






'''simple docstring'''

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


UpperCamelCase							=NewType("DataClass", Any)
UpperCamelCase							=NewType("DataClassType", Any)



def 							snake_case					(     a_							: Optional[Any]	)       ->				Any:





      """simple docstring"""

      if isinstance(a_    ,   a_	):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise ArgumentTypeError(
                f"Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive)."	)



def 							snake_case					(     a_							: list	)       ->				Callable[[str], Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							{str(a_	): choice for choice in choices}
      return lambda a_	: str_to_choice.get(a_    ,   a_	)



def 							snake_case					(     *,
    a_							: Union[str, List[str]] = None    ,   a_							: str = None    ,   a_							: Any = dataclasses.MISSING    ,   a_							: Callable[[], Any] = dataclasses.MISSING    ,   a_							: dict = None    ,   **a_							: str    ,   )       ->				dataclasses.Field:





      """simple docstring"""

      if metadata is None:
            # Important, don't use as default param in function signature because dict is mutable and shared across function calls
            UpperCamelCase_				:   Tuple					    =							{}
      if aliases is not None:
            UpperCamelCase_				:   Dict					    =							aliases
      if help is not None:
            UpperCamelCase_				:   Tuple					    =							help

      return dataclasses.field(metadata=a_    ,   default=a_    ,   default_factory=a_    ,   **a_	)




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Iterable[DataClassType]
 def __init__(							self			,     __lowerCAmelCase			,     **__lowerCAmelCase ):
       # To make the default appear when using --help
       if "formatter_class" not in kwargs:
             UpperCamelCase_				:   Optional[Any]					    =							ArgumentDefaultsHelpFormatter
       super().__init__(**__lowerCAmelCase )
       if dataclasses.is_dataclass(__lowerCAmelCase ):
             UpperCamelCase_				:   Tuple					    =							[dataclass_types]
       UpperCamelCase_				:   List[Any]					    =							list(__lowerCAmelCase )
       for dtype in self.dataclass_types:
             self._add_dataclass_arguments(__lowerCAmelCase )
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Optional[int]					    =							F"--{field.name}"
       UpperCamelCase_				:   Any					    =							field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type			,     __lowerCAmelCase ):
             raise RuntimeError(
                 """Unresolved type detected, which should have been done with the help of """
                 """`typing.get_type_hints` method by default""" )

       UpperCamelCase_				:   Union[str, Any]					    =							kwargs.pop("""aliases"""			,     [] )
       if isinstance(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   str					    =							[aliases]

       UpperCamelCase_				:   str					    =							getattr(field.type			,     """__origin__"""			,     field.type )
       if origin_type is Union or (hasattr(__lowerCAmelCase			,     """UnionType""" ) and isinstance(__lowerCAmelCase			,     types.UnionType )):
             if str not in field.type.__args__ and (
                 len(field.type.__args__ ) != 2 or type(__lowerCAmelCase ) not in field.type.__args__
             ):
                   raise ValueError(
                       """Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because"""
                       """ the argument parser only supports one type per argument."""
                       F" Problem encountered in field '{field.name}'." )
             if type(__lowerCAmelCase ) not in field.type.__args__:
                   # filter `str` in Union
                   UpperCamelCase_				:   Tuple					    =							field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                   UpperCamelCase_				:   Tuple					    =							getattr(field.type			,     """__origin__"""			,     field.type )
             elif bool not in field.type.__args__:
                   # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                   UpperCamelCase_				:   List[Any]					    =							(
                       field.type.__args__[0] if isinstance(__lowerCAmelCase			,     field.type.__args__[1] ) else field.type.__args__[1]
                   )
                   UpperCamelCase_				:   Tuple					    =							getattr(field.type			,     """__origin__"""			,     field.type )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       UpperCamelCase_				:   List[Any]					    =							{}
       if origin_type is Literal or (isinstance(field.type			,     __lowerCAmelCase ) and issubclass(field.type			,     __lowerCAmelCase )):
             if origin_type is Literal:
                   UpperCamelCase_				:   int					    =							field.type.__args__
             else:
                   UpperCamelCase_				:   List[str]					    =							[x.value for x in field.type]

