



"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


A:   str								=		logging.get_logger(__name__)

A:   str								=		"▁"

A:   Union[str, Any]								=		{"vocab_file": "sentencepiece.bpe.model"}

A:   Union[str, Any]								=		{
    "vocab_file": {
        "facebook/nllb-200-distilled-600M": (
            "https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"
        ),
    }
}

A:   Dict								=		{
    "facebook/nllb-200-distilled-600M": 1_0_2_4,
}

# fmt: off
A:   List[Any]								=		["ace_Arab", "ace_Latn", "acm_Arab", "acq_Arab", "aeb_Arab", "afr_Latn", "ajp_Arab", "aka_Latn", "amh_Ethi", "apc_Arab", "arb_Arab", "ars_Arab", "ary_Arab", "arz_Arab", "asm_Beng", "ast_Latn", "awa_Deva", "ayr_Latn", "azb_Arab", "azj_Latn", "bak_Cyrl", "bam_Latn", "ban_Latn", "bel_Cyrl", "bem_Latn", "ben_Beng", "bho_Deva", "bjn_Arab", "bjn_Latn", "bod_Tibt", "bos_Latn", "bug_Latn", "bul_Cyrl", "cat_Latn", "ceb_Latn", "ces_Latn", "cjk_Latn", "ckb_Arab", "crh_Latn", "cym_Latn", "dan_Latn", "deu_Latn", "dik_Latn", "dyu_Latn", "dzo_Tibt", "ell_Grek", "eng_Latn", "epo_Latn", "est_Latn", "eus_Latn", "ewe_Latn", "fao_Latn", "pes_Arab", "fij_Latn", "fin_Latn", "fon_Latn", "fra_Latn", "fur_Latn", "fuv_Latn", "gla_Latn", "gle_Latn", "glg_Latn", "grn_Latn", "guj_Gujr", "hat_Latn", "hau_Latn", "heb_Hebr", "hin_Deva", "hne_Deva", "hrv_Latn", "hun_Latn", "hye_Armn", "ibo_Latn", "ilo_Latn", "ind_Latn", "isl_Latn", "ita_Latn", "jav_Latn", "jpn_Jpan", "kab_Latn", "kac_Latn", "kam_Latn", "kan_Knda", "kas_Arab", "kas_Deva", "kat_Geor", "knc_Arab", "knc_Latn", "kaz_Cyrl", "kbp_Latn", "kea_Latn", "khm_Khmr", "kik_Latn", "kin_Latn", "kir_Cyrl", "kmb_Latn", "kon_Latn", "kor_Hang", "kmr_Latn", "lao_Laoo", "lvs_Latn", "lij_Latn", "lim_Latn", "lin_Latn", "lit_Latn", "lmo_Latn", "ltg_Latn", "ltz_Latn", "lua_Latn", "lug_Latn", "luo_Latn", "lus_Latn", "mag_Deva", "mai_Deva", "mal_Mlym", "mar_Deva", "min_Latn", "mkd_Cyrl", "plt_Latn", "mlt_Latn", "mni_Beng", "khk_Cyrl", "mos_Latn", "mri_Latn", "zsm_Latn", "mya_Mymr", "nld_Latn", "nno_Latn", "nob_Latn", "npi_Deva", "nso_Latn", "nus_Latn", "nya_Latn", "oci_Latn", "gaz_Latn", "ory_Orya", "pag_Latn", "pan_Guru", "pap_Latn", "pol_Latn", "por_Latn", "prs_Arab", "pbt_Arab", "quy_Latn", "ron_Latn", "run_Latn", "rus_Cyrl", "sag_Latn", "san_Deva", "sat_Beng", "scn_Latn", "shn_Mymr", "sin_Sinh", "slk_Latn", "slv_Latn", "smo_Latn", "sna_Latn", "snd_Arab", "som_Latn", "sot_Latn", "spa_Latn", "als_Latn", "srd_Latn", "srp_Cyrl", "ssw_Latn", "sun_Latn", "swe_Latn", "swh_Latn", "szl_Latn", "tam_Taml", "tat_Cyrl", "tel_Telu", "tgk_Cyrl", "tgl_Latn", "tha_Thai", "tir_Ethi", "taq_Latn", "taq_Tfng", "tpi_Latn", "tsn_Latn", "tso_Latn", "tuk_Latn", "tum_Latn", "tur_Latn", "twi_Latn", "tzm_Tfng", "uig_Arab", "ukr_Cyrl", "umb_Latn", "urd_Arab", "uzn_Latn", "vec_Latn", "vie_Latn", "war_Latn", "wol_Latn", "xho_Latn", "ydd_Hebr", "yor_Latn", "yue_Hant", "zho_Hans", "zho_Hant", "zul_Latn"]



