


"""simple docstring"""





import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


snake_case__						:   List[str]			 = logging.get_logger(__name__)



class 						snake_case_(				a__	):
     __UpperCamelCase     =    ['''input_features''', '''is_longer''']






     def __init__(		self     :    int    , UpperCamelCase_     :    Any=6_4    , UpperCamelCase_     :    Dict=4_8_0_0_0    , UpperCamelCase_     :    Dict=4_8_0    , UpperCamelCase_     :    Optional[int]=1_0    , UpperCamelCase_     :    str=1_0_2_4    , UpperCamelCase_     :    Any=0.0    , UpperCamelCase_     :    Union[str, Any]=False    , UpperCamelCase_     :    float = 0    , UpperCamelCase_     :    float = 1_4_0_0_0    , UpperCamelCase_     :    int = None    , UpperCamelCase_     :    str = "fusion"    , UpperCamelCase_     :    str = "repeatpad"    , **UpperCamelCase_     :    List[str]    , ):
           super().__init__(
               feature_size=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , padding_value=UpperCamelCase_    , return_attention_mask=UpperCamelCase_    , **UpperCamelCase_    , )
           lowerCAmelCase    :		str				=      top_db
           lowerCAmelCase    :		str				=      truncation
           lowerCAmelCase    :		Optional[Any]				=      padding
           lowerCAmelCase    :		List[Any]				=      fft_window_size
           lowerCAmelCase    :		Any				=      (fft_window_size >> 1) + 1
           lowerCAmelCase    :		Union[str, Any]				=      hop_length
           lowerCAmelCase    :		int				=      max_length_s
           lowerCAmelCase    :		str				=      max_length_s * sampling_rate
           lowerCAmelCase    :		Any				=      sampling_rate
           lowerCAmelCase    :		str				=      frequency_min
           lowerCAmelCase    :		List[str]				=      frequency_max
           lowerCAmelCase    :		Optional[int]				=      mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins    , num_mel_filters=UpperCamelCase_    , min_frequency=UpperCamelCase_    , max_frequency=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , norm=UpperCamelCase_    , mel_scale='''htk'''    , )
           lowerCAmelCase    :		Union[str, Any]				=      mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins    , num_mel_filters=UpperCamelCase_    , min_frequency=UpperCamelCase_    , max_frequency=UpperCamelCase_    , sampling_rate=UpperCamelCase_    , norm='''slaney'''    , mel_scale='''slaney'''    , )






     def    lowerCamelCase__   (		self     :    Optional[Any] ):
           lowerCAmelCase    :		Optional[int]				=      copy.deepcopy(self.__dict__ )
           lowerCAmelCase    :		Optional[int]				=      self.__class__.__name__
           if "mel_filters" in output:
                 del output["mel_filters"]
           if "mel_filters_slaney" in output:
                 del output["mel_filters_slaney"]
           return output






     def    lowerCamelCase__   (		self     :    Any    , UpperCamelCase_     :    np.array    , UpperCamelCase_     :    Optional[np.array] = None ):
           lowerCAmelCase    :		List[str]				=      spectrogram(
               UpperCamelCase_    , window_function(self.fft_window_size    , '''hann''' )    , frame_length=self.fft_window_size    , hop_length=self.hop_length    , power=2.0    , mel_filters=UpperCamelCase_    , log_mel='''dB'''    , )
           return log_mel_spectrogram.T






     def    lowerCamelCase__   (		self     :    Union[str, Any]    , UpperCamelCase_     :    Optional[int]    , UpperCamelCase_     :    int    , UpperCamelCase_     :    Any ):
           lowerCAmelCase    :		List[str]				=      np.array_split(list(range(0    , total_frames - chunk_frames + 1 ) )    , 3 )
           if len(ranges[1] ) == 0:
                 # if the audio is too short, we just use the first chunk
                 lowerCAmelCase    :		Union[str, Any]				=      [0]
           if len(ranges[2] ) == 0:
                 # if the audio is too short, we just use the first chunk
                 lowerCAmelCase    :		List[Any]				=      [0]
           # randomly choose index for each part
           lowerCAmelCase    :		Dict				=      np.random.choice(ranges[0] )
           lowerCAmelCase    :		str				=      np.random.choice(ranges[1] )
           lowerCAmelCase    :		List[Any]				=      np.random.choice(ranges[2] )

