






"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


UpperCAmelCase_   : Union[str, Any]   			= logging.get_logger(__name__)




class 							lowerCAmelCase__				(			A_ ):






      '''simple docstring'''

      __UpperCamelCase	 =  ["pixel_values"]
      def __init__(						self     :  str						,	lowercase_     :  bool = True						,	lowercase_     :  Union[int, float] = 1 / 255						,	lowercase_     :  bool = True						,	lowercase_     :  int = 8						,	**lowercase_     :  str						,	):




          '''simple docstring'''


          super().__init__(**snake_case__)

          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		do_rescale
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		rescale_factor
          SCREAMING_SNAKE_CASE_      :  str    						=		do_pad
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		pad_size
      def _SCREAMING_SNAKE_CASE (						self     :  int						,	lowercase_     :  np.ndarray						,	lowercase_     :  float						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          return rescale(snake_case__						,	scale=snake_case__						,	data_format=snake_case__						,	**snake_case__)
      def _SCREAMING_SNAKE_CASE (						self     :  List[str]						,	lowercase_     :  np.ndarray						,	lowercase_     :  int						,	lowercase_     :  Optional[Union[str, ChannelDimension]] = None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		get_image_size(snake_case__)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		(old_height // size + 1) * size - old_height
          SCREAMING_SNAKE_CASE_      :  int    						=		(old_width // size + 1) * size - old_width

          return pad(snake_case__						,	((0, pad_height), (0, pad_width))						,	mode='''symmetric'''						,	data_format=snake_case__)



      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	lowercase_     :  ImageInput						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  Optional[float] = None						,	lowercase_     :  Optional[bool] = None						,	lowercase_     :  Optional[int] = None						,	lowercase_     :  Optional[Union[str, TensorType]] = None						,	lowercase_     :  Union[str, ChannelDimension] = ChannelDimension.FIRST						,	**lowercase_     :  Tuple						,	):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		do_rescale if do_rescale is not None else self.do_rescale
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		rescale_factor if rescale_factor is not None else self.rescale_factor
          SCREAMING_SNAKE_CASE_      :  int    						=		do_pad if do_pad is not None else self.do_pad
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		pad_size if pad_size is not None else self.pad_size

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		make_list_of_images(snake_case__)

          if not valid_images(snake_case__):
              raise ValueError(
                  '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
                  '''torch.Tensor, tf.Tensor or jax.ndarray.''')

          if do_rescale and rescale_factor is None:
              raise ValueError('''Rescale factor must be specified if do_rescale is True.''')

          # All transformations expect numpy arrays.
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		[to_numpy_array(snake_case__) for image in images]

          if do_rescale:
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		[self.rescale(image=snake_case__						,	scale=snake_case__) for image in images]

          if do_pad:
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		[self.pad(snake_case__						,	size=snake_case__) for image in images]

          SCREAMING_SNAKE_CASE_      :  Any    						=		[to_channel_dimension_format(snake_case__						,	snake_case__) for image in images]

          SCREAMING_SNAKE_CASE_      :  Tuple    						=		{"pixel_values": images}
          return BatchFeature(data=snake_case__						,	tensor_type=snake_case__)




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 			UpperCamelCase					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

       # compute metrics
       snake_case       :   List[Any]							=      wer.compute(references=result["target"]				,					predictions=result["prediction"]    )
       snake_case       :   int							=      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       :   int							=      f"""log_{dataset_id}_predictions.txt"""
              snake_case       :   List[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(__lowerCamelCase     : str				,					__lowerCamelCase     : Optional[int]    ):
                            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 			UpperCamelCase					(    __lowerCamelCase     : str    ):
       snake_case       :   List[Any]							=      "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

       snake_case       :   List[Any]							=      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[Any]							=      ["\n\n", "\n", "   ", "  "]

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       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       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      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       :   Tuple							=      normalize_text(batch["sentence"]    )
              return batch

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


if __name__ == "__main__":
   __lowerCamelCase							=       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							=       parser.parse_args()

   main(args)





