
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_tf_available():
      import tensorflow as tf

      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      from ..tf_utils import stable_softmax

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_A		     =    logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase__ )
class 						lowercase_				(				UpperCAmelCase__ ):

 def __init__(					self   ,		*__UpperCamelCase   ,		**__UpperCamelCase   ):




        """simple docstring"""


        super().__init__(*UpperCAmelCase__   ,		**UpperCAmelCase__   )
        requires_backends(self   ,		"""vision"""   )
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == """tf"""
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase=None   ):




        """simple docstring"""


        UpperCamelCase_											=				{}
        if top_k is not None:
               UpperCamelCase_											=				top_k
        return {}, {}, postprocess_params

 def __call__(					self   ,		__UpperCamelCase   ,		**__UpperCamelCase   ):




        """simple docstring"""


        return super().__call__(UpperCAmelCase__   ,		**UpperCAmelCase__   )

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				load_image(UpperCAmelCase__   )
        UpperCamelCase_											=				self.image_processor(images=UpperCAmelCase__   ,		return_tensors=self.framework   )
        return model_inputs

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				self.model(**UpperCAmelCase__   )
        return model_outputs

 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase=5   ):




        """simple docstring"""


        if top_k > self.model.config.num_labels:
               UpperCamelCase_											=				self.model.config.num_labels

        if self.framework == "pt":
               UpperCamelCase_											=				model_outputs.logits.softmax(-1   )[0]
               UpperCamelCase_				, UpperCamelCase_											=				probs.topk(UpperCAmelCase__   )
        elif self.framework == "tf":
               UpperCamelCase_											=				stable_softmax(model_outputs.logits   ,		axis=-1   )[0]
               UpperCamelCase_											=				tf.math.top_k(UpperCAmelCase__   ,		k=UpperCAmelCase__   )
               UpperCamelCase_				, UpperCamelCase_											=				topk.values.numpy(), topk.indices.numpy()
        else:
               raise ValueError(f'''Unsupported framework: {self.framework}'''   )

        UpperCamelCase_											=				scores.tolist()
        UpperCamelCase_											=				ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCAmelCase__   ,		UpperCAmelCase__   )]









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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """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 SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       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	:		List[Any]							       =   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	:		str							       =   parser.parse_args()

  main(args)


