
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__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  args.log_outputs
      SCREAMING_SNAKE_CASE_          =  "_".join(args.dataset.split("/"				) + [args.config, args.split]				)

      # load metric
      SCREAMING_SNAKE_CASE_          =  load_metric("wer"				)
      SCREAMING_SNAKE_CASE_          =  load_metric("cer"				)

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

      # print & log results
      SCREAMING_SNAKE_CASE_          =  F'''WER: {wer_result}\nCER: {cer_result}'''
      print(__UpperCamelCase				)

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

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

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

                  result.map(__UpperCamelCase     ,      with_indices=__UpperCamelCase				)


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

      SCREAMING_SNAKE_CASE_          =  re.sub(__UpperCamelCase     ,      ""     ,      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_          =  ["\n\n", "\n", "   ", "  "]

      for t in token_sequences_to_ignore:
            SCREAMING_SNAKE_CASE_          =  " ".join(text.split(__UpperCamelCase				)				)

      return text


def      a__ (		__UpperCamelCase				):
      # load dataset
      SCREAMING_SNAKE_CASE_          =  load_dataset(args.dataset     ,      args.config     ,      split=args.split     ,      use_auth_token=__UpperCamelCase				)

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

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

      # resample audio
      SCREAMING_SNAKE_CASE_          =  dataset.cast_column("audio"     ,      Audio(sampling_rate=__UpperCamelCase				)				)

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

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

            SCREAMING_SNAKE_CASE_          =  prediction["text"]
            SCREAMING_SNAKE_CASE_          =  normalize_text(batch["sentence"]				)
            return batch

      # run inference on all examples
      SCREAMING_SNAKE_CASE_          =  dataset.map(__UpperCamelCase     ,      remove_columns=dataset.column_names				)

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


if __name__ == "__main__":
      A     :  Dict					     =       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     :  Union[str, Any]					     =       parser.parse_args()

      main(args)



import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar


class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  def    __A     (							self :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  logging.get_logger()

        # the current default level is logging.WARNING
        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()

        logging.set_verbosity_error()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_warning()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_info()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        logging.set_verbosity_debug()
        self.assertEqual(logger.getEffectiveLevel()  ,     logging.get_verbosity()   )

        # restore to the original level
        logging.set_verbosity(__magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "Testing 1, 2, 3"

        # should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
        if level_origin <= logging.WARNING:
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning(__magic_name__   )
              self.assertEqual(cl.out  ,     msg + "\n"   )

        # this is setting the level for all of `transformers.*` loggers
        logging.set_verbosity_error()

        # should not be able to log warnings
        with CaptureLogger(__magic_name__   ) as cl:
              logger.warning(__magic_name__   )
        self.assertEqual(cl.out  ,     ""   )

        # should be able to log warnings again
        logging.set_verbosity_warning()
        with CaptureLogger(__magic_name__   ) as cl:
              logger.warning(__magic_name__   )
        self.assertEqual(cl.out  ,     msg + "\n"   )

        # restore to the original level
        logging.set_verbosity(__magic_name__   )







  @mockenv(TRANSFORMERS_VERBOSITY="error"   )
  def    __A     (							self :       int   )  ->	Optional[Any]:
        # reset for the env var to take effect, next time some logger call is made
        transformers.utils.logging._reset_library_root_logger()
        # this action activates the env var
        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )

        SCREAMING_SNAKE_CASE_          =  os.getenv("TRANSFORMERS_VERBOSITY"  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  logging.log_levels[env_level_str]

        SCREAMING_SNAKE_CASE_          =  logging.get_verbosity()
        self.assertEqual(
            __magic_name__  ,     __magic_name__  ,     F'''TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}'''  ,     )

        # restore to the original level
        SCREAMING_SNAKE_CASE_          =  ""
        transformers.utils.logging._reset_library_root_logger()







  @mockenv(TRANSFORMERS_VERBOSITY="super-error"   )
  def    __A     (							self :       Dict   )  ->	List[Any]:
        # reset for the env var to take effect, next time some logger call is made
        transformers.utils.logging._reset_library_root_logger()
        SCREAMING_SNAKE_CASE_          =  logging.logging.getLogger()
        with CaptureLogger(__magic_name__   ) as cl:
              # this action activates the env var
              logging.get_logger("transformers.models.bart.tokenization_bart"   )
        self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error"  ,     cl.out   )

        # no need to restore as nothing was changed







  def    __A     (							self :       Dict   )  ->	Optional[int]:
        # testing `logger.warning_advice()`
        transformers.utils.logging._reset_library_root_logger()

        SCREAMING_SNAKE_CASE_          =  logging.get_logger("transformers.models.bart.tokenization_bart"   )
        SCREAMING_SNAKE_CASE_          =  "Testing 1, 2, 3"

        with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1"   ):
              # nothing should be logged as env var disables this method
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning_advice(__magic_name__   )
              self.assertEqual(cl.out  ,     ""   )

        with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=""   ):
              # should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
              with CaptureLogger(__magic_name__   ) as cl:
                    logger.warning_advice(__magic_name__   )
              self.assertEqual(cl.out  ,     msg + "\n"   )



def      a__ (		):
      disable_progress_bar()
      assert are_progress_bars_disabled()

      enable_progress_bar()
      assert not are_progress_bars_disabled()


