


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


SCREAMING_SNAKE_CASE :   List[Any]							      =				NewType("DataClass", Any)
SCREAMING_SNAKE_CASE :   List[str]							      =				NewType("DataClassType", Any)



def        UpperCamelCase_(   lowerCamelCase_				)					->		Union[str, Any]:
				if isinstance(lowerCamelCase_							,				lowerCamelCase_				):
								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        UpperCamelCase_(   lowerCamelCase_				)					->		Callable[[str], Any]:
				_lowercase :							List[str]   	=	{str(lowerCamelCase_				): choice for choice in choices}
				return lambda lowerCamelCase_				: str_to_choice.get(lowerCamelCase_							,				lowerCamelCase_				)



def        UpperCamelCase_(   *,
    lowerCamelCase_ = None							,				lowerCamelCase_ = None							,				lowerCamelCase_ = dataclasses.MISSING							,				lowerCamelCase_ = dataclasses.MISSING							,				lowerCamelCase_ = None							,				**lowerCamelCase_							,				)					->		dataclasses.Field:
				if metadata is None:
								# Important, don't use as default param in function signature because dict is mutable and shared across function calls
								_lowercase :							Optional[int]   	=	{}
				if aliases is not None:
								_lowercase :							Tuple   	=	aliases
				if help is not None:
								_lowercase :							Optional[int]   	=	help

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






class _lowerCamelCase(		_a     ):
							lowercase_				:			Iterable[DataClassType]
							def __init__(      self,   lowerCamelCase,   **lowerCamelCase) ->     Optional[Any]:




											"""simple docstring"""

											if "formatter_class" not in kwargs:
															_lowercase :							int   	=	ArgumentDefaultsHelpFormatter
											super().__init__(**lowerCamelCase)
											if dataclasses.is_dataclass(lowerCamelCase):
															_lowercase :							Optional[Any]   	=	[dataclass_types]
											_lowercase :							List[Any]   	=	list(lowerCamelCase)
											for dtype in self.dataclass_types:
															self._add_dataclass_arguments(lowerCamelCase)
							@staticmethod
							def     UpperCamelCase  (      lowerCamelCase,   lowerCamelCase) ->     List[Any]:




											"""simple docstring"""

											_lowercase :							Dict   	=	F'''--{field.name}'''
											_lowercase :							Tuple   	=	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')

											_lowercase :							List[Any]   	=	kwargs.pop('aliases',   [])
											if isinstance(lowerCamelCase,   lowerCamelCase):
															_lowercase :							Any   	=	[aliases]

											_lowercase :							Union[str, Any]   	=	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
																			_lowercase :							Optional[int]   	=	field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
																			_lowercase :							Any   	=	getattr(field.type,   '__origin__',   field.type)
															elif bool not in field.type.__args__:
																			# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
																			_lowercase :							Optional[Any]   	=	(
																			    field.type.__args__[0] if isinstance(lowerCamelCase,   field.type.__args__[1]) else field.type.__args__[1]
																			)
																			_lowercase :							List[Any]   	=	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)
											_lowercase :							List[Any]   	=	{}
											if origin_type is Literal or (isinstance(field.type,   lowerCamelCase) and issubclass(field.type,   lowerCamelCase)):
															if origin_type is Literal:
																			_lowercase :							Dict   	=	field.type.__args__
															else:
																			_lowercase :							Dict   	=	[x.value for x in field.type]

															_lowercase :							List[Any]   	=	make_choice_type_function(kwargs['choices'])

															if field.default is not dataclasses.MISSING:
																			_lowercase :							Optional[int]   	=	field.default
															else:
																			_lowercase :							Any   	=	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
															_lowercase :							Union[str, Any]   	=	copy(lowerCamelCase)

															# Hack because type=bool in argparse does not behave as we want.
															_lowercase :							List[str]   	=	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.
																			_lowercase :							Tuple   	=	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
																			_lowercase :							int   	=	default
																			# This tells argparse we accept 0 or 1 value after --field_name
																			_lowercase :							int   	=	'?'
																			# This is the value that will get picked if we do --field_name (without value)
																			_lowercase :							Any   	=	True
											elif isclass(lowerCamelCase) and issubclass(lowerCamelCase,   lowerCamelCase):
															_lowercase :							List[Any]   	=	field.type.__args__[0]
															_lowercase :							Optional[Any]   	=	'+'
															if field.default_factory is not dataclasses.MISSING:
																			_lowercase :							Union[str, Any]   	=	field.default_factory()
															elif field.default is dataclasses.MISSING:
																			_lowercase :							Optional[Any]   	=	True
											else:
															_lowercase :							Any   	=	field.type
															if field.default is not dataclasses.MISSING:
																			_lowercase :							Optional[int]   	=	field.default
															elif field.default_factory is not dataclasses.MISSING:
																			_lowercase :							Optional[Any]   	=	field.default_factory()
															else:
																			_lowercase :							int   	=	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]):
															_lowercase :							Union[str, Any]   	=	False
															parser.add_argument(F'''--no_{field.name}''',   action='store_false',   dest=field.name,   **lowerCamelCase)
							def     UpperCamelCase  (      self,   lowerCamelCase) ->     Optional[int]:




