



'''simple docstring'''






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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__:     str										=    logging.get_logger(__name__)

UpperCamelCase__:     List[str]										=    {"vocab_file": "spm_char.model"}

UpperCamelCase__:     List[str]										=    {
    "vocab_file": {
        "microsoft/speecht5_asr": "https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model",
        "microsoft/speecht5_tts": "https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model",
        "microsoft/speecht5_vc": "https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model",
    }
}

UpperCamelCase__:     Tuple										=    {
    "microsoft/speecht5_asr": 1024,
    "microsoft/speecht5_tts": 1024,
    "microsoft/speecht5_vc": 1024,
}

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= ["""input_ids""", """attention_mask"""]







	def __init__(		self    :  Tuple	,    __snake_case    :  str	,    __snake_case    :  Tuple="<s>"	,    __snake_case    :  Union[str, Any]="</s>"	,    __snake_case    :  Dict="<unk>"	,    __snake_case    :  int="<pad>"	,    __snake_case    :  Optional[Dict[str, Any]] = None	,    **__snake_case    :  List[Any]	,    )    ->	None:
								UpperCAmelCase				: int											=	{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=__snake_case	,    eos_token=__snake_case	,    unk_token=__snake_case	,    pad_token=__snake_case	,    sp_model_kwargs=self.sp_model_kwargs	,    **__snake_case	,    )

								UpperCAmelCase				: Any											=	vocab_file

								UpperCAmelCase				: int											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(__snake_case	)







	@property
	def   A			(		self    :  int	)    ->	Optional[int]:
								return self.sp_model.get_piece_size()







	def   A			(		self    :  Any	)    ->	int:
								UpperCAmelCase				: Optional[int]											=	{self.convert_ids_to_tokens(__snake_case	): i for i in range(self.vocab_size	)}
								vocab.update(self.added_tokens_encoder	)
								return vocab







	def __getstate__(		self    :  List[str]	)    ->	str:
								UpperCAmelCase				: List[str]											=	self.__dict__.copy()
								UpperCAmelCase				: Tuple											=	None
								return state







	def __setstate__(		self    :  Dict	,    __snake_case    :  Optional[Any]	)    ->	Tuple:
								UpperCAmelCase				: Tuple											=	d

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

								UpperCAmelCase				: str											=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
								self.sp_model.Load(self.vocab_file	)







	def   A			(		self    :  Dict	,    __snake_case    :  str	)    ->	List[str]:
								return self.sp_model.encode(__snake_case	,    out_type=__snake_case	)







	def   A			(		self    :  Tuple	,    __snake_case    :  Optional[Any]	)    ->	List[str]:
								return self.sp_model.piece_to_id(__snake_case	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  Any	)    ->	Tuple:
								UpperCAmelCase				: Dict											=	self.sp_model.IdToPiece(__snake_case	)
								return token







	def   A			(		self    :  List[str]	,    __snake_case    :  Optional[int]	)    ->	Optional[Any]:
								UpperCAmelCase				: Any											=	[]
								UpperCAmelCase				: List[Any]											=	''''''
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(__snake_case	) + token
																						UpperCAmelCase				: Dict											=	[]
															else:
																						current_sub_tokens.append(__snake_case	)
								out_string += self.sp_model.decode(__snake_case	)
								return out_string.strip()







	def   A			(		self    :  Dict	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  Any=None	)    ->	List[int]:
								if token_ids_a is None:
															return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]







	def   A			(		self    :  Any	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	,    __snake_case    :  bool = False	)    ->	List[int]:
								if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=__snake_case	,    token_ids_a=__snake_case	,    already_has_special_tokens=__snake_case	)

								UpperCAmelCase				: Any											=	[1]
								if token_ids_a is None:
															return ([0] * len(__snake_case	)) + suffix_ones
								return ([0] * len(__snake_case	)) + ([0] * len(__snake_case	)) + suffix_ones







	def   A			(		self    :  Dict	,    __snake_case    :  str	,    __snake_case    :  Optional[str] = None	)    ->	Tuple[str]:
								if not os.path.isdir(__snake_case	):
															logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
															return
								UpperCAmelCase				: Union[str, Any]											=	os.path.join(
								    __snake_case	,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

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

								return (out_vocab_file,)










'''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_  (      _lowerCAmelCase						:		Dataset     ,   _lowerCAmelCase						:		Dict[str, str]						)   ->     str:
							UpperCAmelCase				: Optional[int]											=	args.log_outputs
							UpperCAmelCase				: Any											=	'''_'''.join(args.dataset.split('''/'''						) + [args.config, args.split]						)

							# load metric
							UpperCAmelCase				: Union[str, Any]											=	load_metric('''wer'''						)
							UpperCAmelCase				: Dict											=	load_metric('''cer'''						)

							# compute metrics
							UpperCAmelCase				: Any											=	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(_lowerCAmelCase						)

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

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														UpperCAmelCase				: Union[str, Any]											=	f"""log_{dataset_id}_predictions.txt"""
														UpperCAmelCase				: Optional[Any]											=	f"""log_{dataset_id}_targets.txt"""

														with open(_lowerCAmelCase     ,   '''w'''						) as p, open(_lowerCAmelCase     ,   '''w'''						) as t:
																					# mapping function to write output
																					def write_to_file(_lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		str						):
																												p.write(f"""{i}""" + '''\n'''						)
																												p.write(batch['''prediction'''] + '''\n'''						)
																												t.write(f"""{i}""" + '''\n'''						)
																												t.write(batch['''target'''] + '''\n'''						)

																					result.map(_lowerCAmelCase     ,   with_indices=_lowerCAmelCase						)


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

							UpperCAmelCase				: List[Any]											=	re.sub(_lowerCAmelCase     ,   ''''''     ,   text.lower()						)

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

							for t in token_sequences_to_ignore:
														UpperCAmelCase				: List[Any]											=	''' '''.join(text.split(_lowerCAmelCase						)						)

							return text


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Union[str, Any]:
							# load dataset
							UpperCAmelCase				: Optional[int]											=	load_dataset(args.dataset     ,   args.config     ,   split=args.split     ,   use_auth_token=_lowerCAmelCase						)

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

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

							# resample audio
							UpperCAmelCase				: Tuple											=	dataset.cast_column('''audio'''     ,   Audio(sampling_rate=_lowerCAmelCase						)						)

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

							# map function to decode audio
							def map_to_pred(_lowerCAmelCase						:		List[str]						):
														UpperCAmelCase				: Optional[int]											=	asr(
														    batch['''audio''']['''array''']     ,   chunk_length_s=args.chunk_length_s     ,   stride_length_s=args.stride_length_s						)

														UpperCAmelCase				: str											=	prediction['''text''']
														UpperCAmelCase				: int											=	normalize_text(batch['''sentence''']						)
														return batch

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

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


if __name__ == "__main__":
						UpperCamelCase__:     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.",
						)
						UpperCamelCase__:     List[str]										=    parser.parse_args()

						main(args)





