
'''simple docstring'''
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=0.999							, _SCREAMING_SNAKE_CASE="cosine"							, ):
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE				):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE				):
																					return math.exp(t * -12.0				)

							else:
														raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

							_snake_case       =  []
							for i in range(lowerCamelCase_				):
														_snake_case       =  i / num_diffusion_timesteps
														_snake_case       =  (i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(lowerCamelCase_				) / alpha_bar_fn(lowerCamelCase_				)							, lowerCamelCase_				)				)
							return torch.tensor(lowerCamelCase_							, dtype=torch.floataa				)


class       _lowerCAmelCase   (   _a					,							_a				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						[e.name for e in KarrasDiffusionSchedulers]
							lowerCAmelCase_ 						=						2

							@register_to_config
							def __init__(self  ,  UpperCAmelCase = 1000  ,  UpperCAmelCase = 0.0_0085  ,  UpperCAmelCase = 0.012  ,  UpperCAmelCase = "linear"  ,  UpperCAmelCase = None  ,  UpperCAmelCase = "epsilon"  ,  UpperCAmelCase = "linspace"  ,  UpperCAmelCase = 0  ,  )					->       Dict:

														if trained_betas is not None:
																					_snake_case       =  torch.tensor(UpperCAmelCase  ,  dtype=torch.floataa  )
														elif beta_schedule == "linear":
																					_snake_case       =  torch.linspace(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  dtype=torch.floataa  )
														elif beta_schedule == "scaled_linear":
																					# this schedule is very specific to the latent diffusion model.
																					_snake_case       =  (
																					    torch.linspace(beta_start**0.5  ,  beta_end**0.5  ,  UpperCAmelCase  ,  dtype=torch.floataa  ) ** 2
																					)
														elif beta_schedule == "squaredcos_cap_v2":
																					# Glide cosine schedule
																					_snake_case       =  betas_for_alpha_bar(UpperCAmelCase  )
														else:
																					raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""  )

														_snake_case       =  1.0 - self.betas
														_snake_case       =  torch.cumprod(self.alphas  ,  dim=0  )

														#  set all values
														self.set_timesteps(UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=None  )					->       Dict:

														if schedule_timesteps is None:
																					_snake_case       =  self.timesteps

														_snake_case       =  (schedule_timesteps == timestep).nonzero()

														# The sigma index that is taken for the **very** first `step`
														# is always the second index (or the last index if there is only 1)
														# This way we can ensure we don't accidentally skip a sigma in
														# case we start in the middle of the denoising schedule (e.g. for image-to-image)
														if len(self._index_counter  ) == 0:
																					_snake_case       =  1 if len(UpperCAmelCase  ) > 1 else 0
														else:
																					_snake_case       =  timestep.cpu().item() if torch.is_tensor(UpperCAmelCase  ) else timestep
																					_snake_case       =  self._index_counter[timestep_int]

														return indices[pos].item()

							@property
							def 	lowercase							(self  )					->       List[str]:

														if self.config.timestep_spacing in ["linspace", "trailing"]:
																					return self.sigmas.max()

														return (self.sigmas.max() ** 2 + 1) ** 0.5

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  )					->       torch.FloatTensor:
														_snake_case       =  self.index_for_timestep(UpperCAmelCase  )

														if self.state_in_first_order:
																					_snake_case       =  self.sigmas[step_index]
														else:
																					_snake_case       =  self.sigmas_interpol[step_index]

														_snake_case       =  sample / ((sigma**2 + 1) ** 0.5)
														return sample

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = None  ,  )					->       Tuple:
														_snake_case       =  num_inference_steps

														_snake_case       =  num_train_timesteps or self.config.num_train_timesteps

														# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
														if self.config.timestep_spacing == "linspace":
																					_snake_case       =  np.linspace(0  ,  num_train_timesteps - 1  ,  UpperCAmelCase  ,  dtype=UpperCAmelCase  )[::-1].copy()
														elif self.config.timestep_spacing == "leading":
																					_snake_case       =  num_train_timesteps // self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					_snake_case       =  (np.arange(0  ,  UpperCAmelCase  ) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase  )
																					timesteps += self.config.steps_offset
														elif self.config.timestep_spacing == "trailing":
																					_snake_case       =  num_train_timesteps / self.num_inference_steps
																					# creates integer timesteps by multiplying by ratio
																					# casting to int to avoid issues when num_inference_step is power of 3
																					_snake_case       =  (np.arange(UpperCAmelCase  ,  0  ,  -step_ratio  )).round().copy().astype(UpperCAmelCase  )
																					timesteps -= 1
														else:
																					raise ValueError(
																					    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'."""  )

														_snake_case       =  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
														_snake_case       =  torch.from_numpy(np.log(UpperCAmelCase  )  ).to(UpperCAmelCase  )

														_snake_case       =  np.interp(UpperCAmelCase  ,  np.arange(0  ,  len(UpperCAmelCase  )  )  ,  UpperCAmelCase  )
														_snake_case       =  np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
														_snake_case       =  torch.from_numpy(UpperCAmelCase  ).to(device=UpperCAmelCase  )

