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


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		"▁"

SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{"vocab_file": "sentencepiece.bpe.model"}

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

SCREAMING_SNAKE_CASE :	Optional[Any]							     =		{
    "facebook/nllb-200-distilled-600M": 1024,
}

# fmt: off
SCREAMING_SNAKE_CASE :	Dict							     =		["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    A_ (       UpperCAmelCase_ ):

  '''simple docstring'''





  _SCREAMING_SNAKE_CASE    				=			VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE    				=			PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE    				=			['input_ids', 'attention_mask']

  _SCREAMING_SNAKE_CASE    				=			[]
  _SCREAMING_SNAKE_CASE    				=			[]

  def __init__(		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Any     ,							__SCREAMING_SNAKE_CASE :      str="<s>"     ,							__SCREAMING_SNAKE_CASE :      Dict="</s>"     ,							__SCREAMING_SNAKE_CASE :      List[str]="</s>"     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]="<s>"     ,							__SCREAMING_SNAKE_CASE :      List[str]="<unk>"     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="<pad>"     ,							__SCREAMING_SNAKE_CASE :      List[str]="<mask>"     ,							__SCREAMING_SNAKE_CASE :      Tuple=None     ,							__SCREAMING_SNAKE_CASE :      List[str]=None     ,							__SCREAMING_SNAKE_CASE :      Dict=None     ,							__SCREAMING_SNAKE_CASE :      List[str] = None     ,							__SCREAMING_SNAKE_CASE :      Any=None     ,							__SCREAMING_SNAKE_CASE :      int=False     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   # Mask token behave like a normal word, i.e. include the space before it
   __a       =  AddedToken(_lowercase     ,							lstrip=_lowercase     ,							rstrip=_lowercase       ) if isinstance(_lowercase     ,							_lowercase       ) else mask_token

   __a       =  {} if sp_model_kwargs is None else sp_model_kwargs
   __a       =  legacy_behaviour

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

   __a       =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.Load(str(_lowercase       )       )
   __a       =  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
   __a       =  {'<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
   __a       =  1

   __a       =  len(self.sp_model       )
   __a       =  {
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_lowercase       )
   }
   __a       =  {v: k for k, v in self.lang_code_to_id.items()}
   __a       =  len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset

   self.fairseq_tokens_to_ids.update(self.lang_code_to_id       )
   __a       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   __a       =  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]       )

   __a       =  src_lang if src_lang is not None else 'eng_Latn'
   __a       =  self.lang_code_to_id[self._src_lang]
   __a       =  tgt_lang
   self.set_src_lang_special_tokens(self._src_lang       )

  def __getstate__(		self :      str       ):
   __a       =  self.__dict__.copy()
   __a       =  None
   __a       =  self.sp_model.serialized_model_proto()
   return state

  def __setstate__(		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  d

   # for backward compatibility
   if not hasattr(self     ,							"sp_model_kwargs"       ):
    __a       =  {}

   __a       =  spm.SentencePieceProcessor(**self.sp_model_kwargs       )
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto       )

  @property
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return len(self.sp_model       ) + len(self.lang_code_to_id       ) + self.fairseq_offset + 1  # Plus 1 for the mask token

  @property
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return self._src_lang

  @src_lang.setter
  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
   __a       =  new_src_lang
   self.set_src_lang_special_tokens(self._src_lang       )

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int = None     ,							__SCREAMING_SNAKE_CASE :      List[str] = False       ):

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

   __a       =  [1] * len(self.prefix_tokens       )
   __a       =  [1] * len(self.suffix_tokens       )
   if token_ids_a is None:
    return prefix_ones + ([0] * len(_lowercase       )) + suffix_ones
   return prefix_ones + ([0] * len(_lowercase       )) + ([0] * len(_lowercase       )) + suffix_ones

  def 		_UpperCAmelCase   (		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int = None       ):
   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 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple = None       ):
   __a       =  [self.sep_token_id]
   __a       =  [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 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      Dict     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							**__SCREAMING_SNAKE_CASE :      Tuple       ):
   if src_lang is None or tgt_lang is None:
    raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"       )
   __a       =  src_lang
   __a       =  self(_lowercase     ,							add_special_tokens=_lowercase     ,							return_tensors=_lowercase     ,							**_lowercase       )
   __a       =  self.convert_tokens_to_ids(_lowercase       )
   __a       =  tgt_lang_id
   return inputs

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  {self.convert_ids_to_tokens(_lowercase       ): i for i in range(self.vocab_size       )}
   vocab.update(self.added_tokens_encoder       )
   return vocab

  def 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      Tuple       ):
   return self.sp_model.encode(_lowercase     ,							out_type=_lowercase       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      str       ):
   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   __a       =  self.sp_model.PieceToId(_lowercase       )

   # 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 		_UpperCAmelCase   (		self :      int     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   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 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   __a       =  ''.join(_lowercase       ).replace(_lowercase     ,							" "       ).strip()
   return out_string

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any] = None       ):
   if not os.path.isdir(_lowercase       ):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
    return
   __a       =  os.path.join(
       _lowercase     ,							(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]       )

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

   return (out_vocab_file,)

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int = "eng_Latn"     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      List[Any] = "fra_Latn"     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   __a       =  src_lang
   __a       =  tgt_lang
   return super().prepare_seqaseq_batch(_lowercase     ,							_lowercase     ,							**_lowercase       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return self.set_src_lang_special_tokens(self.src_lang       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return self.set_tgt_lang_special_tokens(self.tgt_lang       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   __a       =  self.lang_code_to_id[src_lang]
   if self.legacy_behaviour:
    __a       =  []
    __a       =  [self.eos_token_id, self.cur_lang_code]
   else:
    __a       =  [self.cur_lang_code]
    __a       =  [self.eos_token_id]

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  self.lang_code_to_id[lang]
   if self.legacy_behaviour:
    __a       =  []
    __a       =  [self.eos_token_id, self.cur_lang_code]
   else:
    __a       =  [self.cur_lang_code]
    __a       =  [self.eos_token_id]



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   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

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

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

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

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


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

 __a       =  re.sub(_A							,				""							,				text.lower()					)

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

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

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

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

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

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

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	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.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


