






'''simple docstring'''

import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer



def 							snake_case					(     a_							: Any    ,   a_							: Union[str, Any]    ,   a_							: List[Any]    ,   a_							: Any    ,   a_							: Tuple=True    ,   a_							: List[Any]="pt"	):





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							{"""add_prefix_space""": True} if isinstance(a_    ,   a_	) and not line.startswith(""" """	) else {}
      UpperCamelCase_				:   Optional[int]					    =							padding_side
      return tokenizer(
          [line]    ,   max_length=a_    ,   padding="""max_length""" if pad_to_max_length else None    ,   truncation=a_    ,   return_tensors=a_    ,   add_special_tokens=a_    ,   **a_    ,   )



def 							snake_case					(     a_							: Optional[int]    ,   a_							: Dict    ,   a_							: Any=None    ,   ):





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							input_ids.ne(a_	).any(dim=0	)
      if attention_mask is None:
            return input_ids[:, keep_column_mask]
      else:
            return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])




class     A    (      lowercase_						):






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase="train"			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None			,     __lowerCAmelCase=""			,     ):
       super().__init__()
       UpperCamelCase_				:   str					    =							Path(UpperCamelCase__ ).joinpath(type_path + """.source""" )
       UpperCamelCase_				:   Optional[int]					    =							Path(UpperCamelCase__ ).joinpath(type_path + """.target""" )
       UpperCamelCase_				:   str					    =							self.get_char_lens(self.src_file )
       UpperCamelCase_				:   List[str]					    =							max_source_length
       UpperCamelCase_				:   Optional[int]					    =							max_target_length
       assert min(self.src_lens ) > 0, F"found empty line in {self.src_file}"
       UpperCamelCase_				:   List[Any]					    =							tokenizer
       UpperCamelCase_				:   Tuple					    =							prefix
       if n_obs is not None:
             UpperCamelCase_				:   Tuple					    =							self.src_lens[:n_obs]
       UpperCamelCase_				:   Any					    =							src_lang
       UpperCamelCase_				:   Union[str, Any]					    =							tgt_lang
 def __len__(							self ):
       return len(self.src_lens )
 def __getitem__(							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							index + 1  # linecache starts at 1
       UpperCamelCase_				:   Optional[int]					    =							self.prefix + linecache.getline(str(self.src_file )			,     UpperCamelCase__ ).rstrip("""\n""" )
       UpperCamelCase_				:   List[Any]					    =							linecache.getline(str(self.tgt_file )			,     UpperCamelCase__ ).rstrip("""\n""" )
       assert source_line, F"empty source line for index {index}"
       assert tgt_line, F"empty tgt line for index {index}"

       # Need to add eos token manually for T5
       if isinstance(self.tokenizer			,     UpperCamelCase__ ):
             source_line += self.tokenizer.eos_token
             tgt_line += self.tokenizer.eos_token

       # Pad source and target to the right
       UpperCamelCase_				:   Dict					    =							(
           self.tokenizer.question_encoder if isinstance(self.tokenizer			,     UpperCamelCase__ ) else self.tokenizer
       )
       UpperCamelCase_				:   Union[str, Any]					    =							self.tokenizer.generator if isinstance(self.tokenizer			,     UpperCamelCase__ ) else self.tokenizer

       UpperCamelCase_				:   Tuple					    =							encode_line(UpperCamelCase__			,     UpperCamelCase__			,     self.max_source_length			,     """right""" )
       UpperCamelCase_				:   Optional[Any]					    =							encode_line(UpperCamelCase__			,     UpperCamelCase__			,     self.max_target_length			,     """right""" )

       UpperCamelCase_				:   List[str]					    =							source_inputs["""input_ids"""].squeeze()
       UpperCamelCase_				:   Any					    =							target_inputs["""input_ids"""].squeeze()
       UpperCamelCase_				:   str					    =							source_inputs["""attention_mask"""].squeeze()
       return {
           "input_ids": source_ids,
           "attention_mask": src_mask,
           "decoder_input_ids": target_ids,
       }
 @staticmethod
 def   _UpperCAmelCase       (							__lowerCAmelCase ):
       return [len(UpperCamelCase__ ) for x in Path(UpperCamelCase__ ).open().readlines()]





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							torch.stack([x["""input_ids"""] for x in batch] )
       UpperCamelCase_				:   Tuple					    =							torch.stack([x["""attention_mask"""] for x in batch] )
       UpperCamelCase_				:   Dict					    =							torch.stack([x["""decoder_input_ids"""] for x in batch] )
       UpperCamelCase_				:   List[Any]					    =							(
           self.tokenizer.generator.pad_token_id
           if isinstance(self.tokenizer			,     UpperCamelCase__ )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase_				:   Optional[int]					    =							(
           self.tokenizer.question_encoder.pad_token_id
           if isinstance(self.tokenizer			,     UpperCamelCase__ )
           else self.tokenizer.pad_token_id
       )
       UpperCamelCase_				:   str					    =							trim_batch(UpperCamelCase__			,     UpperCamelCase__ )
       UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							trim_batch(UpperCamelCase__			,     UpperCamelCase__			,     attention_mask=UpperCamelCase__ )
       UpperCamelCase_				:   Optional[Any]					    =							{
           """input_ids""": source_ids,
           """attention_mask""": source_mask,
           """decoder_input_ids""": y,
       }
       return batch






