



"""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       _snake_case						(						UpperCamelCase						:		Dataset			,		UpperCamelCase						:		Dict[str, str]						):
 UpperCAmelCase	:					Any	=	args.log_outputs
 UpperCAmelCase	:					Any	=	"""_""".join(args.dataset.split("""/"""						) + [args.config, args.split]						)

 # load metric
 UpperCAmelCase	:					List[Any]	=	load_metric("""wer"""						)
 UpperCAmelCase	:					Any	=	load_metric("""cer"""						)

 # compute metrics
 UpperCAmelCase	:					int	=	wer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)
 UpperCAmelCase	:					str	=	cer.compute(references=result["""target"""]			,		predictions=result["""prediction"""]						)

 # print & log results
 UpperCAmelCase	:					Tuple	=	F"WER: {wer_result}\nCER: {cer_result}"
 print(UpperCamelCase						)

 with open(F"{dataset_id}_eval_results.txt"			,		"""w"""						) as f:
  f.write(UpperCamelCase						)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  UpperCAmelCase	:					str	=	F"log_{dataset_id}_predictions.txt"
  UpperCAmelCase	:					Tuple	=	F"log_{dataset_id}_targets.txt"

  with open(UpperCamelCase			,		"""w"""						) as p, open(UpperCamelCase			,		"""w"""						) as t:
   # mapping function to write output
   def write_to_file(UpperCamelCase						:		List[Any]			,		UpperCamelCase						:		List[Any]						):
    p.write(F"{i}" + """\n"""						)
    p.write(batch["""prediction"""] + """\n"""						)
    t.write(F"{i}" + """\n"""						)
    t.write(batch["""target"""] + """\n"""						)

   result.map(UpperCamelCase			,		with_indices=UpperCamelCase						)

def       _snake_case						(						UpperCamelCase						:		str						):
 UpperCAmelCase	:					List[str]	=	"""[,?.!\-\;\:\"“%‘”�—’…–]"""  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 UpperCAmelCase	:					Dict	=	re.sub(UpperCamelCase			,		""""""			,		text.lower()						)

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

 for t in token_sequences_to_ignore:
  UpperCAmelCase	:					Optional[Any]	=	""" """.join(text.split(UpperCamelCase						)						)

 return text

def       _snake_case						(						UpperCamelCase						:		Tuple						):
 # load dataset
 UpperCAmelCase	:					Union[str, Any]	=	load_dataset(args.dataset			,		args.config			,		split=args.split			,		use_auth_token=UpperCamelCase						)

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

 # load processor
 UpperCAmelCase	:					Optional[int]	=	AutoFeatureExtractor.from_pretrained(args.model_id						)
 UpperCAmelCase	:					Any	=	feature_extractor.sampling_rate

 # resample audio
 UpperCAmelCase	:					List[str]	=	dataset.cast_column("""audio"""			,		Audio(sampling_rate=UpperCamelCase						)						)

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

 # map function to decode audio
 def map_to_pred(UpperCamelCase						:		Any						):
  UpperCAmelCase	:					Any	=	asr(
      batch["""audio"""]["""array"""]			,		chunk_length_s=args.chunk_length_s			,		stride_length_s=args.stride_length_s						)

  UpperCAmelCase	:					Tuple	=	prediction["""text"""]
  UpperCAmelCase	:					List[str]	=	normalize_text(batch["""sentence"""]						)
  return batch

 # run inference on all examples
 UpperCAmelCase	:					int	=	dataset.map(UpperCamelCase			,		remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
      A:   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.",
      )
      A:   Union[str, Any]								=		parser.parse_args()

      main(args)





"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


A:   Any								=		datasets.utils.logging.get_logger(__name__)



