



'''simple docstring'''






import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


UpperCamelCase__:     List[str]										=    logging.get_logger(__name__)

@dataclass
class        SCREAMING_SNAKE_CASE:







	"""simple docstring"""






	lowerCamelCase__    				= field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()    )}    )
	lowerCamelCase__    				= field(
	    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}    )
	lowerCamelCase__    				= field(
	    default=128							,	metadata={
	        """help""": (
	            """The maximum total input sequence length after tokenization. Sequences longer """
	            """than this will be truncated, sequences shorter will be padded."""
	        )
	    }							,	)
	lowerCamelCase__    				= field(
	    default=A__							,	metadata={"""help""": """Overwrite the cached training and evaluation sets"""}    )







	def   A			(		self    :  Tuple	)    ->	Any:
								UpperCAmelCase				: Dict											=	self.task_name.lower()

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= """train"""
	lowerCamelCase__    				= """dev"""
	lowerCamelCase__    				= """test"""

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42
	lowerCamelCase__    				= 42







	def __init__(		self    :  Union[str, Any]	,    __snake_case    :  List[str]	,    __snake_case    :  int	,    __snake_case    :  Union[str, Any] = None	,    __snake_case    :  List[Any] = Split.train	,    __snake_case    :  Dict = None	,    )    ->	Any:
								warnings.warn(
								    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
								    '''library. You can have a look at this example script for pointers: '''
								    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'''	,    __lowerCAmelCase	,    )
								UpperCAmelCase				: str											=	args
								UpperCAmelCase				: Optional[int]											=	glue_processors[args.task_name]()
								UpperCAmelCase				: Optional[int]											=	glue_output_modes[args.task_name]
								if isinstance(__lowerCAmelCase	,    __lowerCAmelCase	):
															try:
																						UpperCAmelCase				: List[Any]											=	Split[mode]
															except KeyError:
																						raise KeyError('''mode is not a valid split name'''	)
        # Load data features from cache or dataset file
								UpperCAmelCase				: List[str]											=	os.path.join(
								    cache_dir if cache_dir is not None else args.data_dir	,    F"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}"""	,    )
								UpperCAmelCase				: List[str]											=	self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															UpperCAmelCase							,	UpperCAmelCase				: Optional[Any]											=	label_list[2], label_list[1]
								UpperCAmelCase				: Union[str, Any]											=	label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								UpperCAmelCase				: str											=	cached_features_file + '''.lock'''
								with FileLock(__lowerCAmelCase	):
															if os.path.exists(__lowerCAmelCase	) and not args.overwrite_cache:
																						UpperCAmelCase				: List[Any]											=	time.time()
																						UpperCAmelCase				: Union[str, Any]											=	torch.load(__lowerCAmelCase	)
																						logger.info(
																						    F"""Loading features from cached file {cached_features_file} [took %.3f s]"""	,    time.time() - start	)
															else:
																						logger.info(F"""Creating features from dataset file at {args.data_dir}"""	)

																						if mode == Split.dev:
																													UpperCAmelCase				: List[Any]											=	self.processor.get_dev_examples(args.data_dir	)
																						elif mode == Split.test:
																													UpperCAmelCase				: Optional[Any]											=	self.processor.get_test_examples(args.data_dir	)
																						else:
																													UpperCAmelCase				: Any											=	self.processor.get_train_examples(args.data_dir	)
																						if limit_length is not None:
																													UpperCAmelCase				: Tuple											=	examples[:limit_length]
																						UpperCAmelCase				: List[str]											=	glue_convert_examples_to_features(
																						    __lowerCAmelCase	,    __lowerCAmelCase	,    max_length=args.max_seq_length	,    label_list=__lowerCAmelCase	,    output_mode=self.output_mode	,    )
																						UpperCAmelCase				: Optional[int]											=	time.time()
																						torch.save(self.features	,    __lowerCAmelCase	)
																						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																						logger.info(
																						    F"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""	)







	def __len__(		self    :  Optional[int]	)    ->	Union[str, Any]:
								return len(self.features	)







	def __getitem__(		self    :  Any	,    __snake_case    :  Union[str, Any]	)    ->	Any:
								return self.features[i]







	def   A			(		self    :  Any	)    ->	Any:
								return self.label_list










'''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)





