




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					np.inf

			def set_batch_size(SCREAMING_SNAKE_CASE   ) -> None:
						nonlocal batch_size
						if isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS   )
						elif isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
									SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS   )
						elif isinstance(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ) and feature.dtype == "binary":
									SCREAMING_SNAKE_CASE           =					min(SCREAMING_SNAKE_CASE					,	config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS   )

			_visit(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			return None if batch_size is np.inf else batch_size




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							def __init__(					self       :	str       ,     a       :	NestedDataStructureLike[PathLike]       ,     a       :	Optional[NamedSplit] = None       ,     a       :	Optional[Features] = None       ,     a       :	str = None       ,     a       :	bool = False       ,     a       :	bool = False       ,     a       :	Optional[int] = None       ,     **a       :	Any       ,     )			->					Union[str, Any]:
										super().__init__(
										    a       ,     split=a       ,     features=a       ,     cache_dir=a       ,     keep_in_memory=a       ,     streaming=a       ,     num_proc=a       ,     **a       ,     )
										SCREAMING_SNAKE_CASE           =					path_or_paths if isinstance(a       ,     a			) else {self.split: path_or_paths}
										SCREAMING_SNAKE_CASE           =					_PACKAGED_DATASETS_MODULES["""parquet"""][1]
										SCREAMING_SNAKE_CASE           =					Parquet(
										    cache_dir=a       ,     data_files=a       ,     features=a       ,     hash=a       ,     **a       ,     )







							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					List[Any]:
										# Build iterable dataset
										if self.streaming:
													SCREAMING_SNAKE_CASE           =					self.builder.as_streaming_dataset(split=self.split			)
										# Build regular (map-style) dataset
										else:
													SCREAMING_SNAKE_CASE           =					None
													SCREAMING_SNAKE_CASE           =					None
													SCREAMING_SNAKE_CASE           =					None
													SCREAMING_SNAKE_CASE           =					None

													self.builder.download_and_prepare(
													    download_config=a       ,     download_mode=a       ,     verification_mode=a       ,     base_path=a       ,     num_proc=self.num_proc       ,     )
													SCREAMING_SNAKE_CASE           =					self.builder.as_dataset(
													    split=self.split       ,     verification_mode=a       ,     in_memory=self.keep_in_memory			)
										return dataset





class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[Any]       ,     a       :	Dataset       ,     a       :	Union[PathLike, BinaryIO]       ,     a       :	Optional[int] = None       ,     **a       :	str       ,     )			->					Optional[int]:
										SCREAMING_SNAKE_CASE           =					dataset
										SCREAMING_SNAKE_CASE           =					path_or_buf
										SCREAMING_SNAKE_CASE           =					batch_size or get_writer_batch_size(dataset.features			)
										SCREAMING_SNAKE_CASE           =					parquet_writer_kwargs





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										SCREAMING_SNAKE_CASE           =					self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

										if isinstance(self.path_or_buf       ,     (str, bytes, os.PathLike)			):
													with open(self.path_or_buf       ,     """wb+"""			) as buffer:
																SCREAMING_SNAKE_CASE           =					self._write(file_obj=a       ,     batch_size=a       ,     **self.parquet_writer_kwargs			)
										else:
													SCREAMING_SNAKE_CASE           =					self._write(file_obj=self.path_or_buf       ,     batch_size=a       ,     **self.parquet_writer_kwargs			)
										return written







							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	BinaryIO       ,     a       :	int       ,     **a       :	Optional[int]			)			->					int:
										SCREAMING_SNAKE_CASE           =					0
										SCREAMING_SNAKE_CASE           =					parquet_writer_kwargs.pop("""path_or_buf"""       ,     a			)
										SCREAMING_SNAKE_CASE           =					self.dataset.features.arrow_schema

										SCREAMING_SNAKE_CASE           =					pq.ParquetWriter(a       ,     schema=a       ,     **a			)

										for offset in logging.tqdm(
										    range(0       ,     len(self.dataset			)       ,     a			)       ,     unit="""ba"""       ,     disable=not logging.is_progress_bar_enabled()       ,     desc="""Creating parquet from Arrow format"""       ,     ):
													SCREAMING_SNAKE_CASE           =					query_table(
													    table=self.dataset._data       ,     key=slice(a       ,     offset + batch_size			)       ,     indices=self.dataset._indices if self.dataset._indices is not None else None       ,     )
													writer.write_table(a			)
													written += batch.nbytes
										writer.close()
										return written












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)