UpperCamelCase							=getLogger(__name__)



def 							snake_case					(     a_							: List[List]	):





      """simple docstring"""

      return list(itertools.chain.from_iterable(a_	)	)



def 							snake_case					(     a_							: str	):





      """simple docstring"""

      UpperCamelCase_				:   Tuple					    =							get_git_info()
      save_json(a_    ,   os.path.join(a_    ,   """git_log.json"""	)	)



def 							snake_case					(     a_							: int    ,   a_							: List[Any]    ,   a_							: Optional[int]=4    ,   **a_							: int	):





      """simple docstring"""

      with open(a_    ,   """w"""	) as f:
            json.dump(a_    ,   a_    ,   indent=a_    ,   **a_	)



def 							snake_case					(     a_							: List[str]	):





      """simple docstring"""

      with open(a_	) as f:
            return json.load(a_	)



def 							snake_case					(     ):





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							git.Repo(search_parent_directories=a_	)
      UpperCamelCase_				:   str					    =							{
          """repo_id""": str(a_	),
          """repo_sha""": str(repo.head.object.hexsha	),
          """repo_branch""": str(repo.active_branch	),
          """hostname""": str(socket.gethostname()	),
      }
      return repo_infos



def 							snake_case					(     a_							: Callable    ,   a_							: Iterable	):





      """simple docstring"""

      return list(map(a_    ,   a_	)	)



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: Dict	):





      """simple docstring"""

      with open(a_    ,   """wb"""	) as f:
            return pickle.dump(a_    ,   a_	)



def 							snake_case					(     a_							: Any	):





      """simple docstring"""

      def remove_articles(a_							: List[str]	):
            return re.sub(r"""\b(a|an|the)\b"""    ,   """ """    ,   a_	)

      def white_space_fix(a_							: Union[str, Any]	):
            return " ".join(text.split()	)

      def remove_punc(a_							: List[Any]	):
            UpperCamelCase_				:   str					    =							set(string.punctuation	)
            return "".join(ch for ch in text if ch not in exclude	)

      def lower(a_							: Optional[Any]	):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(a_	)	)	)	)



def 							snake_case					(     a_							: List[str]    ,   a_							: Optional[int]	):





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							normalize_answer(a_	).split()
      UpperCamelCase_				:   Any					    =							normalize_answer(a_	).split()
      UpperCamelCase_				:   Optional[Any]					    =							Counter(a_	) & Counter(a_	)
      UpperCamelCase_				:   Optional[Any]					    =							sum(common.values()	)
      if num_same == 0:
            return 0
      UpperCamelCase_				:   str					    =							1.0 * num_same / len(a_	)
      UpperCamelCase_				:   Optional[Any]					    =							1.0 * num_same / len(a_	)
      UpperCamelCase_				:   List[Any]					    =							(2 * precision * recall) / (precision + recall)
      return fa



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: Union[str, Any]	):





      """simple docstring"""

      return normalize_answer(a_	) == normalize_answer(a_	)



def 							snake_case					(     a_							: List[str]    ,   a_							: List[str]	):





      """simple docstring"""

      assert len(a_	) == len(a_	)
      UpperCamelCase_				:   List[Any]					    =							0
      for hypo, pred in zip(a_    ,   a_	):
            em += exact_match_score(a_    ,   a_	)
      if len(a_	) > 0:
            em /= len(a_	)
      return {"em": em}



def 							snake_case					(     a_							: Tuple	):





      """simple docstring"""

      return model_prefix.startswith("""rag"""	)



def 							snake_case					(     a_							: int    ,   a_							: Any    ,   a_							: Union[str, Any]	):





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							{p: p for p in extra_params}
      # T5 models don't have `dropout` param, they have `dropout_rate` instead
      UpperCamelCase_				:   Optional[Any]					    =							"""dropout_rate"""
      for p in extra_params:
            if getattr(a_    ,   a_    ,   a_	):
                  if not hasattr(a_    ,   a_	) and not hasattr(a_    ,   equivalent_param[p]	):
                        logger.info("""config doesn't have a `{}` attribute""".format(a_	)	)
                        delattr(a_    ,   a_	)
                        continue
                  UpperCamelCase_				:   Union[str, Any]					    =							p if hasattr(a_    ,   a_	) else equivalent_param[p]
                  setattr(a_    ,   a_    ,   getattr(a_    ,   a_	)	)
                  delattr(a_    ,   a_	)
      return hparams, config














'''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






