



"""simple docstring"""
from __future__ import annotations

def       _snake_case						(						UpperCamelCase						:		list			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int			,		UpperCamelCase						:		int						):
 UpperCAmelCase	:					Tuple	=	[]
 UpperCAmelCase	,		UpperCAmelCase	:					Any	=	input_list[low:mid], input_list[mid : high + 1]
 while left and right:
  result.append((left if left[0] <= right[0] else right).pop(0						)						)
 UpperCAmelCase	:					Tuple	=	result + left + right
 return input_list

def       _snake_case						(						UpperCamelCase						:		list						):
 if len(UpperCamelCase						) <= 1:
  return input_list
 UpperCAmelCase	:					Dict	=	list(UpperCamelCase						)

 # iteration for two-way merging
 UpperCAmelCase	:					Dict	=	2
 while p <= len(UpperCamelCase						):
  # getting low, high and middle value for merge-sort of single list
  for i in range(0			,		len(UpperCamelCase						)			,		UpperCamelCase						):
   UpperCAmelCase	:					Optional[int]	=	i
   UpperCAmelCase	:					Dict	=	i + p - 1
   UpperCAmelCase	:					List[str]	=	(low + high + 1) // 2
   UpperCAmelCase	:					Any	=	merge(UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase			,		UpperCamelCase						)
  # final merge of last two parts
  if p * 2 >= len(UpperCamelCase						):
   UpperCAmelCase	:					List[str]	=	i
   UpperCAmelCase	:					str	=	merge(UpperCamelCase			,		0			,		UpperCamelCase			,		len(UpperCamelCase						) - 1						)
   break
  p *= 2

 return input_list


if __name__ == "__main__":
      A:   Union[str, Any]								=		input("Enter numbers separated by a comma:\n").strip()
      if user_input == "":
            A:   Any								=		[]
      else:
            A:   Dict								=		[int(item.strip()) for item in user_input.split(",")]
      print(iter_merge_sort(unsorted))





"""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)
