


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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= args.log_outputs
		lowercase__				= '''_'''.join(args.dataset.split('''/'''	) + [args.config, args.split]	)

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

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

		# print & log results
		lowercase__				= f'WER: {wer_result}\nCER: {cer_result}'
		print(SCREAMING_SNAKE_CASE	)

		with open(f'{dataset_id}_eval_results.txt'		,							'''w'''	) as f:
				f.write(SCREAMING_SNAKE_CASE	)

		# log all results in text file. Possibly interesting for analysis
		if log_outputs is not None:
				lowercase__				= f'log_{dataset_id}_predictions.txt'
				lowercase__				= f'log_{dataset_id}_targets.txt'

				with open(SCREAMING_SNAKE_CASE		,							'''w'''	) as p, open(SCREAMING_SNAKE_CASE		,							'''w'''	) as t:
						# mapping function to write output
						def write_to_file(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):
								p.write(f'{i}' + '''\n'''	)
								p.write(batch['''prediction'''] + '''\n'''	)
								t.write(f'{i}' + '''\n'''	)
								t.write(batch['''target'''] + '''\n'''	)

						result.map(SCREAMING_SNAKE_CASE		,							with_indices=SCREAMING_SNAKE_CASE	)
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= '''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		lowercase__				= re.sub(SCREAMING_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!
		lowercase__				= ['''\n\n''', '''\n''', '''   ''', '''  ''']

		for t in token_sequences_to_ignore:
				lowercase__				= ''' '''.join(text.split(SCREAMING_SNAKE_CASE	)	)

		return text
def      _a					( SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		lowercase__				= load_dataset(args.dataset		,							args.config		,							split=args.split		,							use_auth_token=SCREAMING_SNAKE_CASE	)

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

		# load processor
		lowercase__				= AutoFeatureExtractor.from_pretrained(args.model_id	)
		lowercase__				= feature_extractor.sampling_rate

		# resample audio
		lowercase__				= dataset.cast_column('''audio'''		,							Audio(sampling_rate=SCREAMING_SNAKE_CASE	)	)

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

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

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

		# run inference on all examples
		lowercase__				= dataset.map(SCREAMING_SNAKE_CASE		,							remove_columns=dataset.column_names	)

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


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)



"""simple docstring"""


import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 							a					(		lowerCAmelCase_						,				unittest.TestCase ):
    _snake_case  :	Dict  				=				MgpstrTokenizer
    _snake_case  :	Any  				=				False
    _snake_case  :	str  				=				{}
    _snake_case  :	Dict  				=				False


    def   lowerCAmelCase_	(       self						:	int       ):
      super().setUp()

      # fmt: off
      _UpperCAmelCase    			=   ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
      # fmt: on
      _UpperCAmelCase    			=   dict(zip(__lowerCAmelCase ,					range(len(__lowerCAmelCase       )       )       )       )

      _UpperCAmelCase    			=   os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES["""vocab_file"""]       )
      with open(self.vocab_file ,					"""w""" ,					encoding="""utf-8"""       ) as fp:
        fp.write(json.dumps(__lowerCAmelCase       ) + """\n"""       )


    def   lowerCAmelCase_	(       self						:	Tuple ,					**__lowerCAmelCase						:	Dict       ):
      return MgpstrTokenizer.from_pretrained(self.tmpdirname ,					**__lowerCAmelCase       )


    def   lowerCAmelCase_	(       self						:	List[str] ,					__lowerCAmelCase						:	int       ):
      _UpperCAmelCase    			=   """tester"""
      _UpperCAmelCase    			=   """tester"""
      return input_text, output_text


    @unittest.skip("""MGP-STR always lower cases letters."""       )
    def   lowerCAmelCase_	(       self						:	Tuple       ):
      pass


    def   lowerCAmelCase_	(       self						:	Any       ):
      _UpperCAmelCase    			=   self.get_tokenizers(do_lower_case=__lowerCAmelCase       )
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase    			=   """[SPECIAL_TOKEN]"""

          tokenizer.add_special_tokens({"""cls_token""": special_token}       )
          _UpperCAmelCase    			=   tokenizer.encode([special_token] ,					add_special_tokens=__lowerCAmelCase       )
          self.assertEqual(len(__lowerCAmelCase       ) ,					1       )

          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
          self.assertTrue(special_token not in decoded       )


    def   lowerCAmelCase_	(       self						:	List[str]       ):
      _UpperCAmelCase    			=   self.get_tokenizers()
      for tokenizer in tokenizers:
        with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
          _UpperCAmelCase			,					_UpperCAmelCase    			=   self.get_input_output_texts(__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.tokenize(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.convert_tokens_to_ids(__lowerCAmelCase       )
          _UpperCAmelCase    			=   tokenizer.encode(__lowerCAmelCase ,					add_special_tokens=__lowerCAmelCase       )
          self.assertListEqual(__lowerCAmelCase ,					__lowerCAmelCase       )

          _UpperCAmelCase    			=   tokenizer.convert_ids_to_tokens(__lowerCAmelCase       )
          self.assertNotEqual(len(__lowerCAmelCase       ) ,					0       )
          _UpperCAmelCase    			=   tokenizer.decode(__lowerCAmelCase       )
          self.assertIsInstance(__lowerCAmelCase ,					__lowerCAmelCase       )

          self.assertEqual(text_a.replace(""" """ ,					""""""       ) ,					__lowerCAmelCase       )


    @unittest.skip("""MGP-STR tokenizer only handles one sequence."""       )
    def   lowerCAmelCase_	(       self						:	List[str]       ):
      pass


    @unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer"""       )
    def   lowerCAmelCase_	(       self						:	Optional[int]       ):
      pass