@dataclass
class       SCREAMING_SNAKE_CASE__  (					datasets.BuilderConfig ):
   __lowerCAmelCase  :   Optional[datasets.Features]						=						None
   __lowerCAmelCase  :   str						=						"utf-8"
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   Optional[str]						=						None
   __lowerCAmelCase  :   bool						=						True  # deprecated
   __lowerCAmelCase  :   Optional[int]						=						None  # deprecated
   __lowerCAmelCase  :   int						=						10 << 20  # 10MB
   __lowerCAmelCase  :   Optional[bool]						=						None



class       SCREAMING_SNAKE_CASE__  (					datasets.ArrowBasedBuilder ):
   __lowerCAmelCase  :   int						=						JsonConfig

   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Any:





    '''simple docstring'''




    if self.config.block_size is not None:
     logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""						)
     UpperCAmelCase	:					Any	=	self.config.block_size
    if self.config.use_threads is not True:
     logger.warning(
         """The JSON loader parameter `use_threads` is deprecated and doesn't have any effect anymore."""						)
    if self.config.newlines_in_values is not None:
     raise ValueError("""The JSON loader parameter `newlines_in_values` is no longer supported"""						)
    return datasets.DatasetInfo(features=self.config.features						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Tuple:





    '''simple docstring'''




    if not self.config.data_files:
     raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"						)
    UpperCAmelCase	:					List[Any]	=	dl_manager.download_and_extract(self.config.data_files						)
    if isinstance(_SCREAMING_SNAKE_CASE      ,     (str, list, tuple)						):
     UpperCAmelCase	:					int	=	data_files
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Tuple	=	[files]
     UpperCAmelCase	:					List[str]	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,     gen_kwargs={"""files""": files}						)]
    UpperCAmelCase	:					List[str]	=	[]
    for split_name, files in data_files.items():
     if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
      UpperCAmelCase	:					Dict	=	[files]
     UpperCAmelCase	:					str	=	[dl_manager.iter_files(_SCREAMING_SNAKE_CASE						) for file in files]
     splits.append(datasets.SplitGenerator(name=_SCREAMING_SNAKE_CASE      ,     gen_kwargs={"""files""": files}						)						)
    return splits

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      pa.Table:





    '''simple docstring'''




    if self.config.features is not None:
     # adding missing columns
     for column_name in set(self.config.features						) - set(pa_table.column_names						):
      UpperCAmelCase	:					Tuple	=	self.config.features.arrow_schema.field(_SCREAMING_SNAKE_CASE						).type
      UpperCAmelCase	:					int	=	pa_table.append_column(_SCREAMING_SNAKE_CASE      ,     pa.array([None] * len(_SCREAMING_SNAKE_CASE						)      ,     type=_SCREAMING_SNAKE_CASE						)						)
     # more expensive cast to support nested structures with keys in a different order
     # allows str <-> int/float or str to Audio for example
     UpperCAmelCase	:					Any	=	table_cast(_SCREAMING_SNAKE_CASE      ,     self.config.features.arrow_schema						)
    return pa_table




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Optional[Any]:





    '''simple docstring'''




    for file_idx, file in enumerate(itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE						)						):
     # If the file is one json object and if we need to look at the list of items in one specific field
     if self.config.field is not None:
      with open(_SCREAMING_SNAKE_CASE      ,     encoding=self.config.encoding      ,     errors=self.config.encoding_errors						) as f:
       UpperCAmelCase	:					Union[str, Any]	=	json.load(_SCREAMING_SNAKE_CASE						)

      # We keep only the field we are interested in
      UpperCAmelCase	:					Dict	=	dataset[self.config.field]

      # We accept two format: a list of dicts or a dict of lists
      if isinstance(_SCREAMING_SNAKE_CASE      ,     (list, tuple)						):
       UpperCAmelCase	:					Optional[Any]	=	set().union(*[row.keys() for row in dataset]						)
       UpperCAmelCase	:					Union[str, Any]	=	{col: [row.get(_SCREAMING_SNAKE_CASE						) for row in dataset] for col in keys}
      else:
       UpperCAmelCase	:					Optional[Any]	=	dataset
      UpperCAmelCase	:					List[str]	=	pa.Table.from_pydict(_SCREAMING_SNAKE_CASE						)
      yield file_idx, self._cast_table(_SCREAMING_SNAKE_CASE						)