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   str						=						VOCAB_FILES_NAMES
   __lowerCAmelCase  :   List[Any]						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase  :   Union[str, Any]						=						PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase  :   int						=						['input_ids', 'attention_mask']

   __lowerCAmelCase  :   List[int]						=						[]
   __lowerCAmelCase  :   List[int]						=						[]

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="</s>"      ,     _SCREAMING_SNAKE_CASE="<s>"      ,     _SCREAMING_SNAKE_CASE="<unk>"      ,     _SCREAMING_SNAKE_CASE="<pad>"      ,     _SCREAMING_SNAKE_CASE="<mask>"      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE=None      ,     _SCREAMING_SNAKE_CASE=False      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	AddedToken(_SCREAMING_SNAKE_CASE      ,     lstrip=_SCREAMING_SNAKE_CASE      ,     rstrip=_SCREAMING_SNAKE_CASE						) if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						) else mask_token

    UpperCAmelCase	:					List[Any]	=	{} if sp_model_kwargs is None else sp_model_kwargs
    UpperCAmelCase	:					Any	=	legacy_behaviour

    super().__init__(
        bos_token=_SCREAMING_SNAKE_CASE      ,     eos_token=_SCREAMING_SNAKE_CASE      ,     unk_token=_SCREAMING_SNAKE_CASE      ,     sep_token=_SCREAMING_SNAKE_CASE      ,     cls_token=_SCREAMING_SNAKE_CASE      ,     pad_token=_SCREAMING_SNAKE_CASE      ,     mask_token=_SCREAMING_SNAKE_CASE      ,     tokenizer_file=_SCREAMING_SNAKE_CASE      ,     src_lang=_SCREAMING_SNAKE_CASE      ,     tgt_lang=_SCREAMING_SNAKE_CASE      ,     additional_special_tokens=_SCREAMING_SNAKE_CASE      ,     sp_model_kwargs=self.sp_model_kwargs      ,     legacy_behaviour=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )

    UpperCAmelCase	:					Tuple	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.Load(str(_SCREAMING_SNAKE_CASE						)						)
    UpperCAmelCase	:					List[str]	=	vocab_file
    # Original fairseq vocab and spm vocab must be "aligned":
    # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
    # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
    # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
    # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

    # Mimic fairseq token-to-id alignment for the first 4 token
    UpperCAmelCase	:					Dict	=	{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

    # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
    UpperCAmelCase	:					str	=	1

    UpperCAmelCase	:					Optional[Any]	=	len(self.sp_model						)
    UpperCAmelCase	:					Dict	=	{
        code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_SCREAMING_SNAKE_CASE						)
    }
    UpperCAmelCase	:					Any	=	{v: k for k, v in self.lang_code_to_id.items()}
    UpperCAmelCase	:					Any	=	len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset

    self.fairseq_tokens_to_ids.update(self.lang_code_to_id						)
    UpperCAmelCase	:					Any	=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
    UpperCAmelCase	:					Optional[int]	=	list(self.lang_code_to_id.keys()						)

    if additional_special_tokens is not None:
     # Only add those special tokens if they are not already there.
     self._additional_special_tokens.extend(
         [t for t in additional_special_tokens if t not in self._additional_special_tokens]						)

    UpperCAmelCase	:					List[str]	=	src_lang if src_lang is not None else """eng_Latn"""
    UpperCAmelCase	:					str	=	self.lang_code_to_id[self._src_lang]
    UpperCAmelCase	:					Optional[int]	=	tgt_lang
    self.set_src_lang_special_tokens(self._src_lang						)

   def __getstate__(   self						)  ->      Optional[int]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	self.__dict__.copy()
    UpperCAmelCase	:					Optional[Any]	=	None
    UpperCAmelCase	:					Tuple	=	self.sp_model.serialized_model_proto()
    return state

   def __setstate__(   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	d

    # for backward compatibility
    if not hasattr(self      ,     """sp_model_kwargs"""						):
     UpperCAmelCase	:					Union[str, Any]	=	{}

    UpperCAmelCase	:					Union[str, Any]	=	spm.SentencePieceProcessor(**self.sp_model_kwargs						)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Tuple:





    '''simple docstring'''




    return len(self.sp_model						) + len(self.lang_code_to_id						) + self.fairseq_offset + 1  # Plus 1 for the mask token

   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      str:





    '''simple docstring'''




    return self._src_lang

   @src_lang.setter
   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Tuple	=	new_src_lang
    self.set_src_lang_special_tokens(self._src_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = False						)  ->      List[int]:





    '''simple docstring'''




    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=_SCREAMING_SNAKE_CASE      ,     token_ids_a=_SCREAMING_SNAKE_CASE      ,     already_has_special_tokens=_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					List[str]	=	[1] * len(self.prefix_tokens						)
    UpperCAmelCase	:					str	=	[1] * len(self.suffix_tokens						)
    if token_ids_a is None:
     return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE						)) + suffix_ones
    return prefix_ones + ([0] * len(_SCREAMING_SNAKE_CASE						)) + ([0] * len(_SCREAMING_SNAKE_CASE						)) + suffix_ones

