'''simple docstring'''




import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


__snake_case   :      List[Any]						    =  namedtuple('ExampleNamedTuple', 'a b c')




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def 					__A       (      self )		->     Optional[Any]:
           A_     =   torch.randn(5    ,     2 )
           A_     =   torch.device('''cuda''' ) if torch.cuda.is_available() else torch.device('''cpu''' )

           A_     =   send_to_device(A__    ,     A__ )
           self.assertTrue(torch.equal(resulta.cpu()    ,     A__ ) )

           A_     =   send_to_device((tensor, [tensor, tensor], 1)    ,     A__ )
           self.assertIsInstance(A__    ,     A__ )
           self.assertTrue(torch.equal(resulta[0].cpu()    ,     A__ ) )
           self.assertIsInstance(resulta[1]    ,     A__ )
           self.assertTrue(torch.equal(resulta[1][0].cpu()    ,     A__ ) )
           self.assertTrue(torch.equal(resulta[1][1].cpu()    ,     A__ ) )
           self.assertEqual(resulta[2]    ,     1 )

           A_     =   send_to_device({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1}    ,     A__ )
           self.assertIsInstance(A__    ,     A__ )
           self.assertTrue(torch.equal(resulta['''a'''].cpu()    ,     A__ ) )
           self.assertIsInstance(resulta['''b''']    ,     A__ )
           self.assertTrue(torch.equal(resulta['''b'''][0].cpu()    ,     A__ ) )
           self.assertTrue(torch.equal(resulta['''b'''][1].cpu()    ,     A__ ) )
           self.assertEqual(resulta['''c''']    ,     1 )

           A_     =   send_to_device(ExampleNamedTuple(a=A__    ,     b=[tensor, tensor]    ,     c=1 )    ,     A__ )
           self.assertIsInstance(A__    ,     A__ )
           self.assertTrue(torch.equal(resulta.a.cpu()    ,     A__ ) )
           self.assertIsInstance(resulta.b    ,     A__ )
           self.assertTrue(torch.equal(resulta.b[0].cpu()    ,     A__ ) )
           self.assertTrue(torch.equal(resulta.b[1].cpu()    ,     A__ ) )
           self.assertEqual(resulta.c    ,     1 )

           A_     =   send_to_device(UserDict({'''a''': tensor, '''b''': [tensor, tensor], '''c''': 1} )    ,     A__ )
           self.assertIsInstance(A__    ,     A__ )
           self.assertTrue(torch.equal(resulta['''a'''].cpu()    ,     A__ ) )
           self.assertIsInstance(resulta['''b''']    ,     A__ )
           self.assertTrue(torch.equal(resulta['''b'''][0].cpu()    ,     A__ ) )
           self.assertTrue(torch.equal(resulta['''b'''][1].cpu()    ,     A__ ) )
           self.assertEqual(resulta['''c''']    ,     1 )

       def 					__A       (      self )		->     Optional[int]:
           with self.assertRaises(A__ ) as cm:
               A_     =   recursively_apply(torch.tensor    ,     (torch.tensor(1 ), 1)    ,     error_on_other_type=A__ )
           self.assertEqual(
               str(cm.exception )    ,     '''Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'''    ,     )

       def 					__A       (      self )		->     List[str]:
           A_     =   torch.tensor([1, 2, 3, 4, 5] )
           self.assertEqual(listify(A__ )    ,     [1, 2, 3, 4, 5] )

           A_     =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )
           self.assertEqual(listify(A__ )    ,     [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] )

           A_     =   torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )
           self.assertEqual(
               listify(A__ )    ,     [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]] )

       def 					__A       (      self )		->     Optional[Any]:
           with patch_environment(aa=1    ,     BB=2 ):
               self.assertEqual(os.environ.get('''AA''' )    ,     '''1''' )
               self.assertEqual(os.environ.get('''BB''' )    ,     '''2''' )

           self.assertNotIn('''AA'''    ,     os.environ )
           self.assertNotIn('''BB'''    ,     os.environ )

       def 					__A       (      self )		->     int:
           A_     =   RegressionModel()
           A_     =   model.forward
           A_     =   convert_outputs_to_fpaa(model.forward )
           A_     =   extract_model_from_parallel(A__    ,     keep_fpaa_wrapper=A__ )
           A_     =   pickle.dumps(A__ )

       @require_cuda
       def 					__A       (      self )		->     int:
           A_     =   RegressionModel()
           A_     =   model.forward
           A_     =   torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
           A_     =   convert_outputs_to_fpaa(model.forward )
           A_     =   extract_model_from_parallel(A__    ,     keep_fpaa_wrapper=A__ )
           A_     =   pickle.dumps(A__ )

       @require_cuda
       @require_torch_min_version(version='''2.0''' )
       def 					__A       (      self )		->     Dict:
           A_     =   RegressionModel()
           A_     =   model.forward
           A_     =   torch.cuda.amp.autocast(dtype=torch.floataa )(model.forward )
           A_     =   convert_outputs_to_fpaa(model.forward )
           A_     =   torch.compile(model.forward    ,     backend='''inductor''' )
           A_     =   torch.randn(4    ,     10 ).cuda()
           A_     =   model(A__ )

       def 					__A       (      self )		->     Any:
           A_     =   RegressionModel()
           # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
           A_     =   torch.nn.parallel.DataParallel(A__ )
           A_     =   extract_model_from_parallel(A__ )

           self.assertEqual(A__    ,     A__ )