           lowerCAmelCase    :		Any				=      mel[idx_front : idx_front + chunk_frames, :]
           lowerCAmelCase    :		int				=      mel[idx_middle : idx_middle + chunk_frames, :]
           lowerCAmelCase    :		Optional[int]				=      mel[idx_back : idx_back + chunk_frames, :]

           lowerCAmelCase    :		List[Any]				=      torch.tensor(mel[None, None, :] )
           lowerCAmelCase    :		Optional[int]				=      torch.nn.functional.interpolate(
               UpperCamelCase_    , size=[chunk_frames, 6_4]    , mode='''bilinear'''    , align_corners=UpperCamelCase_ )
           lowerCAmelCase    :		Optional[Any]				=      mel_shrink[0][0].numpy()
           lowerCAmelCase    :		List[str]				=      np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    , axis=0 )
           return mel_fusion






     def    lowerCamelCase__   (		self     :    List[str]    , UpperCamelCase_     :    np.array    , UpperCamelCase_     :    Optional[Any]    , UpperCamelCase_     :    List[Any]    , UpperCamelCase_     :    List[Any] ):
           if waveform.shape[0] > max_length:
                 if truncation == "rand_trunc":
                       lowerCAmelCase    :		List[str]				=      True
                       # random crop to max_length (for compatibility) -> this should be handled by self.pad
                       lowerCAmelCase    :		Optional[int]				=      len(UpperCamelCase_ ) - max_length
                       lowerCAmelCase    :		List[str]				=      np.random.randint(0    , overflow + 1 )
                       lowerCAmelCase    :		List[Any]				=      waveform[idx : idx + max_length]
                       lowerCAmelCase    :		str				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters_slaney )[None, :]
                 elif truncation == "fusion":
                       lowerCAmelCase    :		List[str]				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters )
                       lowerCAmelCase    :		Union[str, Any]				=      max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                       lowerCAmelCase    :		Tuple				=      mel.shape[0]
                       if chunk_frames == total_frames:
                             # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                             # In this case, we just use the whole audio.
                             lowerCAmelCase    :		Union[str, Any]				=      np.stack([mel, mel, mel, mel]    , axis=0 )
                             lowerCAmelCase    :		Union[str, Any]				=      False
                       else:
                             lowerCAmelCase    :		Union[str, Any]				=      self._random_mel_fusion(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_ )
                             lowerCAmelCase    :		Any				=      True
                 else:
                       raise NotImplementedError(F'''data_truncating {truncation} not implemented''' )

           else:
                 lowerCAmelCase    :		Any				=      False
                 # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                 if waveform.shape[0] < max_length:
                       if padding == "repeat":
                             lowerCAmelCase    :		Optional[Any]				=      int(max_length / len(UpperCamelCase_ ) )
                             lowerCAmelCase    :		Optional[int]				=      np.stack(np.tile(UpperCamelCase_    , n_repeat + 1 ) )[:max_length]
                       if padding == "repeatpad":
                             lowerCAmelCase    :		Any				=      int(max_length / len(UpperCamelCase_ ) )
                             lowerCAmelCase    :		Tuple				=      np.stack(np.tile(UpperCamelCase_    , UpperCamelCase_ ) )
                       lowerCAmelCase    :		Any				=      np.pad(UpperCamelCase_    , (0, max_length - waveform.shape[0])    , mode='''constant'''    , constant_values=0 )

                 if truncation == "fusion":
                       lowerCAmelCase    :		Optional[Any]				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters )
                       lowerCAmelCase    :		List[str]				=      np.stack([input_mel, input_mel, input_mel, input_mel]    , axis=0 )
                 else:
                       lowerCAmelCase    :		Union[str, Any]				=      self._np_extract_fbank_features(UpperCamelCase_    , self.mel_filters_slaney )[None, :]

