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_       ,					lowerCAmelCase_   )		->						Tuple:




       '''simple docstring'''






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

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

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

       # print & log results
       _A=	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:
              _A=	F"log_{dataset_id}_predictions.txt"
              _A=	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_       ,					lowerCAmelCase_   ):
                            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:




       '''simple docstring'''






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

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

       for t in token_sequences_to_ignore:
              _A=	' '.join(text.split(lowerCAmelCase_   )   )

       return text

def 							UpperCamelCase				(    lowerCAmelCase_   )		->						Optional[Any]:




       '''simple docstring'''






       _A=	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
       _A=	AutoFeatureExtractor.from_pretrained(args.model_id   )
       _A=	feature_extractor.sampling_rate

       # resample audio
       _A=	dataset.cast_column('audio'       ,					Audio(sampling_rate=lowerCAmelCase_   )   )

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

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

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

       # run inference on all examples
       _A=	dataset.map(lowerCAmelCase_       ,					remove_columns=dataset.column_names   )

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


if __name__ == "__main__":
      UpperCAmelCase_								=							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.''',
      )
      UpperCAmelCase_								=							parser.parse_args()

      main(args)
import unittest

from transformers import AutoConfig, AutoTokenizer, BertConfig, TensorType, is_flax_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, slow


if is_flax_available():
      import jax

      from transformers.models.auto.modeling_flax_auto import FlaxAutoModel
      from transformers.models.bert.modeling_flax_bert import FlaxBertModel
      from transformers.models.roberta.modeling_flax_roberta import FlaxRobertaModel





@require_flax
class      lowerCAmelCase						(					unittest.TestCase			):



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   with self.subTest(lowerCAmelCase__       ):
                          _A=	AutoConfig.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )

                          _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__       )
                          self.assertIsNotNone(lowerCAmelCase__       )
                          self.assertIsInstance(lowerCAmelCase__				,      lowerCAmelCase__       )



     @slow
     def 			a__  (					self       ):
            for model_name in ["bert-base-cased", "bert-large-uncased"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxBertModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     @slow
     def 			a__  (					self       ):
            for model_name in ["roberta-base", "roberta-large"]:
                   _A=	AutoTokenizer.from_pretrained(lowerCAmelCase__       )
                   _A=	FlaxRobertaModel.from_pretrained(lowerCAmelCase__       )
                   _A=	tokenizer('Do you support jax jitted function?'				,      return_tensors=TensorType.JAX       )

                   @jax.jit
                   def eval(**lowerCAmelCase__       ):
                          return model(**lowerCAmelCase__       )

                   eval(**lowerCAmelCase__       ).block_until_ready()



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'bert-base is not a local folder and is not a valid model identifier'       ):
                   _A=	FlaxAutoModel.from_pretrained('bert-base'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'       ):
                   _A=	FlaxAutoModel.from_pretrained(lowerCAmelCase__				,      revision='aaaaaa'       )



     def 			a__  (					self       ):
            with self.assertRaisesRegex(
                lowerCAmelCase__				,      'hf-internal-testing/config-no-model does not appear to have a file named flax_model.msgpack'				,      ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/config-no-model'       )





     def 			a__  (					self       ):
            with self.assertRaisesRegex(lowerCAmelCase__				,      'Use `from_pt=True` to load this model'       ):
                   _A=	FlaxAutoModel.from_pretrained('hf-internal-testing/tiny-bert-pt-only'       )