


"""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 _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=args.log_outputs
    a					='''_'''.join(args.dataset.split('''/'''		) + [args.config, args.split]		)

    # load metric
    a					=load_metric('''wer'''		)
    a					=load_metric('''cer'''		)

    # compute metrics
    a					=wer.compute(references=result['''target''']	,   predictions=result['''prediction''']		)
    a					=cer.compute(references=result['''target''']	,   predictions=result['''prediction''']		)

    # print & log results
    a					=f'''WER: {wer_result}\nCER: {cer_result}'''
    print(lowercase		)

    with open(f'''{dataset_id}_eval_results.txt'''	,   '''w'''		) as f:
        f.write(lowercase		)

    # log all results in text file. Possibly interesting for analysis
    if log_outputs is not None:
        a					=f'''log_{dataset_id}_predictions.txt'''
        a					=f'''log_{dataset_id}_targets.txt'''

        with open(lowercase	,   '''w'''		) as p, open(lowercase	,   '''w'''		) as t:
            # mapping function to write output
            def write_to_file(lowercase	,   lowercase		):
                p.write(f'''{i}''' + '''\n'''		)
                p.write(batch['''prediction'''] + '''\n'''		)
                t.write(f'''{i}''' + '''\n'''		)
                t.write(batch['''target'''] + '''\n'''		)

            result.map(lowercase	,   with_indices=lowercase		)
def _A						(						lowercase		):







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

    a					=re.sub(lowercase	,   ''''''	,   text.lower()		)

    # In addition, we can normalize the target text, e.g. removing new lines characters etc...
    # note that order is important here!
    a					=['''\n\n''', '''\n''', '''   ''', '''  ''']

    for t in token_sequences_to_ignore:
        a					=''' '''.join(text.split(lowercase		)		)

    return text
def _A						(						lowercase		):







    """simple docstring"""
    # load dataset
    a					=load_dataset(args.dataset	,   args.config	,   split=args.split	,   use_auth_token=lowercase		)

    # for testing: only process the first two examples as a test
    # dataset = dataset.select(range(10))

    # load processor
    a					=AutoFeatureExtractor.from_pretrained(args.model_id		)
    a					=feature_extractor.sampling_rate

    # resample audio
    a					=dataset.cast_column('''audio'''	,   Audio(sampling_rate=lowercase		)		)

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

    # map function to decode audio
    def map_to_pred(lowercase		):
        a					=asr(
            batch['''audio''']['''array''']	,   chunk_length_s=args.chunk_length_s	,   stride_length_s=args.stride_length_s		)

        a					=prediction['''text''']
        a					=normalize_text(batch['''sentence''']		)
        return batch

    # run inference on all examples
    a					=dataset.map(lowercase	,   remove_columns=dataset.column_names		)

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


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[int]      						=  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.""",
       )
       lowerCamelCase_       :       int      						=  parser.parse_args()

       main(args)



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       :       Union[str, Any]      						=  {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Optional[int]      						=  ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       lowerCamelCase_       :       Union[str, Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)