

'''simple docstring'''

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    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Dict						      =					args.log_outputs
  SCREAMING_SNAKE_CASE:str						      =					"_".join(args.dataset.split("/" ) + [args.config, args.split] )

  # load metric
  SCREAMING_SNAKE_CASE:Tuple						      =					load_metric("wer" )
  SCREAMING_SNAKE_CASE:List[str]						      =					load_metric("cer" )

  # compute metrics
  SCREAMING_SNAKE_CASE:Any						      =					wer.compute(references=result["target"]      ,       predictions=result["prediction"] )
  SCREAMING_SNAKE_CASE:str						      =					cer.compute(references=result["target"]      ,       predictions=result["prediction"] )

  # print & log results
  SCREAMING_SNAKE_CASE:Any						      =					F'''WER: {wer_result}\nCER: {cer_result}'''
  print(snake_case )

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

  # log all results in text file. Possibly interesting for analysis
  if log_outputs is not None:
    SCREAMING_SNAKE_CASE:Dict						      =					F'''log_{dataset_id}_predictions.txt'''
    SCREAMING_SNAKE_CASE:Dict						      =					F'''log_{dataset_id}_targets.txt'''

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

      result.map(snake_case      ,       with_indices=snake_case )





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

  SCREAMING_SNAKE_CASE:int						      =					re.sub(snake_case      ,       ""      ,       text.lower() )

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

  for t in token_sequences_to_ignore:
    SCREAMING_SNAKE_CASE:Tuple						      =					" ".join(text.split(snake_case ) )

  return text





def    A_  ( snake_case ):
  # load dataset
  SCREAMING_SNAKE_CASE:Any						      =					load_dataset(args.dataset      ,       args.config      ,       split=args.split      ,       use_auth_token=snake_case )

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

  # load processor
  SCREAMING_SNAKE_CASE:Dict						      =					AutoFeatureExtractor.from_pretrained(args.model_id )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					feature_extractor.sampling_rate

  # resample audio
  SCREAMING_SNAKE_CASE:Dict						      =					dataset.cast_column("audio"      ,       Audio(sampling_rate=snake_case ) )

  # load eval pipeline
  if args.device is None:
    SCREAMING_SNAKE_CASE:Any						      =					0 if torch.cuda.is_available() else -1
  SCREAMING_SNAKE_CASE:Optional[int]						      =					pipeline("automatic-speech-recognition"      ,       model=args.model_id      ,       device=args.device )

  # map function to decode audio
  def map_to_pred(snake_case ):
    SCREAMING_SNAKE_CASE:Any						      =					asr(
        batch["audio"]["array"]      ,       chunk_length_s=args.chunk_length_s      ,       stride_length_s=args.stride_length_s )

    SCREAMING_SNAKE_CASE:int						      =					prediction["text"]
    SCREAMING_SNAKE_CASE:Optional[int]						      =					normalize_text(batch["sentence"] )
    return batch

  # run inference on all examples
  SCREAMING_SNAKE_CASE:Any						      =					dataset.map(snake_case      ,       remove_columns=dataset.column_names )

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


if __name__ == "__main__":
    A_       						=							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.",
    )
    A_       						=							parser.parse_args()

    main(args)









'''simple docstring'''

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


A_       						=							"true"





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=16 ):
  set_seed(42 )
  SCREAMING_SNAKE_CASE:Optional[int]						      =					RegressionModel()
  SCREAMING_SNAKE_CASE:int						      =					deepcopy(snake_case )
  SCREAMING_SNAKE_CASE:List[str]						      =					RegressionDataset(length=snake_case )
  SCREAMING_SNAKE_CASE:int						      =					DataLoader(snake_case      ,       batch_size=snake_case )
  model.to(accelerator.device )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					accelerator.prepare(snake_case      ,       snake_case )
  return model, ddp_model, dataloader





def    A_  ( snake_case      ,       snake_case=False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
  SCREAMING_SNAKE_CASE:Dict						      =					load_dataset("glue"      ,       "mrpc"      ,       split="validation" )

  def tokenize_function(snake_case ):
    SCREAMING_SNAKE_CASE:Optional[int]						      =					tokenizer(examples["sentence1"]      ,       examples["sentence2"]      ,       truncation=snake_case      ,       max_length=snake_case )
    return outputs

  with accelerator.main_process_first():
    SCREAMING_SNAKE_CASE:int						      =					dataset.map(
        snake_case      ,       batched=snake_case      ,       remove_columns=["idx", "sentence1", "sentence2"]      ,       )