														# interpolate sigmas
														_snake_case       =  sigmas.log().lerp(sigmas.roll(1  ).log()  ,  0.5  ).exp()

														_snake_case       =  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2  ), sigmas[-1:]]  )
														_snake_case       =  torch.cat(
														    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2  ), sigmas_interpol[-1:]]  )

														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					# mps does not support float64
																					_snake_case       =  torch.from_numpy(UpperCAmelCase  ).to(UpperCAmelCase  ,  dtype=torch.floataa  )
														else:
																					_snake_case       =  torch.from_numpy(UpperCAmelCase  ).to(UpperCAmelCase  )

														# interpolate timesteps
														_snake_case       =  self.sigma_to_t(UpperCAmelCase  ).to(UpperCAmelCase  ,  dtype=timesteps.dtype  )
														_snake_case       =  torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])  ,  dim=-1  ).flatten()

														_snake_case       =  torch.cat([timesteps[:1], interleaved_timesteps]  )

														_snake_case       =  None

														# for exp beta schedules, such as the one for `pipeline_shap_e.py`
														# we need an index counter
														_snake_case       =  defaultdict(UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  )					->       int:
														_snake_case       =  sigma.log()

														# get distribution
														_snake_case       =  log_sigma - self.log_sigmas[:, None]

														# get sigmas range
														_snake_case       =  dists.ge(0  ).cumsum(dim=0  ).argmax(dim=0  ).clamp(max=self.log_sigmas.shape[0] - 2  )
														_snake_case       =  low_idx + 1

														_snake_case       =  self.log_sigmas[low_idx]
														_snake_case       =  self.log_sigmas[high_idx]

														# interpolate sigmas
														_snake_case       =  (low - log_sigma) / (low - high)
														_snake_case       =  w.clamp(0  ,  1  )

														# transform interpolation to time range
														_snake_case       =  (1 - w) * low_idx + w * high_idx
														_snake_case       =  t.view(sigma.shape  )
														return t

							@property
							def 	lowercase							(self  )					->       int:
														return self.sample is None

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = True  ,  )					->       Union[SchedulerOutput, Tuple]:
														_snake_case       =  self.index_for_timestep(UpperCAmelCase  )

														# advance index counter by 1
														_snake_case       =  timestep.cpu().item() if torch.is_tensor(UpperCAmelCase  ) else timestep
														self._index_counter[timestep_int] += 1

														if self.state_in_first_order:
																					_snake_case       =  self.sigmas[step_index]
																					_snake_case       =  self.sigmas_interpol[step_index + 1]
																					_snake_case       =  self.sigmas[step_index + 1]
														else:
																					# 2nd order / KDPM2's method
																					_snake_case       =  self.sigmas[step_index - 1]
																					_snake_case       =  self.sigmas_interpol[step_index]
																					_snake_case       =  self.sigmas[step_index]

														# currently only gamma=0 is supported. This usually works best anyways.
														# We can support gamma in the future but then need to scale the timestep before
														# passing it to the model which requires a change in API
														_snake_case       =  0
														_snake_case       =  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

														# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
														if self.config.prediction_type == "epsilon":
																					_snake_case       =  sigma_hat if self.state_in_first_order else sigma_interpol
																					_snake_case       =  sample - sigma_input * model_output
														elif self.config.prediction_type == "v_prediction":
																					_snake_case       =  sigma_hat if self.state_in_first_order else sigma_interpol
																					_snake_case       =  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
																					    sample / (sigma_input**2 + 1)
																					)
														elif self.config.prediction_type == "sample":
																					raise NotImplementedError("""prediction_type not implemented yet: sample"""  )
														else:
																					raise ValueError(
																					    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""  )

														if self.state_in_first_order:
																					# 2. Convert to an ODE derivative for 1st order
																					_snake_case       =  (sample - pred_original_sample) / sigma_hat
																					# 3. delta timestep
																					_snake_case       =  sigma_interpol - sigma_hat

																					# store for 2nd order step
																					_snake_case       =  sample
														else:
																					# DPM-Solver-2
																					# 2. Convert to an ODE derivative for 2nd order
																					_snake_case       =  (sample - pred_original_sample) / sigma_interpol

																					# 3. delta timestep
																					_snake_case       =  sigma_next - sigma_hat

																					_snake_case       =  self.sample
																					_snake_case       =  None

														_snake_case       =  sample + derivative * dt

														if not return_dict:
																					return (prev_sample,)

														return SchedulerOutput(prev_sample=UpperCAmelCase  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  )					->       torch.FloatTensor:
														_snake_case       =  self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype  )
														if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase  ):
																					# mps does not support float64
																					_snake_case       =  self.timesteps.to(original_samples.device  ,  dtype=torch.floataa  )
																					_snake_case       =  timesteps.to(original_samples.device  ,  dtype=torch.floataa  )
														else:
																					_snake_case       =  self.timesteps.to(original_samples.device  )
																					_snake_case       =  timesteps.to(original_samples.device  )

														_snake_case       =  [self.index_for_timestep(UpperCAmelCase  ,  UpperCAmelCase  ) for t in timesteps]

