


"""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 		lowercase							(     _snake_case						:      Dataset     ,   _snake_case						:      Dict[str, str]     )     ->List[str]:
	"""simple docstring"""





	__snake_case	:						str						=				args.log_outputs
	__snake_case	:						Union[str, Any]						=				'''_'''.join(args.dataset.split('''/'''     ) + [args.config, args.split]     )

	# load metric
	__snake_case	:						Dict						=				load_metric('''wer'''     )
	__snake_case	:						str						=				load_metric('''cer'''     )

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

	# print & log results
	__snake_case	:						List[Any]						=				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:
		__snake_case	:						Optional[Any]						=				f"""log_{dataset_id}_predictions.txt"""
		__snake_case	:						Optional[int]						=				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						:      Dict     ,   _snake_case						:      List[str]     ):
				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 		lowercase							(     _snake_case						:      str     )     ->str:
	"""simple docstring"""





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

	__snake_case	:						Dict						=				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!
	__snake_case	:						Union[str, Any]						=				['''\n\n''', '''\n''', '''   ''', '''  ''']

	for t in token_sequences_to_ignore:
		__snake_case	:						List[str]						=				''' '''.join(text.split(_snake_case     )     )

	return text






def 		lowercase							(     _snake_case						:      Dict     )     ->Any:
	"""simple docstring"""





	__snake_case	:						Tuple						=				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
	__snake_case	:						Dict						=				AutoFeatureExtractor.from_pretrained(args.model_id     )
	__snake_case	:						Union[str, Any]						=				feature_extractor.sampling_rate

	# resample audio
	__snake_case	:						Union[str, Any]						=				dataset.cast_column('''audio'''     ,   Audio(sampling_rate=_snake_case     )     )

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

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

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

	# run inference on all examples
	__snake_case	:						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__":
			SCREAMING_SNAKE_CASE :    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.""",
			)
			SCREAMING_SNAKE_CASE :    Union[str, Any]									=				parser.parse_args()

			main(args)









"""simple docstring"""
from sklearn.metrics import recall_score

import datasets


SCREAMING_SNAKE_CASE :    Dict									=				"""
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true positives and FN is the false negatives.
"""


SCREAMING_SNAKE_CASE :    Any									=				"""
Args:
- **predictions** (`list` of `int`): The predicted labels.
- **references** (`list` of `int`): The ground truth labels.
- **labels** (`list` of `int`): The set of labels to include when `average` is not set to `binary`, and their order when average is `None`. Labels present in the data can be excluded in this input, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in y_true and y_pred are used in sorted order. Defaults to None.
- **pos_label** (`int`): The class label to use as the 'positive class' when calculating the recall. Defaults to `1`.
- **average** (`string`): This parameter is required for multiclass/multilabel targets. If None, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
    - `'binary'`: Only report results for the class specified by `pos_label`. This is applicable only if the target labels and predictions are binary.
    - `'micro'`: Calculate metrics globally by counting the total true positives, false negatives, and false positives.
    - `'macro'`: Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
    - `'weighted'`: Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. Note that it can result in an F-score that is not between precision and recall.
    - `'samples'`: Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
- **sample_weight** (`list` of `float`): Sample weights Defaults to `None`.
- **zero_division** (): Sets the value to return when there is a zero division. Defaults to .
    - `'warn'`: If there is a zero division, the return value is `0`, but warnings are also raised.
    - `0`: If there is a zero division, the return value is `0`.
    - `1`: If there is a zero division, the return value is `1`.

Returns:
- **recall** (`float`, or `array` of `float`): Either the general recall score, or the recall scores for individual classes, depending on the values input to `labels` and `average`. Minimum possible value is 0. Maximum possible value is 1. A higher recall means that more of the positive examples have been labeled correctly. Therefore, a higher recall is generally considered better.

Examples:

    Example 1-A simple example with some errors
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1])
        >>> print(results)
        {'recall': 0.6666666666666666}

    Example 2-The same example as Example 1, but with `pos_label=0` instead of the default `pos_label=1`.
        >>> recall_metric = datasets.load_metric('recall')
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], pos_label=0)
        >>> print(results)
        {'recall': 0.5}

    Example 3-The same example as Example 1, but with `sample_weight` included.
        >>> recall_metric = datasets.load_metric('recall')
        >>> sample_weight = [0.9, 0.2, 0.9, 0.3, 0.8]
        >>> results = recall_metric.compute(references=[0, 0, 1, 1, 1], predictions=[0, 1, 0, 1, 1], sample_weight=sample_weight)
        >>> print(results)
        {'recall': 0.55}

    Example 4-A multiclass example, using different averages.
        >>> recall_metric = datasets.load_metric('recall')
        >>> predictions = [0, 2, 1, 0, 0, 1]
        >>> references = [0, 1, 2, 0, 1, 2]
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='macro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='micro')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average='weighted')
        >>> print(results)
        {'recall': 0.3333333333333333}
        >>> results = recall_metric.compute(predictions=predictions, references=references, average=None)
        >>> print(results)
        {'recall': array([1., 0., 0.])}
"""


SCREAMING_SNAKE_CASE :    Tuple									=				"""
@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}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,				_KWARGS_DESCRIPTION				)
class 			_UpperCAmelCase (							datasets.Metric				):






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return datasets.MetricInfo(
			    description=_DESCRIPTION				,       citation=_CITATION				,       inputs_description=_KWARGS_DESCRIPTION				,       features=datasets.Features(
			        {
			            '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
			            '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
			        }
			        if self.config_name == '''multilabel'''
			        else {
			            '''predictions''': datasets.Value('''int32'''					),
			            '''references''': datasets.Value('''int32'''					),
			        }					)				,       reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html''']				,       )



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_=None				,       a_=1				,       a_="binary"				,       a_=None				,       a_="warn"				,       ):





			'''simple docstring'''

			__snake_case	:						Any						=				recall_score(
			    a_				,       a_				,       labels=a_				,       pos_label=a_				,       average=a_				,       sample_weight=a_				,       zero_division=a_				,       )
			return {"recall": float(a_					) if score.size == 1 else score}





