




from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Union[str, Any]:



       """simple docstring"""

       for param, grad_param in zip(model_a.parameters() ,				model_b.parameters()						):
              if not param.requires_grad:
                     continue
              if not did_step:
                     # Grads should not be in sync
                     assert (
                         torch.allclose(param.grad ,				grad_param.grad						) is False
                     ), f"""Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})"""
              else:
                     # Grads should be in sync
                     assert (
                         torch.allclose(param.grad ,				grad_param.grad						) is True
                     ), f"""Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})"""







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_=True						)   ->       str:



       """simple docstring"""

       model.train()
       A__       =    model(lowercase_						)
       A__       =    F.mse_loss(lowercase_ ,				target.to(output.device						)						)
       if not do_backward:
              loss /= accelerator.gradient_accumulation_steps
              loss.backward()
       else:
              accelerator.backward(lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False						)   ->       int:



       """simple docstring"""

       set_seed(42						)
       A__       =    RegressionModel()
       A__       =    deepcopy(lowercase_						)
       A__       =    RegressionDataset(length=80						)
       A__       =    DataLoader(lowercase_ ,				batch_size=16						)
       model.to(accelerator.device						)
       if sched:
              A__       =    AdamW(params=model.parameters() ,				lr=1E-3						)
              A__       =    AdamW(params=ddp_model.parameters() ,				lr=1E-3						)
              A__       =    LambdaLR(lowercase_ ,				lr_lambda=lambda lowercase_						: epoch**0.65						)
              A__       =    LambdaLR(lowercase_ ,				lr_lambda=lambda lowercase_						: epoch**0.65						)
       # Make a copy of `model`
       if sched:
              A__ ,							A__ ,							A__ ,							A__       =    accelerator.prepare(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
       else:
              A__ ,							A__       =    accelerator.prepare(lowercase_ ,				lowercase_						)
       if sched:
              return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
       return model, ddp_model, dataloader







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__ ,							A__ ,							A__       =    get_training_setup(lowercase_						)
       # Use a single batch
       A__ ,							A__       =    next(iter(lowercase_						)						).values()
       for iteration in range(3						):
              # Gather the distributed inputs and targs for the base model
              A__ ,							A__       =    accelerator.gather((ddp_input, ddp_target)						)
              A__ ,							A__       =    input.to(accelerator.device						), target.to(accelerator.device						)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowercase_						):
                            step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              else:
                     # Sync grads
                     step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              for param, ddp_param in zip(model.parameters() ,				ddp_model.parameters()						):
                     if not param.requires_grad:
                            continue
                     assert torch.allclose(
                         param.grad ,				ddp_param.grad						), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__ ,							A__ ,							A__       =    get_training_setup(lowercase_						)
       # Use a single batch
       A__ ,							A__       =    next(iter(lowercase_						)						).values()
       for iteration in range(3						):
              # Gather the distributed inputs and targs for the base model
              A__ ,							A__       =    accelerator.gather((ddp_input, ddp_target)						)
              A__ ,							A__       =    input.to(accelerator.device						), target.to(accelerator.device						)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              # Do "gradient accumulation" (noop)
              if iteration % 2 == 0:
                     # Accumulate grads locally
                     with accelerator.no_sync(lowercase_						):
                            step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              else:
                     # Sync grads
                     step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters() ,				ddp_model.parameters()						):
                     if not param.requires_grad:
                            continue
                     if iteration % 2 == 0:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad ,				ddp_param.grad						) is False
                            ), f"""Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad ,				ddp_param.grad						) is True
                            ), f"""Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]







def SCREAMING_SNAKE_CASE				(       lowercase_=False ,				lowercase_=False						)   ->       Dict:



       """simple docstring"""

       A__       =    Accelerator(
           split_batches=lowercase_ ,				dispatch_batches=lowercase_ ,				gradient_accumulation_steps=2						)
       # Test that context manager behaves properly
       A__ ,							A__ ,							A__       =    get_training_setup(lowercase_						)
       for iteration, batch in enumerate(lowercase_						):
              A__ ,							A__       =    batch.values()
              # Gather the distributed inputs and targs for the base model
              A__ ,							A__       =    accelerator.gather((ddp_input, ddp_target)						)
              A__ ,							A__       =    input.to(accelerator.device						), target.to(accelerator.device						)
              # Perform our initial ground truth step in non "DDP"
              step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              # Do "gradient accumulation" (noop)
              with accelerator.accumulate(lowercase_						):
                     step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)

