




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      _a				(						SCREAMING_SNAKE_CASE_							:    Dataset				,				SCREAMING_SNAKE_CASE_							:    Dict[str, str]					):
					__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(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:
										__lowerCAmelCase           =       F"""log_{dataset_id}_predictions.txt"""
										__lowerCAmelCase           =       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_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Any					):
																				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      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =       "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

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

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

					return text







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]					):
					# load dataset
					__lowerCAmelCase           =       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
					__lowerCAmelCase           =       AutoFeatureExtractor.from_pretrained(args.model_id					)
					__lowerCAmelCase           =       feature_extractor.sampling_rate

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

					# 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(SCREAMING_SNAKE_CASE_							:    str					):
										__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(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__":
					UpperCamelCase__         =      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.""",
					)
					UpperCamelCase__         =      parser.parse_args()

					main(args)










from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__         =      """\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

UpperCamelCase__         =      """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


UpperCamelCase__         =      """
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

        \"raw_values\" : Returns a full set of errors in case of multioutput input.

        \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric(\"mse\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {'mse': 0.6123724356957945}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mse_metric = datasets.load_metric(\"mse\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mse': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mse': array([0.41666667, 1. ])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION			,    _KWARGS_DESCRIPTION				)
class       a__   ( datasets.Metric				):






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											return datasets.MetricInfo(
											    description=_DESCRIPTION		,      citation=_CITATION		,      inputs_description=_KWARGS_DESCRIPTION		,      features=datasets.Features(self._get_feature_types()      )		,      reference_urls=[
											        "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
											    ]		,      )






						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											if self.config_name == "multilist":
																return {
																    "predictions": datasets.Sequence(datasets.Value("float"      )      ),
																    "references": datasets.Sequence(datasets.Value("float"      )      ),
																}
											else:
																return {
																    "predictions": datasets.Value("float"      ),
																    "references": datasets.Value("float"      ),
																}






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=None		,      _A="uniform_average"		,      _A=True      ):




											"""simple docstring"""



											__lowerCAmelCase           =	mean_squared_error(
											    _A		,      _A		,      sample_weight=_A		,      multioutput=_A		,      squared=_A      )

											return {"mse": mse}




