UpperCAmelCase	:							Optional[Any]						=			nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T      )      )

  UpperCAmelCase	:							Dict						=			nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,			requires_grad=UpperCAmelCase      )

  for lyr_num, lyr in enumerate(model.encoders      ):
    UpperCAmelCase	:							int						=			weights[f'''layers_{lyr_num}''']
    UpperCAmelCase	:							List[Any]						=			ly_weight['''attention''']

    UpperCAmelCase	:							Optional[int]						=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
    UpperCAmelCase	:							List[Any]						=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
    UpperCAmelCase	:							int						=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
    UpperCAmelCase	:							str						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']      )      )

    UpperCAmelCase	:							Any						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
    UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Union[str, Any]						=			nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )

  UpperCAmelCase	:							Any						=			nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']      )      )

  return model

def       a__					(		UpperCAmelCase							:      Optional[Any]    ,			UpperCAmelCase							:      Union[str, Any]      )  ->						Dict:
  UpperCAmelCase	:							Union[str, Any]						=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T      )      )
  UpperCAmelCase	:							Optional[Any]						=			nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T      )      )

  UpperCAmelCase	:							List[str]						=			nn.Parameter(
      torch.FloatTensor(weights['''Embed_0''']['''embedding''']      )    ,			requires_grad=UpperCAmelCase      )

  UpperCAmelCase	:							List[str]						=			nn.Parameter(
      torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T      )      )

  for lyr_num, lyr in enumerate(model.decoders      ):
    UpperCAmelCase	:							Optional[Any]						=			weights[f'''layers_{lyr_num}''']
    UpperCAmelCase	:							Union[str, Any]						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']      )      )

    UpperCAmelCase	:							int						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T      )      )

    UpperCAmelCase	:							Union[str, Any]						=			ly_weight['''self_attention''']
    UpperCAmelCase	:							Optional[int]						=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
    UpperCAmelCase	:							int						=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Tuple						=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Optional[Any]						=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )

    UpperCAmelCase	:							Dict						=			ly_weight['''MultiHeadDotProductAttention_0''']
    UpperCAmelCase	:							str						=			nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T      )      )
    UpperCAmelCase	:							int						=			nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T      )      )
    UpperCAmelCase	:							int						=			nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T      )      )
    UpperCAmelCase	:							Dict						=			nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T      )      )
    UpperCAmelCase	:							List[Any]						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']      )      )

    UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']      )      )
    UpperCAmelCase	:							Union[str, Any]						=			nn.Parameter(
        torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T      )      )
    UpperCAmelCase	:							str						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T      )      )
    UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T      )      )
    UpperCAmelCase	:							str						=			nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T      )      )

  UpperCAmelCase	:							List[str]						=			nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']      )      )

  UpperCAmelCase	:							int						=			nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T      )      )

  return model

def       a__					(		UpperCAmelCase							:      Dict      )  ->						Tuple:
  UpperCAmelCase	:							List[Any]						=			checkpoints.load_tax_checkpoint(args.checkpoint_path      )
  UpperCAmelCase	:							Dict						=			jnp.tree_util.tree_map(onp.array    ,			UpperCAmelCase      )

  UpperCAmelCase	:							int						=			[
      '''from __gin__ import dynamic_registration''',
      '''from music_spectrogram_diffusion.models.diffusion import diffusion_utils''',
      '''diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0''',
      '''diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()''',
  ]

  UpperCAmelCase	:							Any						=			os.path.join(args.checkpoint_path    ,			'''..'''    ,			'''config.gin'''      )
  UpperCAmelCase	:							str						=			inference.parse_training_gin_file(UpperCAmelCase    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			inference.InferenceModel(args.checkpoint_path    ,			UpperCAmelCase      )

  UpperCAmelCase	:							List[str]						=			DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''    ,			variance_type='''fixed_large'''      )

  UpperCAmelCase	:							List[Any]						=			SpectrogramNotesEncoder(
      max_length=synth_model.sequence_length['''inputs''']    ,			vocab_size=synth_model.model.module.config.vocab_size    ,			d_model=synth_model.model.module.config.emb_dim    ,			dropout_rate=synth_model.model.module.config.dropout_rate    ,			num_layers=synth_model.model.module.config.num_encoder_layers    ,			num_heads=synth_model.model.module.config.num_heads    ,			d_kv=synth_model.model.module.config.head_dim    ,			d_ff=synth_model.model.module.config.mlp_dim    ,			feed_forward_proj='''gated-gelu'''    ,			)

  UpperCAmelCase	:							Dict						=			SpectrogramContEncoder(
      input_dims=synth_model.audio_codec.n_dims    ,			targets_context_length=synth_model.sequence_length['''targets_context''']    ,			d_model=synth_model.model.module.config.emb_dim    ,			dropout_rate=synth_model.model.module.config.dropout_rate    ,			num_layers=synth_model.model.module.config.num_encoder_layers    ,			num_heads=synth_model.model.module.config.num_heads    ,			d_kv=synth_model.model.module.config.head_dim    ,			d_ff=synth_model.model.module.config.mlp_dim    ,			feed_forward_proj='''gated-gelu'''    ,			)

  UpperCAmelCase	:							List[Any]						=			TaFilmDecoder(
      input_dims=synth_model.audio_codec.n_dims    ,			targets_length=synth_model.sequence_length['''targets_context''']    ,			max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time    ,			d_model=synth_model.model.module.config.emb_dim    ,			num_layers=synth_model.model.module.config.num_decoder_layers    ,			num_heads=synth_model.model.module.config.num_heads    ,			d_kv=synth_model.model.module.config.head_dim    ,			d_ff=synth_model.model.module.config.mlp_dim    ,			dropout_rate=synth_model.model.module.config.dropout_rate    ,			)

  UpperCAmelCase	:							Optional[Any]						=			load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']    ,			UpperCAmelCase      )
  UpperCAmelCase	:							Dict						=			load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']    ,			UpperCAmelCase      )
  UpperCAmelCase	:							int						=			load_decoder(ta_checkpoint['''target''']['''decoder''']    ,			UpperCAmelCase      )

  UpperCAmelCase	:							Optional[Any]						=			OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''      )

  UpperCAmelCase	:							Optional[Any]						=			SpectrogramDiffusionPipeline(
      notes_encoder=UpperCAmelCase    ,			continuous_encoder=UpperCAmelCase    ,			decoder=UpperCAmelCase    ,			scheduler=UpperCAmelCase    ,			melgan=UpperCAmelCase    ,			)
  if args.save:
    pipe.save_pretrained(args.output_path      )


if __name__ == "__main__":
     _lowerCamelCase    :       List[str]        =							argparse.ArgumentParser()

     parser.add_argument("--output_path", default=None, type=str, required=True, help="Path to the converted model.")
     parser.add_argument(
         "--save", default=True, type=bool, required=False, help="Whether to save the converted model or not."
     )
     parser.add_argument(
         "--checkpoint_path",
         default=f"""{MODEL}/checkpoint_500000""",
         type=str,
         required=False,
         help="Path to the original jax model checkpoint.",
     )
     _lowerCamelCase    :       Union[str, Any]        =							parser.parse_args()

     main(args)








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()						)