  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					tokenized_datasets.rename_column("label"      ,       "labels" )

  def collate_fn(snake_case ):
    if use_longest:
      return tokenizer.pad(snake_case      ,       padding="longest"      ,       return_tensors="pt" )
    return tokenizer.pad(snake_case      ,       padding="max_length"      ,       max_length=128      ,       return_tensors="pt" )

  return DataLoader(snake_case      ,       shuffle=snake_case      ,       collate_fn=snake_case      ,       batch_size=16 )





def    A_  ( snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(dispatch_batches=snake_case      ,       split_batches=snake_case )
  SCREAMING_SNAKE_CASE:List[Any]						      =					get_dataloader(snake_case      ,       not dispatch_batches )
  SCREAMING_SNAKE_CASE:Tuple						      =					AutoModelForSequenceClassification.from_pretrained(
      "hf-internal-testing/mrpc-bert-base-cased"      ,       return_dict=snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.prepare(snake_case      ,       snake_case )
  return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator





def    A_  ( snake_case      ,       snake_case      ,       snake_case ):
  SCREAMING_SNAKE_CASE:str						      =					[]
  for batch in dataloader:
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					batch.values()
    with torch.no_grad():
      SCREAMING_SNAKE_CASE:int						      =					model(snake_case )
      SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:List[str]						      =					accelerator.gather_for_metrics((logit, target) )
      logits_and_targets.append((logit, target) )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Union[str, Any]						      =					[], []
  for logit, targ in logits_and_targets:
    logits.append(snake_case )
    targs.append(snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					torch.cat(snake_case ), torch.cat(snake_case )
  return logits, targs





def    A_  ( snake_case      ,       snake_case=82      ,       snake_case=False      ,       snake_case=False      ,       snake_case=16 ):
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					get_basic_setup(snake_case      ,       snake_case      ,       snake_case )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Optional[Any]						      =					generate_predictions(snake_case      ,       snake_case      ,       snake_case )
  assert (
      len(snake_case ) == num_samples
  ), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(snake_case )}'''





def    A_  ( snake_case = False      ,       snake_case = False ):
  SCREAMING_SNAKE_CASE:List[Any]						      =					evaluate.load("glue"      ,       "mrpc" )
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Dict						      =					get_mrpc_setup(snake_case      ,       snake_case )
  # First do baseline
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["no"]
  model.to(snake_case )
  model.eval()
  for batch in dataloader:
    batch.to(snake_case )
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Dict						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Any						      =					outputs.logits.argmax(dim=-1 )
    metric.add_batch(predictions=snake_case      ,       references=batch["labels"] )
  SCREAMING_SNAKE_CASE:Union[str, Any]						      =					metric.compute()

  # Then do distributed
  SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:Tuple						      =					setup["ddp"]
  model.eval()
  for batch in dataloader:
    with torch.inference_mode():
      SCREAMING_SNAKE_CASE:Any						      =					model(**snake_case )
    SCREAMING_SNAKE_CASE:Optional[Any]						      =					outputs.logits.argmax(dim=-1 )
    SCREAMING_SNAKE_CASE:List[str]						      =					batch["labels"]
    SCREAMING_SNAKE_CASE      ,    SCREAMING_SNAKE_CASE:int						      =					accelerator.gather_for_metrics((preds, references) )
    metric.add_batch(predictions=snake_case      ,       references=snake_case )
  SCREAMING_SNAKE_CASE:Any						      =					metric.compute()

  for key in "accuracy f1".split():
    assert math.isclose(
        baseline[key]      ,       distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''





def    A_  ( ):
  SCREAMING_SNAKE_CASE:Optional[int]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
  if accelerator.is_local_main_process:
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_warning()
  else:
    datasets.utils.logging.set_verbosity_error()
    transformers.utils.logging.set_verbosity_error()
  # These are a bit slower so they should only be ran on the GPU or TPU
  if torch.cuda.is_available() or is_tpu_available():
    if accelerator.is_local_main_process:
      print("**Testing gather_for_metrics**" )
    for split_batches in [True, False]:
      for dispatch_batches in [True, False]:
        if accelerator.is_local_main_process:
          print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
        test_mrpc(snake_case      ,       snake_case )
        accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test torch metrics**" )
  for split_batches in [True, False]:
    for dispatch_batches in [True, False]:
      SCREAMING_SNAKE_CASE:Optional[Any]						      =					Accelerator(split_batches=snake_case      ,       dispatch_batches=snake_case )
      if accelerator.is_local_main_process:
        print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
      test_torch_metrics(snake_case      ,       99 )
      accelerator.state._reset_state()
  if accelerator.is_local_main_process:
    print("**Test last batch is not dropped when perfectly divisible**" )
  SCREAMING_SNAKE_CASE:List[str]						      =					Accelerator()
  test_torch_metrics(snake_case      ,       512 )
  accelerator.state._reset_state()





def    A_  ( snake_case ):
  # For xla_spawn (TPUs)
  main()


if __name__ == "__main__":
    main()