             UpperCamelCase_				:   str					    =							make_choice_type_function(kwargs["""choices"""] )

             if field.default is not dataclasses.MISSING:
                   UpperCamelCase_				:   Dict					    =							field.default
             else:
                   UpperCamelCase_				:   int					    =							True
       elif field.type is bool or field.type == Optional[bool]:
             # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
             # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
             UpperCamelCase_				:   Tuple					    =							copy(__lowerCAmelCase )

             # Hack because type=bool in argparse does not behave as we want.
             UpperCamelCase_				:   Tuple					    =							string_to_bool
             if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                   # Default value is False if we have no default when of type bool.
                   UpperCamelCase_				:   Dict					    =							False if field.default is dataclasses.MISSING else field.default
                   # This is the value that will get picked if we don't include --field_name in any way
                   UpperCamelCase_				:   Union[str, Any]					    =							default
                   # This tells argparse we accept 0 or 1 value after --field_name
                   UpperCamelCase_				:   Optional[Any]					    =							"""?"""
                   # This is the value that will get picked if we do --field_name (without value)
                   UpperCamelCase_				:   List[str]					    =							True
       elif isclass(__lowerCAmelCase ) and issubclass(__lowerCAmelCase			,     __lowerCAmelCase ):
             UpperCamelCase_				:   Dict					    =							field.type.__args__[0]
             UpperCamelCase_				:   Optional[Any]					    =							"""+"""
             if field.default_factory is not dataclasses.MISSING:
                   UpperCamelCase_				:   Optional[Any]					    =							field.default_factory()
             elif field.default is dataclasses.MISSING:
                   UpperCamelCase_				:   Tuple					    =							True
       else:
             UpperCamelCase_				:   Optional[int]					    =							field.type
             if field.default is not dataclasses.MISSING:
                   UpperCamelCase_				:   List[Any]					    =							field.default
             elif field.default_factory is not dataclasses.MISSING:
                   UpperCamelCase_				:   Any					    =							field.default_factory()
             else:
                   UpperCamelCase_				:   Optional[Any]					    =							True
       parser.add_argument(__lowerCAmelCase			,     *__lowerCAmelCase			,     **__lowerCAmelCase )

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
             UpperCamelCase_				:   int					    =							False
             parser.add_argument(F"--no_{field.name}"			,     action="""store_false"""			,     dest=field.name			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if hasattr(__lowerCAmelCase			,     """_argument_group_name""" ):
             UpperCamelCase_				:   str					    =							self.add_argument_group(dtype._argument_group_name )
       else:
             UpperCamelCase_				:   Union[str, Any]					    =							self

       try:
             UpperCamelCase_				:   Dict[str, type]					    =							get_type_hints(__lowerCAmelCase )
       except NameError:
             raise RuntimeError(
                 F"Type resolution failed for {dtype}. Try declaring the class in global scope or "
                 """removing line of `from __future__ import annotations` which opts in Postponed """
                 """Evaluation of Annotations (PEP 563)""" )
       except TypeError as ex:
             # Remove this block when we drop Python 3.9 support
             if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(__lowerCAmelCase ):
                   UpperCamelCase_				:   Optional[Any]					    =							""".""".join(map(__lowerCAmelCase			,     sys.version_info[:3] ) )
                   raise RuntimeError(
                       F"Type resolution failed for {dtype} on Python {python_version}. Try removing "
                       """line of `from __future__ import annotations` which opts in union types as """
                       """`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To """
                       """support Python versions that lower than 3.10, you need to use """
                       """`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of """
                       """`X | None`.""" ) from ex
             raise

       for field in dataclasses.fields(__lowerCAmelCase ):
             if not field.init:
                   continue
             UpperCamelCase_				:   Optional[Any]					    =							type_hints[field.name]
             self._parse_dataclass_field(__lowerCAmelCase			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=False			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     ):

       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv )):
             UpperCamelCase_				:   Optional[int]					    =							[]

             if args_filename:
                   args_files.append(Path(__lowerCAmelCase ) )
             elif look_for_args_file and len(sys.argv ):
                   args_files.append(Path(sys.argv[0] ).with_suffix(""".args""" ) )

             # args files specified via command line flag should overwrite default args files so we add them last
             if args_file_flag:
                   # Create special parser just to extract the args_file_flag values
                   UpperCamelCase_				:   Dict					    =							ArgumentParser()
                   args_file_parser.add_argument(__lowerCAmelCase			,     type=__lowerCAmelCase			,     action="""append""" )

