

"""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       lowercase__						(       snake_case_   :Dataset			,		snake_case_   :Dict[str, str]    ):
	__UpperCAmelCase      		= args.log_outputs
	__UpperCAmelCase      		= '''_'''.join(args.dataset.split('''/'''    ) + [args.config, args.split]    )

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

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

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

	__UpperCAmelCase      		= 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!
	__UpperCAmelCase      		= ['''\n\n''', '''\n''', '''   ''', '''  ''']

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

	return text





def       lowercase__						(       snake_case_   :Dict    ):
	# load dataset
	__UpperCAmelCase      		= 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
	__UpperCAmelCase      		= AutoFeatureExtractor.from_pretrained(args.model_id    )
	__UpperCAmelCase      		= feature_extractor.sampling_rate

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

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

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

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

	# run inference on all examples
	__UpperCAmelCase      		= 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__":
				_lowercase   :	str												=						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.',
				)
				_lowercase   :	List[Any]												=						parser.parse_args()

				main(args)







from timeit import timeit

_lowerCAmelCase				:   Tuple						       =						{
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					List[str]        =      0
   A_       :					str        =      len(_lowerCAmelCase						) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   A_       :					int        =      len(_lowerCAmelCase						) // 2
   A_       :					Union[str, Any]        =      len(_lowerCAmelCase						)

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(_lowerCAmelCase						)						)

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   if len(_lowerCAmelCase						) <= 2:
      return True
   if s[0] == s[len(_lowerCAmelCase						) - 1]:
      return is_palindrome_recursive(s[1:-1]						)
   else:
      return False

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    bool:
   return s == s[::-1]

def   __snake_case    (       _lowerCAmelCase			:			str						)			->    None:
   A_       :					int        =      f"all({name}(key) is value for key, value in test_data.items())"
   A_       :					List[str]        =      f"from __main__ import test_data, {name}"
   A_       :					str        =      500000
   A_       :					List[str]        =      timeit(stmt=_lowerCAmelCase					,					setup=_lowerCAmelCase					,					number=_lowerCAmelCase						)
   print(f"{name:<35} finished {number:,} runs in {result:.5f} seconds"						)


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(F'''{key:21} {value}''')
 print('''a man a plan a canal panama''')

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function('''is_palindrome_slice''')
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function('''is_palindrome''')
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function('''is_palindrome_recursive''')
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function('''is_palindrome_traversal''')
