






from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("""3.8"""):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata


snake_case__      :	Any       	=      """"""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):


 class 						_a       (  tr.AbstractTransform    ):






   """simple docstring"""





   def __init__(     self		,		_UpperCAmelCase = " " )			-> Optional[int]:
      UpperCamelCase_									=     sentence_delimiter




   def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Optional[int]:
      return list(_UpperCAmelCase )




   def    _UpperCAmelCase     (     self		,		_UpperCAmelCase )			-> Optional[Any]:
      UpperCamelCase_									=     []
      for sent_idx, sentence in enumerate(_UpperCAmelCase ):
         chars.extend(self.process_string(_UpperCAmelCase ) )
         if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(_UpperCAmelCase ) - 1:
            chars.append(self.sentence_delimiter )
      return chars




 snake_case__      :	List[str]       	=      tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 snake_case__      :	Optional[int]       	=      tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


snake_case__      :	int       	=      """\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
"""

snake_case__      :	int       	=      """\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
"""

snake_case__      :	Optional[int]       	=      """
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,       _KWARGS_DESCRIPTION    )
class 						_a       (  datasets.Metric    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> List[str]:
     return datasets.MetricInfo(
         description=_DESCRIPTION		,		citation=_CITATION		,		inputs_description=_KWARGS_DESCRIPTION		,		features=datasets.Features(
             {
                 'predictions': datasets.Value('string'		,		id='sequence' ),
                 'references': datasets.Value('string'		,		id='sequence' ),
             } )		,		codebase_urls=['https://github.com/jitsi/jiwer/']		,		reference_urls=[
             'https://en.wikipedia.org/wiki/Word_error_rate',
             'https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates',
         ]		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> Any:
     if concatenate_texts:
        return jiwer.compute_measures(
            _UpperCAmelCase		,		_UpperCAmelCase		,		truth_transform=_UpperCAmelCase		,		hypothesis_transform=_UpperCAmelCase		,		)["wer"]

     UpperCamelCase_									=     0
     UpperCamelCase_									=     0
     for prediction, reference in zip(_UpperCAmelCase		,		_UpperCAmelCase ):
        UpperCamelCase_									=     jiwer.compute_measures(
            _UpperCAmelCase		,		_UpperCAmelCase		,		truth_transform=_UpperCAmelCase		,		hypothesis_transform=_UpperCAmelCase		,		)
        incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
        total += measures["substitutions"] + measures["deletions"] + measures["hits"]

     return incorrect / total













import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command


class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   inspect.getfile(accelerate.test_utils    )
  A_						=   os.path.sep.join(mod_file.split(os.path.sep    )[:-1] + ["""scripts""", """test_cli.py"""]    )

  A_						=   ["""accelerate""", """launch"""]
  A_						=   Path.home() / """.cache/huggingface/accelerate"""
  A_						=   """default_config.yaml"""
  A_						=   config_folder / config_file
  A_						=   config_folder / """_default_config.yaml"""

  A_						=   Path("""tests/test_configs"""    )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> Union[str, Any]:
     if cls.config_path.is_file():
        cls.config_path.rename(cls.changed_path )




  @classmethod
  def    _UpperCAmelCase     (     cls )			-> List[str]:
     if cls.changed_path.is_file():
        cls.changed_path.rename(cls.config_path )




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     self.base_cmd
     if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
        cmd += ["--multi_gpu"]
     execute_subprocess_async(cmd + [self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     for config in sorted(self.test_config_path.glob('**/*.yaml' ) ):
        with self.subTest(config_file=_UpperCAmelCase ):
           execute_subprocess_async(
               self.base_cmd + ['--config_file', str(_UpperCAmelCase ), self.test_file_path]		,		env=os.environ.copy() )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     execute_subprocess_async(['accelerate', 'test']		,		env=os.environ.copy() )




class 						_a       (  unittest.TestCase    ):






  """simple docstring"""





  A_						=   """test-tpu"""
  A_						=   """us-central1-a"""
  A_						=   """ls"""
  A_						=   ["""accelerate""", """tpu-config"""]
  A_						=   """cd /usr/share"""
  A_						=   """tests/test_samples/test_command_file.sh"""
  A_						=   """Running gcloud compute tpus tpu-vm ssh"""




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command',
             self.command,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Any:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']		,		return_stdout=_UpperCAmelCase )
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--command',
             self.command,
             '--command',
             'echo "Hello World"',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/0_12_0.yaml',
             '--command_file',
             self.command_file,
             '--tpu_zone',
             self.tpu_zone,
             '--tpu_name',
             self.tpu_name,
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_									=     run_command(
         self.cmd
         + [
             '--config_file',
             'tests/test_configs/latest.yaml',
             '--install_accelerate',
             '--accelerate_version',
             '12.0.0',
             '--debug',
         ]		,		return_stdout=_UpperCAmelCase		,		)
     self.assertIn(
         f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""		,		_UpperCAmelCase		,		)