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    if token_ids_a is None:
     return self.prefix_tokens + token_ids_a + self.suffix_tokens
    # We don't expect to process pairs, but leave the pair logic for API consistency
    return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      List[int]:





    '''simple docstring'''




    UpperCAmelCase	:					Union[str, Any]	=	[self.sep_token_id]
    UpperCAmelCase	:					Optional[int]	=	[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep						) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    if src_lang is None or tgt_lang is None:
     raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
    UpperCAmelCase	:					Any	=	src_lang
    UpperCAmelCase	:					Dict	=	self(_SCREAMING_SNAKE_CASE      ,     add_special_tokens=_SCREAMING_SNAKE_CASE      ,     return_tensors=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					str	=	self.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					List[Any]	=	tgt_lang_id
    return inputs

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      int:





    '''simple docstring'''




    UpperCAmelCase	:					Any	=	{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE						): i for i in range(self.vocab_size						)}
    vocab.update(self.added_tokens_encoder						)
    return vocab

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    return self.sp_model.encode(_SCREAMING_SNAKE_CASE      ,     out_type=_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    UpperCAmelCase	:					Optional[int]	=	self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE						)

    # Need to return unknown token if the SP model returned 0
    return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Union[str, Any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	"""""".join(_SCREAMING_SNAKE_CASE						).replace(_SCREAMING_SNAKE_CASE      ,     """ """						).strip()
    return out_string

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = None						)  ->      Tuple[str]:





    '''simple docstring'''




    if not os.path.isdir(_SCREAMING_SNAKE_CASE						):
     logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
     return
    UpperCAmelCase	:					int	=	os.path.join(
        _SCREAMING_SNAKE_CASE      ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

    if os.path.abspath(self.vocab_file						) != os.path.abspath(_SCREAMING_SNAKE_CASE						) and os.path.isfile(self.vocab_file						):
     copyfile(self.vocab_file      ,     _SCREAMING_SNAKE_CASE						)
    elif not os.path.isfile(self.vocab_file						):
     with open(_SCREAMING_SNAKE_CASE      ,     """wb"""						) as fi:
      UpperCAmelCase	:					Any	=	self.sp_model.serialized_model_proto()
      fi.write(_SCREAMING_SNAKE_CASE						)

    return (out_vocab_file,)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE = "eng_Latn"      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "fra_Latn"      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      BatchEncoding:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	src_lang
    UpperCAmelCase	:					Tuple	=	tgt_lang
    return super().prepare_seqaseq_batch(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict:





    '''simple docstring'''




    return self.set_src_lang_special_tokens(self.src_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      List[Any]:





    '''simple docstring'''




    return self.set_tgt_lang_special_tokens(self.tgt_lang						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					Optional[Any]	=	self.lang_code_to_id[src_lang]
    if self.legacy_behaviour:
     UpperCAmelCase	:					Optional[int]	=	[]
     UpperCAmelCase	:					List[Any]	=	[self.eos_token_id, self.cur_lang_code]
    else:
     UpperCAmelCase	:					List[Any]	=	[self.cur_lang_code]
     UpperCAmelCase	:					Optional[Any]	=	[self.eos_token_id]




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      None:





    '''simple docstring'''




    UpperCAmelCase	:					List[str]	=	self.lang_code_to_id[lang]
    if self.legacy_behaviour:
     UpperCAmelCase	:					Any	=	[]
     UpperCAmelCase	:					List[Any]	=	[self.eos_token_id, self.cur_lang_code]
    else:
     UpperCAmelCase	:					Tuple	=	[self.cur_lang_code]
     UpperCAmelCase	:					Optional[int]	=	[self.eos_token_id]





"""simple docstring"""
import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline

def       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 UpperCAmelCase	:					List[str]	=	["""\n\n""", """\n""", """   """, """  """]

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

 # load eval pipeline
 if args.device is None:
  UpperCAmelCase	:					Optional[int]	=	0 if torch.cuda.is_available() else -1
 UpperCAmelCase	:					Tuple	=	pipeline("""automatic-speech-recognition"""			,		model=args.model_id			,		device=args.device						)

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

 # compute and log_results
 # do not change function below
 log_results(UpperCamelCase			,		UpperCamelCase						)


if __name__ == "__main__":
      A:   List[Any]								=		argparse.ArgumentParser()

      parser.add_argument(
          "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
      )
      parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
      )
      parser.add_argument(
          "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
      )
      parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
      parser.add_argument(
          "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
      )
      parser.add_argument(
          "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
      )
      parser.add_argument(
          "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
      )
      parser.add_argument(
          "--device",
          type=int,
          default=None,
          help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)
