

"""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						       =      logging.get_logger(__name__)

__A						       =      "▁"

__A						       =      {"vocab_file": "sentencepiece.bpe.model"}

__A						       =      {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

__A						       =      {
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__A						       =      ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				str       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				str       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				Union[str, Any]       				=  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  ["""input_ids""", """attention_mask"""]

				SCREAMING_SNAKE_CASE_       :				Any       				=  []
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  []

				def __init__(     self							:    str					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int="<s>"					,  UpperCamelCase__							:    str="</s>"					,  UpperCamelCase__							:    str="</s>"					,  UpperCamelCase__							:    int="<s>"					,  UpperCamelCase__							:    Tuple="<unk>"					,  UpperCamelCase__							:    int="<pad>"					,  UpperCamelCase__							:    int="<mask>"					,  UpperCamelCase__							:    List[str]=None					,  UpperCamelCase__							:    List[Any]=None					,  UpperCamelCase__							:    str=None					,  UpperCamelCase__							:    List[str] = None					,  UpperCamelCase__							:    str=None					,  **UpperCamelCase__							:    int					,  )->       int:



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      AddedToken(__a					,  lstrip=__a					,  rstrip=__a) if isinstance(__a					,  __a) else mask_token

						__lowerCAmelCase:	Optional[int]   				=      {} if sp_model_kwargs is None else sp_model_kwargs

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

						__lowerCAmelCase:	Optional[int]   				=      spm.SentencePieceProcessor(**self.sp_model_kwargs)
						self.sp_model.Load(str(__a))
						__lowerCAmelCase:	Tuple   				=      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>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
						# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

						# Mimic fairseq token-to-id alignment for the first 4 token
						__lowerCAmelCase:	Optional[int]   				=      {"<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
						__lowerCAmelCase:	Tuple   				=      1

						__lowerCAmelCase:	int   				=      len(self.sp_model)
						__lowerCAmelCase:	Optional[int]   				=      {
						    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a)
						}
						__lowerCAmelCase:	List[str]   				=      {v: k for k, v in self.lang_code_to_id.items()}
						__lowerCAmelCase:	str   				=      len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset

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

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

				def __getstate__(     self							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      d

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

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

				@property
				def 							lowercase_					(     self							:    Tuple)->       List[Any]:



						'''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 							lowercase_					(     self							:    Any)->       str:



						'''simple docstring'''


						return self._src_lang

				@src_lang.setter
				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Any)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      new_src_lang
						self.set_src_lang_special_tokens(self._src_lang)

				def 							lowercase_					(     self							:    List[str]					,  UpperCamelCase__							:    Dict					,  UpperCamelCase__							:    Optional[Any] = None					,  UpperCamelCase__							:    List[str] = False)->       List[int]:



						'''simple docstring'''


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

						__lowerCAmelCase:	Union[str, Any]   				=      [1] * len(self.prefix_tokens)
						__lowerCAmelCase:	int   				=      [1] * len(self.suffix_tokens)
						if token_ids_a is None:
								return prefix_ones + ([0] * len(__a)) + suffix_ones
						return prefix_ones + ([0] * len(__a)) + ([0] * len(__a)) + suffix_ones

				def 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Optional[Any]					,  UpperCamelCase__							:    int = 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 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Any = None)->       List[int]:



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      [self.sep_token_id]
						__lowerCAmelCase:	Any   				=      [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 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    int					,  UpperCamelCase__							:    List[str]					,  **UpperCamelCase__							:    List[str])->       str:



						'''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")
						__lowerCAmelCase:	List[Any]   				=      src_lang
						__lowerCAmelCase:	Any   				=      self(__a					,  add_special_tokens=__a					,  return_tensors=__a					,  **__a)
						__lowerCAmelCase:	int   				=      self.convert_tokens_to_ids(__a)
						__lowerCAmelCase:	str   				=      tgt_lang_id
						return inputs

				def 							lowercase_					(     self							:    Any)->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      {self.convert_ids_to_tokens(__a): i for i in range(self.vocab_size)}
						vocab.update(self.added_tokens_encoder)
						return vocab

