

'''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 		__lowerCamelCase			(  __snake_case	:						Dataset,						__snake_case	:						Dict[str, str]			)			->				List[Any]:






						"""simple docstring"""





						A__	:  Tuple           =args.log_outputs
						A__	:  Optional[int]           ='''_'''.join(args.dataset.split("""/"""			) + [args.config, args.split]			)

						# load metric
						A__	:  List[str]           =load_metric("""wer"""			)
						A__	:  List[str]           =load_metric("""cer"""			)

						# compute metrics
						A__	:  List[Any]           =wer.compute(references=result["""target"""],						predictions=result["""prediction"""]			)
						A__	:  Tuple           =cer.compute(references=result["""target"""],						predictions=result["""prediction"""]			)

						# print & log results
						A__	:  List[str]           =f"WER: {wer_result}\nCER: {cer_result}"
						print(__snake_case			)

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

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

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

																		result.map(__snake_case,						with_indices=__snake_case			)
def 		__lowerCamelCase			(  __snake_case	:						str			)			->				Tuple:






						"""simple docstring"""





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

						A__	:  Any           =re.sub(__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!
						A__	:  str           =['''\n\n''', '''\n''', '''   ''', '''  ''']

						for t in token_sequences_to_ignore:
												A__	:  Any           =''' '''.join(text.split(__snake_case			)			)

						return text
def 		__lowerCamelCase			(  __snake_case	:						Any			)			->				str:






						"""simple docstring"""





						A__	:  List[str]           =load_dataset(args.dataset,						args.config,						split=args.split,						use_auth_token=__snake_case			)

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

						# load processor
						A__	:  List[str]           =AutoFeatureExtractor.from_pretrained(args.model_id			)
						A__	:  List[str]           =feature_extractor.sampling_rate

						# resample audio
						A__	:  List[str]           =dataset.cast_column("""audio""",						Audio(sampling_rate=__snake_case			)			)

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

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

												A__	:  Union[str, Any]           =prediction['''text''']
												A__	:  List[Any]           =normalize_text(batch["""sentence"""]			)
												return batch

						# run inference on all examples
						A__	:  Optional[Any]           =dataset.map(__snake_case,						remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
		__snake_case					:      List[str] 		=     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.',
		)
		__snake_case					:      Optional[int] 		=     parser.parse_args()

		main(args)








"""simple docstring"""





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class 		SCREAMING_SNAKE_CASE_				(     unittest.TestCase ):
				"""simple docstring"""




				def        __lowercase	(  self	:Dict      ):
											__lowerCamelCase   :   Union[str, Any]       =inspect.getfile(accelerate.test_utils      )
											__lowerCamelCase   :   Union[str, Any]       =os.path.sep.join(
											    mod_file.split(os.path.sep      )[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']      )

											from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

											__lowerCamelCase   :   List[str]       =test_metrics




				@require_cpu
				def        __lowercase	(  self	:Optional[Any]      ):
											debug_launcher(self.test_metrics.main    ,     num_processes=1      )




				@require_cpu
				def        __lowercase	(  self	:int      ):
											debug_launcher(self.test_metrics.main      )




				@require_single_gpu
				def        __lowercase	(  self	:int      ):
											self.test_metrics.main()




				@require_multi_gpu
				def        __lowercase	(  self	:Dict      ):
											print(f'Found {torch.cuda.device_count()} devices.'      )
											__lowerCamelCase   :   Union[str, Any]       =['''torchrun''', f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
											with patch_environment(omp_num_threads=1      ):
																		execute_subprocess_async(__lowercase    ,     env=os.environ.copy()      )