														_snake_case       =  sigmas[step_indices].flatten()
														while len(sigma.shape  ) < len(original_samples.shape  ):
																					_snake_case       =  sigma.unsqueeze(-1  )

														_snake_case       =  original_samples + noise * sigma
														return noisy_samples





							def __len__(self  )					->       Optional[int]:
														return self.config.num_train_timesteps

'''simple docstring'''
import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

__lowerCAmelCase												=	'bert-base-cased'
__lowerCAmelCase												=	'fp16'
__lowerCAmelCase												=	'bf16'
__lowerCAmelCase												=	[FPaa, BFaa]


@require_fsdp
@require_cuda
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[Any]:
														super().setUp()

														_snake_case       =  dict(
														    ACCELERATE_USE_FSDP="""true"""  ,  MASTER_ADDR="""localhost"""  ,  MASTER_PORT="""10999"""  ,  RANK="""0"""  ,  LOCAL_RANK="""0"""  ,  WORLD_SIZE="""1"""  ,  )

							def 	lowercase							(self  )					->       Optional[int]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

														for i, strategy in enumerate(UpperCAmelCase  ):
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  f"""{i + 1}"""
																					_snake_case       =  strategy
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.sharding_strategy  ,  ShardingStrategy(i + 1  )  )

							def 	lowercase							(self  )					->       List[str]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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

							def 	lowercase							(self  )					->       int:
														from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

														for i, state_dict_type in enumerate(UpperCAmelCase  ):
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  state_dict_type
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  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 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  AutoModel.from_pretrained(UpperCAmelCase  )
														for policy in FSDP_AUTO_WRAP_POLICY:
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  policy
																					if policy == "TRANSFORMER_BASED_WRAP":
																												_snake_case       =  """BertLayer"""
																					elif policy == "SIZE_BASED_WRAP":
																												_snake_case       =  """2000"""
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  FullyShardedDataParallelPlugin()
																												fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase  )
																												if policy == "NO_WRAP":
																																			self.assertIsNone(fsdp_plugin.auto_wrap_policy  )
																												else:
																																			self.assertIsNotNone(fsdp_plugin.auto_wrap_policy  )

														_snake_case       =  self.dist_env.copy()
														_snake_case       =  """TRANSFORMER_BASED_WRAP"""
														_snake_case       =  """T5Layer"""
														with mockenv_context(**UpperCAmelCase  ):
																					_snake_case       =  FullyShardedDataParallelPlugin()
																					with self.assertRaises(UpperCAmelCase  ) as cm:
																												fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase  )
																					self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception  )  )

														_snake_case       =  self.dist_env.copy()
														_snake_case       =  """SIZE_BASED_WRAP"""
														_snake_case       =  """0"""
														with mockenv_context(**UpperCAmelCase  ):
																					_snake_case       =  FullyShardedDataParallelPlugin()
																					fsdp_plugin.set_auto_wrap_policy(UpperCAmelCase  )
																					self.assertIsNone(fsdp_plugin.auto_wrap_policy  )

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





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

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


@require_fsdp
@require_multi_gpu
@slow
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       List[Any]:
														super().setUp()
														_snake_case       =  0.82
														_snake_case       =  [
														    """fsdp_shard_grad_op_transformer_based_wrap""",
														    """fsdp_full_shard_transformer_based_wrap""",
														]
														_snake_case       =  {
														    """multi_gpu_fp16""": 3200,
														    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2000,
														    """fsdp_full_shard_transformer_based_wrap_fp16""": 1900,
														    # Disabling below test as it overwhelms the RAM memory usage
														    # on CI self-hosted runner leading to tests getting killed.
														    # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
														}
														_snake_case       =  160
														_snake_case       =  160

														_snake_case       =  inspect.getfile(accelerate.test_utils  )
														_snake_case       =  os.path.sep.join(mod_file.split(os.path.sep  )[:-1] + ["""scripts""", """external_deps"""]  )

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

							def 	lowercase							(self  )					->       Union[str, Any]:
														_snake_case       =  os.path.join(self.test_scripts_folder  ,  """test_checkpointing.py"""  )
														_snake_case       =  [
														    """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(UpperCAmelCase  ):
																					_snake_case       =  cmd.copy()
																					cmd_config.append(f"""--fsdp_sharding_strategy={i+1}"""  )
																					if strategy != "FULL_SHARD":
																												continue
																					_snake_case       =  len(UpperCAmelCase  )
																					for state_dict_type in FSDP_STATE_DICT_TYPE:
																												_snake_case       =  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(UpperCAmelCase  ,  env=os.environ.copy()  )

																												_snake_case       =  cmd_config[:-1]
																												_snake_case       =  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(UpperCAmelCase  ,  env=os.environ.copy()  )





							def 	lowercase							(self  )					->       Optional[Any]:
														_snake_case       =  os.path.join(self.test_scripts_folder  ,  """test_peak_memory_usage.py"""  )
														_snake_case       =  [
														    """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():
																					_snake_case       =  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(UpperCAmelCase  ):
																																			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(UpperCAmelCase  ,  env=os.environ.copy()  )