
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__       (       __lowerCAmelCase						:						Dataset					,							__lowerCAmelCase						:						Dict[str, str]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			args.log_outputs
			lowerCAmelCase_      			=			"_".join(args.dataset.split("/"       ) + [args.config, args.split]       )

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

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

			# print & log results
			lowerCAmelCase_      			=			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:
						lowerCAmelCase_      			=			F"""log_{dataset_id}_predictions.txt"""
						lowerCAmelCase_      			=			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						:						Optional[Any]					,							__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      lowerCamelCase__       (       __lowerCAmelCase						:						str       ):




			"""simple docstring"""


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

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

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

			return text






def      lowerCamelCase__       (       __lowerCAmelCase						:						Union[str, Any]       ):




			"""simple docstring"""


			lowerCAmelCase_      			=			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
			lowerCAmelCase_      			=			AutoFeatureExtractor.from_pretrained(args.model_id       )
			lowerCAmelCase_      			=			feature_extractor.sampling_rate

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

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

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

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

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

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


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)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_A     =  {}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  ["NllbTokenizerFast"]


if TYPE_CHECKING:
			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb import NllbTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_nllb_fast import NllbTokenizerFast

else:
			import sys

			_A     =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



