





from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('3.8'):
       import importlib_metadata
else:
       import importlib.metadata as importlib_metadata


_A					:    int   =  ""


if version.parse(importlib_metadata.version('jiwer')) < version.parse('2.3.0'):


       class    __SCREAMING_SNAKE_CASE			( tr.AbstractTransform					):







              def __init__(      self     :	Optional[Any]		,					A     :	str = " "			)   ->Tuple:
                 lowerCamelCase__   :						Tuple					   =							sentence_delimiter







              def 						__lowerCamelCase    (      self     :	Union[str, Any]		,					A     :	str			)   ->Optional[int]:
                 return list(UpperCamelCase__			)







              def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	List[str]			)   ->str:
                 lowerCamelCase__   :						str					   =							[]
                 for sent_idx, sentence in enumerate(UpperCamelCase__			):
                    chars.extend(self.process_string(UpperCamelCase__			)			)
                    if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(UpperCamelCase__			) - 1:
                       chars.append(self.sentence_delimiter			)
                 return chars


       _A					:    Any   =  tr.Compose(
           [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
       )
else:
       _A					:    str   =  tr.Compose(
           [
               tr.RemoveMultipleSpaces(),
               tr.Strip(),
               tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
               tr.ReduceToListOfListOfChars(),
           ]
       )


_A					:    str   =  "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

_A					:    List[Any]   =  "\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

_A					:    Tuple   =  "\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,_KWARGS_DESCRIPTION					)
class    __SCREAMING_SNAKE_CASE			( datasets.Metric					):







       def 						__lowerCamelCase    (      self     :	Optional[Any]			)   ->str:
          return datasets.MetricInfo(
              description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string'''		,					id='''sequence'''			),
                      '''references''': datasets.Value('''string'''		,					id='''sequence'''			),
                  }			)		,					codebase_urls=['''https://github.com/jitsi/jiwer/''']		,					reference_urls=[
                  '''https://en.wikipedia.org/wiki/Word_error_rate''',
                  '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
              ]		,					)







       def 						__lowerCamelCase    (      self     :	List[str]		,					A     :	Optional[Any]		,					A     :	List[str]		,					A     :	Union[str, Any]=False			)   ->Union[str, Any]:







          if concatenate_texts:
             return jiwer.compute_measures(
                 UpperCamelCase__		,					UpperCamelCase__		,					truth_transform=UpperCamelCase__		,					hypothesis_transform=UpperCamelCase__		,					)["wer"]

          lowerCamelCase__   :						List[Any]					   =							0
          lowerCamelCase__   :						str					   =							0
          for prediction, reference in zip(UpperCamelCase__		,					UpperCamelCase__			):
             lowerCamelCase__   :						Dict					   =							jiwer.compute_measures(
                 UpperCamelCase__		,					UpperCamelCase__		,					truth_transform=UpperCamelCase__		,					hypothesis_transform=UpperCamelCase__		,					)
             incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
             total += measures["substitutions"] + measures["deletions"] + measures["hits"]

          return incorrect / total








'''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()