           return input_mel, longer






     def __call__(		self     :    Optional[int]    , UpperCamelCase_     :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    , UpperCamelCase_     :    str = None    , UpperCamelCase_     :    Optional[str] = None    , UpperCamelCase_     :    Optional[int] = None    , UpperCamelCase_     :    Optional[int] = None    , UpperCamelCase_     :    Optional[Union[str, TensorType]] = None    , **UpperCamelCase_     :    Optional[Any]    , ):
           lowerCAmelCase    :		Optional[int]				=      truncation if truncation is not None else self.truncation
           lowerCAmelCase    :		str				=      padding if padding else self.padding

           if sampling_rate is not None:
                 if sampling_rate != self.sampling_rate:
                       raise ValueError(
                           F'''The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'''
                           F''' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'''
                           F''' was sampled with {self.sampling_rate} and not {sampling_rate}.''' )
           else:
                 logger.warning(
                     '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                     '''Failing to do so can result in silent errors that might be hard to debug.''' )

           lowerCAmelCase    :		Tuple				=      isinstance(UpperCamelCase_    , np.ndarray ) and len(raw_speech.shape ) > 1
           if is_batched_numpy and len(raw_speech.shape ) > 2:
                 raise ValueError(F'''Only mono-channel audio is supported for input to {self}''' )
           lowerCAmelCase    :		Any				=      is_batched_numpy or (
               isinstance(UpperCamelCase_    , (list, tuple) ) and (isinstance(raw_speech[0]    , (np.ndarray, tuple, list) ))
           )

           if is_batched:
                 lowerCAmelCase    :		Tuple				=      [np.asarray(UpperCamelCase_    , dtype=np.floataa ) for speech in raw_speech]
           elif not is_batched and not isinstance(UpperCamelCase_    , np.ndarray ):
                 lowerCAmelCase    :		List[str]				=      np.asarray(UpperCamelCase_    , dtype=np.floataa )
           elif isinstance(UpperCamelCase_    , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
                 lowerCAmelCase    :		str				=      raw_speech.astype(np.floataa )

           # always return batch
           if not is_batched:
                 lowerCAmelCase    :		Tuple				=      [np.asarray(UpperCamelCase_ )]

           # convert to mel spectrogram, truncate and pad if needed.
           lowerCAmelCase    :		Union[str, Any]				=      [
               self._get_input_mel(UpperCamelCase_    , max_length if max_length else self.nb_max_samples    , UpperCamelCase_    , UpperCamelCase_ )
               for waveform in raw_speech
           ]

           lowerCAmelCase    :		str				=      []
           lowerCAmelCase    :		List[Any]				=      []
           for mel, longer in padded_inputs:
                 input_mel.append(UpperCamelCase_ )
                 is_longer.append(UpperCamelCase_ )

           if truncation == "fusion" and sum(UpperCamelCase_ ) == 0:
                 # if no audio is longer than 10s, then randomly select one audio to be longer
                 lowerCAmelCase    :		List[Any]				=      np.random.randint(0    , len(UpperCamelCase_ ) )
                 lowerCAmelCase    :		Optional[Any]				=      True

           if isinstance(input_mel[0]    , UpperCamelCase_ ):
                 lowerCAmelCase    :		Dict				=      [np.asarray(UpperCamelCase_    , dtype=np.floataa ) for feature in input_mel]

           # is_longer is a list of bool
           lowerCAmelCase    :		Any				=      [[longer] for longer in is_longer]

           lowerCAmelCase    :		str				=      {'''input_features''': input_mel, '''is_longer''': is_longer}
           lowerCAmelCase    :		int				=      BatchFeature(UpperCamelCase_ )

           if return_tensors is not None:
                 lowerCAmelCase    :		Dict				=      input_features.convert_to_tensors(UpperCamelCase_ )

           return input_features







'''simple docstring'''