              # DDP model and model should only be in sync when not (iteration % 2 == 0)
              for param, ddp_param in zip(model.parameters() ,				ddp_model.parameters()						):
                     if not param.requires_grad:
                            continue
                     if ((iteration + 1) % 2 == 0) or (iteration == len(lowercase_						) - 1):
                            # Grads should be in sync
                            assert (
                                torch.allclose(param.grad ,				ddp_param.grad						) is True
                            ), f"""Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})"""
                     else:
                            # Grads should not be in sync
                            assert (
                                torch.allclose(param.grad ,				ddp_param.grad						) is False
                            ), f"""Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})"""

        # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE				(       lowercase_=False ,				lowercase_=False						)   ->       int:



       """simple docstring"""

       A__       =    Accelerator(
           split_batches=lowercase_ ,				dispatch_batches=lowercase_ ,				gradient_accumulation_steps=2						)
       # Test that context manager behaves properly
       A__ ,							A__ ,							A__ ,							A__ ,							A__ ,							A__ ,							A__       =    get_training_setup(lowercase_ ,				lowercase_						)
       for iteration, batch in enumerate(lowercase_						):
              A__ ,							A__       =    batch.values()
              # Gather the distributed inputs and targs for the base model
              A__ ,							A__       =    accelerator.gather((ddp_input, ddp_target)						)
              A__ ,							A__       =    input.to(accelerator.device						), target.to(accelerator.device						)
              # Perform our initial ground truth step in non "DDP"
              model.train()
              ddp_model.train()
              step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              opt.step()

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase_						)):
                     if split_batches:
                            sched.step()
                     else:
                            for _ in range(accelerator.num_processes						):
                                   sched.step()
              opt.zero_grad()
              # Perform gradient accumulation under wrapper
              with accelerator.accumulate(lowercase_						):
                     step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
                     ddp_opt.step()
                     ddp_sched.step()
                     ddp_opt.zero_grad()

              # Learning rates should be the same
              assert (
                  opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
              ), f"""Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n"""
              A__       =    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase_						))
              if accelerator.num_processes > 1:
                     check_model_parameters(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration						)
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE				(       )   ->       Optional[Any]:



       """simple docstring"""

       A__       =    Accelerator()

       A__       =    RegressionDataset(length=80						)
       A__       =    DataLoader(lowercase_ ,				batch_size=16						)
       A__       =    RegressionDataset(length=96						)
       A__       =    DataLoader(lowercase_ ,				batch_size=16						)
       A__ ,							A__       =    accelerator.prepare(lowercase_ ,				lowercase_						)
       assert accelerator.gradient_state.active_dataloader is None
       for iteration, _ in enumerate(lowercase_						):
              assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase_						)
              if iteration < len(lowercase_						) - 1:
                     assert not accelerator.gradient_state.end_of_dataloader
                     if iteration == 1:
                            for batch_num, _ in enumerate(lowercase_						):
                                   assert id(accelerator.gradient_state.active_dataloader						) == id(lowercase_						)
                                   if batch_num < len(lowercase_						) - 1:
                                          assert not accelerator.gradient_state.end_of_dataloader
                                   else:
                                          assert accelerator.gradient_state.end_of_dataloader
              else:
                     assert accelerator.gradient_state.end_of_dataloader
       assert accelerator.gradient_state.active_dataloader is None







def SCREAMING_SNAKE_CASE				(       )   ->       Dict:



       """simple docstring"""