                   # Use only remaining args for further parsing (remove the args_file_flag)
                   UpperCamelCase_    ,			UpperCamelCase_				:   Optional[int]					    =							args_file_parser.parse_known_args(args=__lowerCAmelCase )
                   UpperCamelCase_				:   List[str]					    =							vars(__lowerCAmelCase ).get(args_file_flag.lstrip("""-""" )			,     __lowerCAmelCase )

                   if cmd_args_file_paths:
                         args_files.extend([Path(__lowerCAmelCase ) for p in cmd_args_file_paths] )

             UpperCamelCase_				:   Dict					    =							[]
             for args_file in args_files:
                   if args_file.exists():
                         file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
             UpperCamelCase_				:   List[str]					    =							file_args + args if args is not None else file_args + sys.argv[1:]
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							self.parse_known_args(args=__lowerCAmelCase )
       UpperCamelCase_				:   List[str]					    =							[]
       for dtype in self.dataclass_types:
             UpperCamelCase_				:   Optional[Any]					    =							{f.name for f in dataclasses.fields(__lowerCAmelCase ) if f.init}
             UpperCamelCase_				:   int					    =							{k: v for k, v in vars(__lowerCAmelCase ).items() if k in keys}
             for k in keys:
                   delattr(__lowerCAmelCase			,     __lowerCAmelCase )
             UpperCamelCase_				:   str					    =							dtype(**__lowerCAmelCase )
             outputs.append(__lowerCAmelCase )
       if len(namespace.__dict__ ) > 0:
             # additional namespace.
             outputs.append(__lowerCAmelCase )
       if return_remaining_strings:
             return (*outputs, remaining_args)
       else:
             if remaining_args:
                   raise ValueError(F"Some specified arguments are not used by the HfArgumentParser: {remaining_args}" )

             return (*outputs,)
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = False ):
       UpperCamelCase_				:   Optional[int]					    =							set(args.keys() )
       UpperCamelCase_				:   List[Any]					    =							[]
       for dtype in self.dataclass_types:
             UpperCamelCase_				:   Any					    =							{f.name for f in dataclasses.fields(__lowerCAmelCase ) if f.init}
             UpperCamelCase_				:   Any					    =							{k: v for k, v in args.items() if k in keys}
             unused_keys.difference_update(inputs.keys() )
             UpperCamelCase_				:   List[str]					    =							dtype(**__lowerCAmelCase )
             outputs.append(__lowerCAmelCase )
       if not allow_extra_keys and unused_keys:
             raise ValueError(F"Some keys are not used by the HfArgumentParser: {sorted(__lowerCAmelCase )}" )
       return tuple(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = False ):
       with open(Path(__lowerCAmelCase )			,     encoding="""utf-8""" ) as open_json_file:
             UpperCamelCase_				:   str					    =							json.loads(open_json_file.read() )
       UpperCamelCase_				:   Optional[int]					    =							self.parse_dict(__lowerCAmelCase			,     allow_extra_keys=__lowerCAmelCase )
       return tuple(__lowerCAmelCase )





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase = False ):
       UpperCamelCase_				:   Tuple					    =							self.parse_dict(yaml.safe_load(Path(__lowerCAmelCase ).read_text() )			,     allow_extra_keys=__lowerCAmelCase )
       return tuple(__lowerCAmelCase )














'''simple docstring'''

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


UpperCamelCase							=""


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




      class     A    (      tr.AbstractTransform						):






       """simple docstring"""





       def __init__(							self			,     __lowerCAmelCase = " " ):
             UpperCamelCase_				:   str					    =							sentence_delimiter
       def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
             return list(__lowerCAmelCase )





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






      UpperCamelCase							=tr.Compose(
          [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
      )
else:
      UpperCamelCase							=tr.Compose(
          [
              tr.RemoveMultipleSpaces(),
              tr.Strip(),
              tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
              tr.ReduceToListOfListOfChars(),
          ]
      )


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

UpperCamelCase							="\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER'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\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

UpperCamelCase							="\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"




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






 """simple docstring"""





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

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

       return incorrect / total






