

'''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 argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
A_       						=							[
    "kernels/rwkv/wkv_cuda.cu",
    "kernels/rwkv/wkv_op.cpp",
    "kernels/deformable_detr/ms_deform_attn.h",
    "kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh",
    "models/graphormer/algos_graphormer.pyx",
]





def    A_  ( snake_case ):
  # Test all the extensions added in the setup
  for file in FILES_TO_FIND:
    if not (transformers_path / file).exists():
      return False
  return True


if __name__ == "__main__":
    A_       						=							argparse.ArgumentParser()
    parser.add_argument("--check_lib", action="store_true", help="Whether to check the build or the actual package.")
    A_       						=							parser.parse_args()
    if args.check_lib:
        A_       						=							importlib.import_module("transformers")
        A_       						=							Path(transformers_module.__file__).parent
    else:
        A_       						=							Path.cwd() / "build/lib/transformers"
    if not test_custom_files_are_present(transformers_path):
        raise ValueError("The built release does not contain the custom files. Fix this before going further!")