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


_lowerCamelCase	:       Optional[int]      	=	logging.getLogger(__name__)







def 				__lowerCamelCase				(   A__=2    ,						A__=3    ,						A__=16    ,						A__ = 10    ,						A__ = 2    )       ->	int:






   """simple docstring"""

   def get_dataset(A__    ):
      UpperCamelCase					=  torch.randn(batch_size * n_batches    ,						1    )
      return TensorDataset(A__    ,						a * x + b + 0.1 * torch.randn(batch_size * n_batches    ,						1    )    )

   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  get_dataset(A__    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   UpperCamelCase					=  DataLoader(A__    ,						shuffle=A__    ,						batch_size=A__    ,						num_workers=4    )
   return (train_dataloader, valid_dataloader)







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    ,						A__    ,						A__=None    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  []
   for epoch in range(A__    ):
      # Train quickly
      model.train()
      for batch in dataloader:
         UpperCamelCase				,      UpperCamelCase					=  batch
         UpperCamelCase					=  model(A__    )
         UpperCamelCase					=  torch.nn.functional.mse_loss(A__    ,						A__    )
         accelerator.backward(A__    )
         optimizer.step()
         optimizer.zero_grad()
      rands.append(random.random()    )  # Introduce some randomness
      if scheduler is not None:
         scheduler.step()
   return rands

class  SCREAMING_SNAKE_CASE (  nn.Module		):






    """simple docstring"""





    def __init__(  self      :					Tuple ):


       """simple docstring"""







       super().__init__()
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )
       UpperCamelCase					=  nn.Parameter(torch.randn(1 ) )






    def  A       (  self      :					str  ,						UpperCamelCase__      :					Dict ):


       """simple docstring"""







       return x * self.a + self.b

class  SCREAMING_SNAKE_CASE (  unittest.TestCase		):






    """simple docstring"""





    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(total_limit=1  ,						project_dir=UpperCamelCase__  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()

          # Save second state
          accelerator.save_state()
          self.assertEqual(len(os.listdir(accelerator.project_dir ) )  ,						1 )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          # Train baseline
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'initial' )
          accelerator.save_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          UpperCamelCase					=  train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  Accelerator()
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  train(2  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save everything
          UpperCamelCase					=  os.path.join(UpperCamelCase__  ,						'checkpoint' )
          accelerator.save_state(UpperCamelCase__ )

          # Load everything back in and make sure all states work
          accelerator.load_state(UpperCamelCase__ )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Union[str, Any] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )

          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          UpperCamelCase					=  train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()

          # Train partially
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(iteration=1  ,						automatic_checkpoint_naming=UpperCamelCase__ )
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )

          UpperCamelCase					=  train(2  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save everything
          accelerator.save_state()

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_1' ) )
          test_rands += train(1  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          ((UpperCamelCase)				,      (UpperCamelCase))					=  model.a.item(), model.b.item()
          UpperCamelCase					=  optimizer.state_dict()
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertEqual(UpperCamelCase__  ,						UpperCamelCase__ )






    def  A       (  self      :					Optional[int] ):


       """simple docstring"""







       UpperCamelCase					=  torch.tensor([1, 2, 3] )
       UpperCamelCase					=  torch.tensor([2, 3, 4] )
       UpperCamelCase					=  DummyModel()
       UpperCamelCase					=  torch.optim.Adam(net.parameters() )
       UpperCamelCase					=  Accelerator()
       with self.assertRaises(UpperCamelCase__ ) as ve:
          accelerator.register_for_checkpointing(UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
       UpperCamelCase					=  str(ve.exception )
       self.assertTrue('Item at index 0' in message )
       self.assertTrue('Item at index 1' in message )
       self.assertFalse('Item at index 2' in message )
       self.assertFalse('Item at index 3' in message )






    def  A       (  self      :					Dict ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  torch.optim.Adam(params=model.parameters()  ,						lr=1E-3 )
          UpperCamelCase					=  torch.optim.lr_scheduler.StepLR(UpperCamelCase__  ,						step_size=1  ,						gamma=0.9_9 )
          UpperCamelCase				,      UpperCamelCase					=  dummy_dataloaders()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__ )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  accelerator.prepare(
              UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          # Save initial
          accelerator.save_state()
          UpperCamelCase					=  scheduler.state_dict()
          train(3  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__  ,						UpperCamelCase__ )
          self.assertNotEqual(UpperCamelCase__  ,						scheduler.state_dict() )

