




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


__A						:							Tuple							=  logging.get_logger(__name__)




@dataclass
class 			UpperCAmelCase_      :




							'''simple docstring'''



							a__						  =     field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()       )}       )
							a__						  =     field(
							    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}       )
							a__						  =     field(
							    default=1_2_8		,      metadata={
							        '''help''': (
							            '''The maximum total input sequence length after tokenization. Sequences longer '''
							            '''than this will be truncated, sequences shorter will be padded.'''
							        )
							    }		,      )
							a__						  =     field(
							    default=UpperCamelCase__		,      metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}       )





							def    _UpperCAmelCase     (					self       :	Dict			)			->					Union[str, Any]:
										SCREAMING_SNAKE_CASE           =					self.task_name.lower()





class 			UpperCAmelCase_      (   UpperCamelCase__       ):




							'''simple docstring'''



							a__						  =     '''train'''
							a__						  =     '''dev'''
							a__						  =     '''test'''





class 			UpperCAmelCase_      (   UpperCamelCase__       ):




							'''simple docstring'''



							a__						  =     4_2
							a__						  =     4_2
							a__						  =     4_2





							def __init__(					self       :	Any       ,     a       :	GlueDataTrainingArguments       ,     a       :	PreTrainedTokenizerBase       ,     a       :	Optional[int] = None       ,     a       :	Union[str, Split] = Split.train       ,     a       :	Optional[str] = None       ,     )			->					List[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"""       ,     __A       ,     )
										SCREAMING_SNAKE_CASE           =					args
										SCREAMING_SNAKE_CASE           =					glue_processors[args.task_name]()
										SCREAMING_SNAKE_CASE           =					glue_output_modes[args.task_name]
										if isinstance(__A       ,     __A			):
													try:
																SCREAMING_SNAKE_CASE           =					Split[mode]
													except KeyError:
																raise KeyError("""mode is not a valid split name"""			)
        # Load data features from cache or dataset file
										SCREAMING_SNAKE_CASE           =					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}"""       ,     )
										SCREAMING_SNAKE_CASE           =					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)
													SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					label_list[2], label_list[1]
										SCREAMING_SNAKE_CASE           =					label_list

										# Make sure only the first process in distributed training processes the dataset,
										# and the others will use the cache.
										SCREAMING_SNAKE_CASE           =					cached_features_file + """.lock"""
										with FileLock(__A			):
													if os.path.exists(__A			) and not args.overwrite_cache:
																SCREAMING_SNAKE_CASE           =					time.time()
																SCREAMING_SNAKE_CASE           =					torch.load(__A			)
																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:
																			SCREAMING_SNAKE_CASE           =					self.processor.get_dev_examples(args.data_dir			)
																elif mode == Split.test:
																			SCREAMING_SNAKE_CASE           =					self.processor.get_test_examples(args.data_dir			)
																else:
																			SCREAMING_SNAKE_CASE           =					self.processor.get_train_examples(args.data_dir			)
																if limit_length is not None:
																			SCREAMING_SNAKE_CASE           =					examples[:limit_length]
																SCREAMING_SNAKE_CASE           =					glue_convert_examples_to_features(
																    __A       ,     __A       ,     max_length=args.max_seq_length       ,     label_list=__A       ,     output_mode=self.output_mode       ,     )
																SCREAMING_SNAKE_CASE           =					time.time()
																torch.save(self.features       ,     __A			)
																# ^ 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       :	List[Any]			)			->					str:
										return len(self.features			)





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







							def    _UpperCAmelCase     (					self       :	int			)			->					Union[str, Any]:
										return self.label_list









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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					args.log_outputs
			SCREAMING_SNAKE_CASE           =					"""_""".join(args.dataset.split("""/"""   ) + [args.config, args.split]   )

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

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

			# print & log results
			SCREAMING_SNAKE_CASE           =					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:
						SCREAMING_SNAKE_CASE           =					f"""log_{dataset_id}_predictions.txt"""
						SCREAMING_SNAKE_CASE           =					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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

			SCREAMING_SNAKE_CASE           =					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!
			SCREAMING_SNAKE_CASE           =					["""\n\n""", """\n""", """   """, """  """]

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

			return text
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			# load dataset
			SCREAMING_SNAKE_CASE           =					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
			SCREAMING_SNAKE_CASE           =					AutoFeatureExtractor.from_pretrained(args.model_id   )
			SCREAMING_SNAKE_CASE           =					feature_extractor.sampling_rate

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

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

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

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

			# run inference on all examples
			SCREAMING_SNAKE_CASE           =					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						:							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						:							Tuple							=  parser.parse_args()

		main(args)






