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     UpperCAmelCase__ (       lowerCAmelCase__  :Dataset      ,	lowerCAmelCase__  :Dict[str, str]	)				->					int:
      '''simple docstring'''


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

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

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

      # print & log results
      lowercase														=			f'WER: {wer_result}\nCER: {cer_result}'
      print(lowerCAmelCase__	)

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

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

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

                  result.map(lowerCAmelCase__      ,	with_indices=lowerCAmelCase__	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					str:
      '''simple docstring'''


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

      lowercase														=			re.sub(lowerCAmelCase__      ,	""""""      ,	text.lower()	)

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

      for t in token_sequences_to_ignore:
            lowercase														=			""" """.join(text.split(lowerCAmelCase__	)	)

      return text


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Dict:
      '''simple docstring'''


      lowercase														=			load_dataset(args.dataset      ,	args.config      ,	split=args.split      ,	use_auth_token=lowerCAmelCase__	)

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

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

      # resample audio
      lowercase														=			dataset.cast_column("""audio"""      ,	Audio(sampling_rate=lowerCAmelCase__	)	)

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

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

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

      # run inference on all examples
      lowercase														=			dataset.map(lowerCAmelCase__      ,	remove_columns=dataset.column_names	)

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


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

       main(args)




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def     UpperCAmelCase__ (       )				->					Tuple:
      '''simple docstring'''


      lowercase														=			ArgumentParser("""Accelerate CLI tool"""      ,	usage="""accelerate <command> [<args>]"""      ,	allow_abbrev=lowerCAmelCase__	)
      lowercase														=			parser.add_subparsers(help="""accelerate command helpers"""	)

      # Register commands
      get_config_parser(subparsers=lowerCAmelCase__	)
      env_command_parser(subparsers=lowerCAmelCase__	)
      launch_command_parser(subparsers=lowerCAmelCase__	)
      tpu_command_parser(subparsers=lowerCAmelCase__	)
      test_command_parser(subparsers=lowerCAmelCase__	)

      # Let's go
      lowercase														=			parser.parse_args()

      if not hasattr(lowerCAmelCase__      ,	"""func"""	):
            parser.print_help()
            exit(1	)

      # Run
      args.func(lowerCAmelCase__	)


if __name__ == "__main__":
       main()



