





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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       List[Any]:


      '''simple docstring'''


      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(_SCREAMING_SNAKE_CASE     )

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

      # 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(_SCREAMING_SNAKE_CASE			,						"""w"""     ) as p, open(_SCREAMING_SNAKE_CASE			,						"""w"""     ) as t:
                  # mapping function to write output
                  def write_to_file(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                        p.write(F"""{i}""" + """\n"""     )
                        p.write(batch["""prediction"""] + """\n"""     )
                        t.write(F"""{i}""" + """\n"""     )
                        t.write(batch["""target"""] + """\n"""     )

                  result.map(_SCREAMING_SNAKE_CASE			,						with_indices=_SCREAMING_SNAKE_CASE     )



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						re.sub(_SCREAMING_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		=						["""\n\n""", """\n""", """   """, """  """]

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

      return text



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						load_dataset(args.dataset			,						args.config			,						split=args.split			,						use_auth_token=_SCREAMING_SNAKE_CASE     )

      # 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=_SCREAMING_SNAKE_CASE     )     )

      # 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(_SCREAMING_SNAKE_CASE     ):
            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(_SCREAMING_SNAKE_CASE			,						remove_columns=dataset.column_names     )

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


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

      main(args)









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


