



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

    # load metric
    snake_case_ :     Dict				=	load_metric('''wer'''    )
    snake_case_ :     Optional[Any]				=	load_metric('''cer'''    )

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

    # print & log results
    snake_case_ :     int				=	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:
        snake_case_ :     str				=	f"log_{dataset_id}_predictions.txt"
        snake_case_ :     Union[str, Any]				=	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(_a		,			_a    ):
                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 __lowercase			(							_a    ):
    snake_case_ :     List[str]				=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

    snake_case_ :     List[str]				=	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!
    snake_case_ :     Optional[int]				=	["""\n\n""", """\n""", """   """, """  """]

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

    return text




def __lowercase			(							_a    ):
    snake_case_ :     str				=	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
    snake_case_ :     List[Any]				=	AutoFeatureExtractor.from_pretrained(args.model_id    )
    snake_case_ :     Dict				=	feature_extractor.sampling_rate

    # resample audio
    snake_case_ :     str				=	dataset.cast_column('''audio'''		,			Audio(sampling_rate=lowerCAmelCase_    )    )

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

    # map function to decode audio
    def map_to_pred(_a    ):
        snake_case_ :     Optional[Any]				=	asr(
            batch['''audio''']['''array''']		,			chunk_length_s=args.chunk_length_s		,			stride_length_s=args.stride_length_s    )

        snake_case_ :     Any				=	prediction["""text"""]
        snake_case_ :     str				=	normalize_text(batch['''sentence''']    )
        return batch

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

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


if __name__ == "__main__":
     lowercase__    :  Dict							    =	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.''',
     )
     lowercase__    :  Any							    =	parser.parse_args()

     main(args)










'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



