


"""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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''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(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:
										__SCREAMING_SNAKE_CASE						       =    f"""log_{dataset_id}_predictions.txt"""
										__SCREAMING_SNAKE_CASE						       =    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_  ,						lowerCAmelCase_       ):
																				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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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

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

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

					return text




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    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
					__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=lowerCAmelCase_       )       )

					# 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(lowerCAmelCase_       ):
										__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(lowerCAmelCase_  ,						remove_columns=dataset.column_names       )

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


if __name__ == "__main__":
		a__    :							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.''',
		)
		a__    :							Optional[Any]            =					parser.parse_args()

		main(args)




"""simple docstring"""







from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return math.sqrt(sum(pow(a - b  ,						2       ) for a, b in zip(lowerCAmelCase_  ,						lowerCAmelCase_       )       )       )




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					if dataset.ndim != value_array.ndim:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Wrong input data's dimensions... "
										    f"""dataset : {dataset.ndim}, value_array : {value_array.ndim}"""
										)
										raise ValueError(lowerCAmelCase_       )

					try:
										if dataset.shape[1] != value_array.shape[1]:
															__SCREAMING_SNAKE_CASE						       =    (
															    "Wrong input data's shape... "
															    f"""dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"""
															)
															raise ValueError(lowerCAmelCase_       )
					except IndexError:
										if dataset.ndim != value_array.ndim:
															raise TypeError("Wrong shape"       )

					if dataset.dtype != value_array.dtype:
										__SCREAMING_SNAKE_CASE						       =    (
										    "Input data have different datatype... "
										    f"""dataset : {dataset.dtype}, value_array : {value_array.dtype}"""
										)
										raise TypeError(lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    []

					for value in value_array:
										__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						dataset[0]       )
										__SCREAMING_SNAKE_CASE						       =    dataset[0].tolist()

										for dataset_value in dataset[1:]:
															__SCREAMING_SNAKE_CASE						       =    euclidean(lowerCAmelCase_  ,						lowerCAmelCase_       )

															if dist > temp_dist:
																				__SCREAMING_SNAKE_CASE						       =    temp_dist
																				__SCREAMING_SNAKE_CASE						       =    dataset_value.tolist()

										answer.append([vector, dist]       )

					return answer




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					return np.dot(lowerCAmelCase_  ,						lowerCAmelCase_       ) / (norm(lowerCAmelCase_       ) * norm(lowerCAmelCase_       ))


if __name__ == "__main__":
		import doctest

		doctest.testmod()