     # If the file has one json object per line
     else:
      with open(_SCREAMING_SNAKE_CASE      ,     """rb"""						) as f:
       UpperCAmelCase	:					Union[str, Any]	=	0
       # Use block_size equal to the chunk size divided by 32 to leverage multithreading
       # Set a default minimum value of 16kB if the chunk size is really small
       UpperCAmelCase	:					int	=	max(self.config.chunksize // 32      ,     16 << 10						)
       UpperCAmelCase	:					Dict	=	(
           self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
       )
       while True:
        UpperCAmelCase	:					Tuple	=	f.read(self.config.chunksize						)
        if not batch:
         break
        # Finish current line
        try:
         batch += f.readline()
        except (AttributeError, io.UnsupportedOperation):
         batch += readline(_SCREAMING_SNAKE_CASE						)
        # PyArrow only accepts utf-8 encoded bytes
        if self.config.encoding != "utf-8":
         UpperCAmelCase	:					Tuple	=	batch.decode(self.config.encoding      ,     errors=_SCREAMING_SNAKE_CASE						).encode("""utf-8"""						)
        try:
         while True:
          try:
           UpperCAmelCase	:					int	=	paj.read_json(
               io.BytesIO(_SCREAMING_SNAKE_CASE						)      ,     read_options=paj.ReadOptions(block_size=_SCREAMING_SNAKE_CASE						)						)
           break
          except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
           if (
               isinstance(_SCREAMING_SNAKE_CASE      ,     pa.ArrowInvalid						)
               and "straddling" not in str(_SCREAMING_SNAKE_CASE						)
               or block_size > len(_SCREAMING_SNAKE_CASE						)
           ):
            raise
           else:
            # Increase the block size in case it was too small.
            # The block size will be reset for the next file.
            logger.debug(
                F"Batch of {len(_SCREAMING_SNAKE_CASE						)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."						)
            block_size *= 2
        except pa.ArrowInvalid as e:
         try:
          with open(
              _SCREAMING_SNAKE_CASE      ,     encoding=self.config.encoding      ,     errors=self.config.encoding_errors						) as f:
           UpperCAmelCase	:					str	=	json.load(_SCREAMING_SNAKE_CASE						)
         except json.JSONDecodeError:
          logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {e}"						)
          raise e
         # If possible, parse the file as a list of json objects and exit the loop
         if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):  # list is the only sequence type supported in JSON
          try:
           UpperCAmelCase	:					Dict	=	set().union(*[row.keys() for row in dataset]						)
           UpperCAmelCase	:					Union[str, Any]	=	{col: [row.get(_SCREAMING_SNAKE_CASE						) for row in dataset] for col in keys}
           UpperCAmelCase	:					Dict	=	pa.Table.from_pydict(_SCREAMING_SNAKE_CASE						)
          except (pa.ArrowInvalid, AttributeError) as e:
           logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {e}"						)
           raise ValueError(F"Not able to read records in the JSON file at {file}."						) from None
          yield file_idx, self._cast_table(_SCREAMING_SNAKE_CASE						)
          break
         else:
          logger.error(F"Failed to read file '{file}' with error {type(_SCREAMING_SNAKE_CASE						)}: {e}"						)
          raise ValueError(
              F"Not able to read records in the JSON file at {file}. "
              F"You should probably indicate the field of the JSON file containing your records. "
              F"This JSON file contain the following fields: {str(list(dataset.keys()						)						)}. "
              F"Select the correct one and provide it as `field='XXX'` to the dataset loading method. "						) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
        yield (file_idx, batch_idx), self._cast_table(_SCREAMING_SNAKE_CASE						)
        batch_idx += 1