          # Load everything back in and make sure all states work
          accelerator.load_state(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) )
          self.assertEqual(UpperCamelCase__  ,						scheduler.state_dict() )






    def  A       (  self      :					List[str] ):


       """simple docstring"""







       with tempfile.TemporaryDirectory() as tmpdir:
          set_seed(4_2 )
          UpperCamelCase					=  DummyModel()
          UpperCamelCase					=  ProjectConfiguration(automatic_checkpoint_naming=UpperCamelCase__  ,						total_limit=2 )
          # Train baseline
          UpperCamelCase					=  Accelerator(project_dir=UpperCamelCase__  ,						project_config=UpperCamelCase__ )
          UpperCamelCase					=  accelerator.prepare(UpperCamelCase__ )
          # Save 3 states:
          for _ in range(1_1 ):
             accelerator.save_state()
          self.assertTrue(not os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_0' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_9' ) ) )
          self.assertTrue(os.path.exists(os.path.join(UpperCamelCase__  ,						'checkpoints'  ,						'checkpoint_10' ) ) )






    @require_cuda
    def  A       (  self      :					Dict ):


       """simple docstring"""







       UpperCamelCase					=  ['torchrun', f"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
       execute_subprocess_async(UpperCamelCase__  ,						env=os.environ.copy() )


if __name__ == "__main__":
   _lowerCamelCase	:       Optional[int]      	=	"/tmp/accelerate/state_checkpointing"
   _lowerCamelCase	:       Union[str, Any]      	=	DummyModel()
   _lowerCamelCase	:       Optional[Any]      	=	torch.optim.Adam(params=model.parameters(), lr=1e-3)
   _lowerCamelCase	:       List[Any]      	=	torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	dummy_dataloaders()
   _lowerCamelCase	:       List[Any]      	=	ProjectConfiguration(automatic_checkpoint_naming=True)
   # Train baseline
   _lowerCamelCase	:       Any      	=	Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="no")
   if accelerator.process_index == 0:
      if os.path.exists(savedir):
         shutil.rmtree(savedir)
      os.makedirs(savedir)
   _lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	,_lowerCamelCase	:       Union[str, Any]      	=	accelerator.prepare(
       model, optimizer, train_dataloader, valid_dataloader, scheduler
   )
   _lowerCamelCase	,_lowerCamelCase	:       Tuple      	=	accelerator.prepare(model, optimizer)
   train(3, model, train_dataloader, optimizer, accelerator, scheduler)
   # Check that the intial optimizer is loaded on the GPU
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Any      	=	group["params"][0].device
      break
   assert param_device.type == accelerator.device.type
   _lowerCamelCase	:       Tuple      	=	model.cpu()
   accelerator.wait_for_everyone()
   accelerator.save_state()
   accelerator.wait_for_everyone()

   # Check CPU state
   accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="cpu")
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Optional[Any]      	=	group["params"][0].device
      break
   assert (
       param_device.type == torch.device("cpu").type
   ), f"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

   # Check device state
   model.to(accelerator.device)
   accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="on_device")
   for group in optimizer.param_groups:
      _lowerCamelCase	:       Dict      	=	group["params"][0].device
      break
   assert (
       param_device.type == accelerator.device.type
   ), f"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

   # Check error
   with pytest.raises(TypeError, match="Unsupported optimizer map location passed"):
      accelerator.load_state(os.path.join(savedir, "checkpoints", "checkpoint_0"), map_location="invalid")
   accelerator.wait_for_everyone()
   if accelerator.process_index == 0:
      shutil.rmtree(savedir)
   accelerator.wait_for_everyone()

