





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 lowerCamelCase__    (							snake_case_	:  Dataset       ,      snake_case_	:  Dict[str, str]       )      ->				int:
   __snake_case							       =   args.log_outputs
   __snake_case							       =   '''_'''.join(args.dataset.split('''/'''       ) + [args.config, args.split]       )

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

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

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

   __snake_case							       =   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							       =   ['''\n\n''', '''\n''', '''   ''', '''  ''']

   for t in token_sequences_to_ignore:
      __snake_case							       =   ''' '''.join(text.split(snake_case_       )       )

   return text






def lowerCamelCase__    (							snake_case_	:  Any       )      ->				str:
   # load dataset
   __snake_case							       =   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							       =   AutoFeatureExtractor.from_pretrained(args.model_id       )
   __snake_case							       =   feature_extractor.sampling_rate

   # resample audio
   __snake_case							       =   dataset.cast_column('''audio'''       ,      Audio(sampling_rate=snake_case_       )       )

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

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

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

   # run inference on all examples
   __snake_case							       =   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__":
    snake_case_				=			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.',
    )
    snake_case_				=			parser.parse_args()

    main(args)








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, 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_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



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


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