											"""simple docstring"""

											if hasattr(lowerCamelCase,   '_argument_group_name'):
															_lowercase :							List[str]   	=	self.add_argument_group(dtype._argument_group_name)
											else:
															_lowercase :							Union[str, Any]   	=	self

											try:
															_lowercase :							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):
																			_lowercase :							Tuple   	=	'.'.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
															_lowercase :							Any   	=	type_hints[field.name]
															self._parse_dataclass_field(lowerCamelCase,   lowerCamelCase)
							def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=False,   lowerCamelCase=True,   lowerCamelCase=None,   lowerCamelCase=None,   ) ->     Tuple[DataClass, ...]:




											"""simple docstring"""

											if args_file_flag or args_filename or (look_for_args_file and len(sys.argv)):
															_lowercase :							Any   	=	[]

															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
																			_lowercase :							List[str]   	=	ArgumentParser()
																			args_file_parser.add_argument(lowerCamelCase,   type=lowerCamelCase,   action='append')

																			# Use only remaining args for further parsing (remove the args_file_flag)
																			_lowercase :							Optional[Any]   	=	args_file_parser.parse_known_args(args=lowerCamelCase)
																			_lowercase :							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])

															_lowercase :							List[str]   	=	[]
															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
															_lowercase :							Any   	=	file_args + args if args is not None else file_args + sys.argv[1:]
											_lowercase :							Dict   	=	self.parse_known_args(args=lowerCamelCase)
											_lowercase :							Union[str, Any]   	=	[]
											for dtype in self.dataclass_types:
															_lowercase :							Tuple   	=	{f.name for f in dataclasses.fields(lowerCamelCase) if f.init}
															_lowercase :							Union[str, Any]   	=	{k: v for k, v in vars(lowerCamelCase).items() if k in keys}
															for k in keys:
																			delattr(lowerCamelCase,   lowerCamelCase)
															_lowercase :							Optional[int]   	=	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) ->     Tuple[DataClass, ...]:




											"""simple docstring"""

											_lowercase :							List[str]   	=	set(args.keys())
											_lowercase :							Union[str, Any]   	=	[]
											for dtype in self.dataclass_types:
															_lowercase :							Optional[int]   	=	{f.name for f in dataclasses.fields(lowerCamelCase) if f.init}
															_lowercase :							int   	=	{k: v for k, v in args.items() if k in keys}
															unused_keys.difference_update(inputs.keys())
															_lowercase :							Dict   	=	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) ->     Tuple[DataClass, ...]:




											"""simple docstring"""

											with open(Path(lowerCamelCase),   encoding='utf-8') as open_json_file:
															_lowercase :							str   	=	json.loads(open_json_file.read())
											_lowercase :							List[Any]   	=	self.parse_dict(lowerCamelCase,   allow_extra_keys=lowerCamelCase)
											return tuple(lowerCamelCase)




							def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = False) ->     Tuple[DataClass, ...]:




											"""simple docstring"""

											_lowercase :							str   	=	self.parse_dict(yaml.safe_load(Path(lowerCamelCase).read_text()),   allow_extra_keys=lowerCamelCase)
											return tuple(lowerCamelCase)






from jiwer import compute_measures

import datasets


SCREAMING_SNAKE_CASE :   Tuple							      =				"\\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"

SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (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 words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

SCREAMING_SNAKE_CASE :   Any							      =				"\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word 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    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION     )
class _lowerCamelCase(		datasets.Metric     ):
       def     UpperCamelCase  (      self) ->     Optional[Any]:




           """simple docstring"""

           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',
               ],   )




       def     UpperCamelCase  (      self,   lowerCamelCase=None,   lowerCamelCase=None,   lowerCamelCase=False) ->     Optional[Any]:




           """simple docstring"""

           if concatenate_texts:
               return compute_measures(lowerCamelCase,   lowerCamelCase)["wer"]
           else:
               _lowercase :							Dict   	=	0
               _lowercase :							Optional[int]   	=	0
               for prediction, reference in zip(lowerCamelCase,   lowerCamelCase):
                   _lowercase :							List[Any]   	=	compute_measures(lowerCamelCase,   lowerCamelCase)
                   incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                   total += measures["substitutions"] + measures["deletions"] + measures["hits"]
               return incorrect / total


