

"""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


SCREAMING_SNAKE_CASE_     :			str	  =			''''''


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


     class 		_A       (			tr.AbstractTransform					):





           def __init__(  self    ,						SCREAMING_SNAKE_CASE__ = " "		)   ->					List[str]:
              lowerCamelCase__						=			sentence_delimiter





           def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					str:
              return list(SCREAMING_SNAKE_CASE__		)





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






     SCREAMING_SNAKE_CASE_     :			int	  =			tr.Compose(
         [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
     )
else:
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			tr.Compose(
         [
             tr.RemoveMultipleSpaces(),
             tr.Strip(),
             tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
             tr.ReduceToListOfListOfChars(),
         ]
     )


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			'''\
@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.}
}
'''

SCREAMING_SNAKE_CASE_     :			Tuple	  =			'''\
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.
'''

SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			'''
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					):





      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         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 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False		)   ->					int:
         if concatenate_texts:
            return jiwer.compute_measures(
                SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						truth_transform=SCREAMING_SNAKE_CASE__    ,						hypothesis_transform=SCREAMING_SNAKE_CASE__    ,						)["wer"]

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

         return incorrect / total







"""simple docstring"""




import operator as op



def 	UpperCAmelCase__   (				A__   )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			lambda A__	,    A__   : int(x / y   )  # noqa: E731 integer division operation
   lowerCamelCase__						=			{
       "^": op.pow,
       "*": op.mul,
       "/": div,
       "+": op.add,
       "-": op.sub,
   }  # operators & their respective operation

   # print table header
   print("Symbol".center(8   )	,    "Action".center(12   )	,    "Stack"	,    sep=" | "   )
   print("-" * (30 + len(A__   ))   )

   for x in post_fix:
      if x.isdigit():  # if x in digit
         stack.append(A__   )  # append x to stack
         # output in tabular format
         print(x.rjust(8   )	,    ("push(" + x + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )
      else:
         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         lowerCamelCase__						=			stack.pop()  # pop stack
         # output in tabular format
         print("".rjust(8   )	,    ("pop(" + a + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "   )

         stack.append(
             str(opr[x](int(A__   )	,    int(A__   )   )   )   )  # evaluate the 2 values popped from stack & push result to stack
         # output in tabular format
         print(
             x.rjust(8   )	,    ("push(" + a + x + b + ")").ljust(12   )	,    ",".join(A__   )	,    sep=" | "	,    )

   return int(stack[0]   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			input('''\n\nEnter a Postfix Equation (space separated) = ''').split(''' ''')
     print('''\n\tResult = ''', solve(Postfix))