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    Tuple)->       List[str]:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    List[Any])->       int:



						'''simple docstring'''


						if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
						__lowerCAmelCase:	Optional[Any]   				=      self.sp_model.PieceToId(__a)

						# 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 							lowercase_					(     self							:    Any					,  UpperCamelCase__							:    int)->       List[str]:



						'''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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    Any)->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      "".join(__a).replace(__a					,  " ").strip()
						return out_string

				def 							lowercase_					(     self							:    Union[str, Any]					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Tuple = None)->       Tuple[str]:



						'''simple docstring'''


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

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

						return (out_vocab_file,)

				def 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Tuple					,  UpperCamelCase__							:    Tuple = "en_XX"					,  UpperCamelCase__							:    int = None					,  UpperCamelCase__							:    List[Any] = "ro_RO"					,  **UpperCamelCase__							:    Optional[int]					,  )->       BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:	Union[str, Any]   				=      src_lang
						__lowerCAmelCase:	int   				=      tgt_lang
						return super().prepare_seqaseq_batch(__a					,  __a					,  **__a)

				def 							lowercase_					(     self							:    List[Any])->       Optional[Any]:



						'''simple docstring'''


						return self.set_src_lang_special_tokens(self.src_lang)

				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						return self.set_tgt_lang_special_tokens(self.tgt_lang)

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Any)->       None:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      self.lang_code_to_id[src_lang]
						__lowerCAmelCase:	Optional[Any]   				=      []
						__lowerCAmelCase:	int   				=      [self.eos_token_id, self.cur_lang_code]




				def 							lowercase_					(     self							:    Tuple					,  UpperCamelCase__							:    List[Any])->       None:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      self.lang_code_to_id[lang]
						__lowerCAmelCase:	int   				=      []
						__lowerCAmelCase:	int   				=      [self.eos_token_id, self.cur_lang_code]









"""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    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	int   				=      args.log_outputs
		__lowerCAmelCase:	Tuple   				=      "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

		# load metric
		__lowerCAmelCase:	str   				=      load_metric("wer"			)
		__lowerCAmelCase:	int   				=      load_metric("cer"			)

		# compute metrics
		__lowerCAmelCase:	Optional[Any]   				=      wer.compute(references=result["target"] ,			predictions=result["prediction"]			)
		__lowerCAmelCase:	Dict   				=      cer.compute(references=result["target"] ,			predictions=result["prediction"]			)

		# print & log results
		__lowerCAmelCase:	Optional[int]   				=      F"WER: {wer_result}\nCER: {cer_result}"
		print(__SCREAMING_SNAKE_CASE			)

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

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

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

						result.map(__SCREAMING_SNAKE_CASE ,			with_indices=__SCREAMING_SNAKE_CASE			)






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

		__lowerCAmelCase:	List[Any]   				=      re.sub(__SCREAMING_SNAKE_CASE ,			"" ,			text.lower()			)

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

		for t in token_sequences_to_ignore:
				__lowerCAmelCase:	str   				=      " ".join(text.split(__SCREAMING_SNAKE_CASE			)			)

		return text






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		# load dataset
		__lowerCAmelCase:	List[str]   				=      load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=__SCREAMING_SNAKE_CASE			)

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

		# load processor
		__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(args.model_id			)
		__lowerCAmelCase:	str   				=      feature_extractor.sampling_rate

		# resample audio
		__lowerCAmelCase:	Tuple   				=      dataset.cast_column("audio" ,			Audio(sampling_rate=__SCREAMING_SNAKE_CASE			)			)

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

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

				__lowerCAmelCase:	Any   				=      prediction["text"]
				__lowerCAmelCase:	List[str]   				=      normalize_text(batch["sentence"]			)
				return batch

		# run inference on all examples
		__lowerCAmelCase:	str   				=      dataset.map(__SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
			__A						       =      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						       =      parser.parse_args()

			main(args)