       A__       =    Accelerator()
       A__       =    accelerator.state
       if state.local_process_index == 0:
              print('''**Test `accumulate` gradient accumulation with dataloader break**'''						)
       test_dataloader_break()
       if state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print('''**Test NOOP `no_sync` context manager**'''						)
              test_noop_sync(lowercase_						)
       if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
              if state.local_process_index == 0:
                     print('''**Test Distributed `no_sync` context manager**'''						)
              test_distributed_sync(lowercase_						)
       if state.distributed_type == DistributedType.MULTI_GPU:
              for split_batch in [True, False]:
                     for dispatch_batches in [True, False]:
                            if state.local_process_index == 0:
                                   print(
                                       '''**Test `accumulate` gradient accumulation, ''' ,				f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" ,				)
                            test_gradient_accumulation(lowercase_ ,				lowercase_						)

    # Currently will break on torch 2.0 +, need to investigate why
       if is_torch_version('''<''' ,				'''2.0'''						) or state.distributed_type == DistributedType.NO:
              if state.local_process_index == 0:
                     print(
                         '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,				'''`split_batches=False`, `dispatch_batches=False`**''' ,				)
              test_gradient_accumulation_with_opt_and_scheduler()
              if state.distributed_type == DistributedType.MULTI_GPU:
                     for split_batch in [True, False]:
                            for dispatch_batches in [True, False]:
                                   if not split_batch and not dispatch_batches:
                                          continue
                                   if state.local_process_index == 0:
                                          print(
                                              '''**Test `accumulate` gradient accumulation with optimizer and scheduler, ''' ,				f"""`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**""" ,				)
                                   test_gradient_accumulation_with_opt_and_scheduler(lowercase_ ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       main()


if __name__ == "__main__":
  main()








import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

       A__       =    args.log_outputs
       A__       =    '''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

       # load metric
       A__       =    load_metric('''wer'''						)
       A__       =    load_metric('''cer'''						)

       # compute metrics
       A__       =    wer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)
       A__       =    cer.compute(references=result['''target'''] ,				predictions=result['''prediction''']						)

       # print & log results
       A__       =    f"""WER: {wer_result}\nCER: {cer_result}"""
       print(lowercase_						)

       with open(f"""{dataset_id}_eval_results.txt""" ,				'''w'''						) as f:
              f.write(lowercase_						)

       # log all results in text file. Possibly interesting for analysis
       if log_outputs is not None:
              A__       =    f"""log_{dataset_id}_predictions.txt"""
              A__       =    f"""log_{dataset_id}_targets.txt"""

              with open(lowercase_ ,				'''w'''						) as p, open(lowercase_ ,				'''w'''						) as t:
                     # mapping function to write output
                     def write_to_file(lowercase_ ,				lowercase_						):
                            p.write(f"""{i}""" + '''\n'''						)
                            p.write(batch['''prediction'''] + '''\n'''						)
                            t.write(f"""{i}""" + '''\n'''						)
                            t.write(batch['''target'''] + '''\n'''						)

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

       A__       =    '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       A__       =    re.sub(lowercase_ ,				'''''' ,				text.lower()						)

       # In addition, we can normalize the target text, e.g. removing new lines characters etc...
       # note that order is important here!
       A__       =    ['''\n\n''', '''\n''', '''   ''', '''  ''']

       for t in token_sequences_to_ignore:
              A__       =    ''' '''.join(text.split(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

       # for testing: only process the first two examples as a test
       # dataset = dataset.select(range(10))

       # load processor
       A__       =    AutoFeatureExtractor.from_pretrained(args.model_id						)
       A__       =    feature_extractor.sampling_rate

       # resample audio
       A__       =    dataset.cast_column('''audio''' ,				Audio(sampling_rate=lowercase_						)						)

       # load eval pipeline
       if args.device is None:
              A__       =    0 if torch.cuda.is_available() else -1
       A__       =    pipeline('''automatic-speech-recognition''' ,				model=args.model_id ,				device=args.device						)

       # map function to decode audio
       def map_to_pred(lowercase_						):
              A__       =    asr(
                  batch['''audio''']['''array'''] ,				chunk_length_s=args.chunk_length_s ,				stride_length_s=args.stride_length_s						)

              A__       =    prediction['''text''']
              A__       =    normalize_text(batch['''sentence''']						)
              return batch

       # run inference on all examples
       A__       =    dataset.map(lowercase_ ,				remove_columns=dataset.column_names						)

       # compute and log_results
       # do not change function below
       log_results(lowercase_ ,				lowercase_						)


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

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