       @require_torch_min_version(version='''2.0''' )
       def 					__A       (      self )		->     Tuple:
           A_     =   RegressionModel()
           A_     =   torch.compile(A__ )

           # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
           A_     =   torch.nn.parallel.DataParallel(A__ )
           A_     =   torch.compile(A__ )
           A_     =   extract_model_from_parallel(A__ )

           self.assertEqual(compiled_model._orig_mod    ,     compiled_model_unwrapped._orig_mod )

       def 					__A       (      self )		->     str:
           self.assertEqual(find_device([1, '''a''', torch.tensor([1, 2, 3] )] )    ,     torch.device('''cpu''' ) )
           self.assertEqual(find_device({'''a''': 1, '''b''': torch.tensor([1, 2, 3] )} )    ,     torch.device('''cpu''' ) )
           self.assertIsNone(find_device([1, '''a'''] ) )












import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				List[dict]   , lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int   , lowerCamelCase:				GradScaler = None   , )    ->	Any:


      '''simple docstring'''




      for i, state in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Model weights saved in {output_model_file}"""  )
      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = opt.state_dict()
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Optimizer state saved in {output_optimizer_file}"""  )
      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = scheduler.state_dict()
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Scheduler state saved in {output_scheduler_file}"""  )
      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = scaler.state_dict()
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            torch.save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""  )
      # Random number generator states
      lowerCamelCase__					     = {}
      lowerCamelCase__					     = f"""{RNG_STATE_NAME}_{process_index}.pkl"""
      lowerCamelCase__					     = random.getstate()
      lowerCamelCase__					     = np.random.get_state()
      lowerCamelCase__					     = torch.get_rng_state()
      if is_xpu_available():
            lowerCamelCase__					     = torch.xpu.get_rng_state_all()
      else:
            lowerCamelCase__					     = torch.cuda.get_rng_state_all()
      if is_tpu_available():
            lowerCamelCase__					     = xm.get_rng_state()
      lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
      torch.save(lowerCamelCase   , lowerCamelCase  )
      logger.info(f"""Random states saved in {output_states_file}"""  )
      return output_dir

def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Dict   , lowerCamelCase:				Union[str, Any]   , lowerCamelCase:				Any   , lowerCamelCase:				Any   , lowerCamelCase:				Any=None   , lowerCamelCase:				Dict=None   , **lowerCamelCase:				Optional[Any]   , )    ->	Any:


      '''simple docstring'''




      if map_location not in [None, "cpu", "on_device"]:
            raise TypeError(
                'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'  )
      if map_location is None:
            lowerCamelCase__					     = 'cpu'
      elif map_location == "on_device":
            lowerCamelCase__					     = PartialState().device
      # Model states
      for i, model in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            models[i].load_state_dict(torch.load(lowerCamelCase   , map_location=lowerCamelCase  )   , **lowerCamelCase  )
      logger.info('All model weights loaded successfully'  )

      # Optimizer states
      for i, opt in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            lowerCamelCase__					     = torch.load(lowerCamelCase   , map_location=lowerCamelCase  )
            optimizers[i].load_state_dict(lowerCamelCase  )
      logger.info('All optimizer states loaded successfully'  )

      # Scheduler states
      for i, scheduler in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scheduler.load_state_dict(torch.load(lowerCamelCase  )  )
      logger.info('All scheduler states loaded successfully'  )

      # GradScaler state
      if scaler is not None:
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            scaler.load_state_dict(torch.load(lowerCamelCase  )  )
            logger.info('GradScaler state loaded successfully'  )

      # Random states
      try:
            lowerCamelCase__					     = torch.load(os.path.join(lowerCamelCase   , f"""{RNG_STATE_NAME}_{process_index}.pkl"""  )  )
            random.setstate(states['random_state']  )
            np.random.set_state(states['numpy_random_seed']  )
            torch.set_rng_state(states['torch_manual_seed']  )
            if is_xpu_available():
                  torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']  )
            else:
                  torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']  )
            if is_tpu_available():
                  xm.set_rng_state(states['xm_seed']  )
            logger.info('All random states loaded successfully'  )
      except Exception:
            logger.info('Could not load random states'  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Dict   , lowerCamelCase:				Any   , lowerCamelCase:				int = 0  )    ->	Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__					     = Path(lowerCamelCase  ) / f"""custom_checkpoint_{index}.pkl"""
      logger.info(f"""Saving the state of {get_pretty_name(lowerCamelCase  )} to {save_location}"""  )
      torch.save(obj.state_dict()   , lowerCamelCase  )


def 	lowerCAmelCase_	(     lowerCamelCase:				int   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				int = 0  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = f"""{path}/custom_checkpoint_{index}.pkl"""
      logger.info(f"""Loading the state of {get_pretty_name(lowerCamelCase  )} from {load_location}"""  )
      obj.load_state_dict(torch.load(lowerCamelCase   , map_location='cpu'  )  )