
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
_A		     =    pytest.mark.integration


_A		     =    {"""comet"""}
_A		     =    importlib.util.find_spec('''fairseq''') is not None

_A		     =    {"""code_eval"""}
_A		     =    os.name == """nt"""

_A		     =    {"""bertscore""", """frugalscore""", """perplexity"""}
_A		     =    importlib.util.find_spec('''transformers''') is not None




def  lowerCamelCase__ (  a__     :     str   )						->		Dict:

       @wraps(lowercase_   )
       def wrapper(self     :     Tuple		,					a__     :     Optional[Any]   ):
              if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
                     self.skipTest("""\"test requires Fairseq\""""   )
              else:
                     test_case(self		,					lowercase_   )

       return wrapper




def  lowerCamelCase__ (  a__     :     List[Any]   )						->		Optional[Any]:

       @wraps(lowercase_   )
       def wrapper(self     :     Any		,					a__     :     int   ):
              if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
                     self.skipTest("""\"test requires transformers\""""   )
              else:
                     test_case(self		,					lowercase_   )

       return wrapper




def  lowerCamelCase__ (  a__     :     Dict   )						->		Optional[Any]:

       @wraps(lowercase_   )
       def wrapper(self     :     Dict		,					a__     :     Dict   ):
              if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
                     self.skipTest("""\"test not supported on Windows\""""   )
              else:
                     test_case(self		,					lowercase_   )

       return wrapper




def  lowerCamelCase__ (  )						->		Optional[Any]:
       UpperCamelCase_											=				[metric_dir.split(os.sep   )[-2] for metric_dir in glob.glob("""./metrics/*/"""   )]
       return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished

@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
    UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ )
@local
class 						lowercase_				(				parameterized.TestCase ):
 A__  : List[Any]      					=			{}
 A__  : Tuple      					=			None

 @pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning"""   )
 @pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning"""   )
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				"""[...]"""
        UpperCamelCase_											=				importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("""metrics"""   ,		UpperCAmelCase__   )   ).module_path   )
        UpperCamelCase_											=				datasets.load.import_main_class(metric_module.__name__   ,		dataset=UpperCAmelCase__   )
        # check parameters
        UpperCamelCase_											=				inspect.signature(metric._compute   ).parameters
        self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()   )   )  # no **kwargs
        # run doctest
        with self.patch_intensive_calls(UpperCAmelCase__   ,		metric_module.__name__   ):
               with self.use_local_metrics():
                      try:
                             UpperCamelCase_											=				doctest.testmod(UpperCAmelCase__   ,		verbose=UpperCAmelCase__   ,		raise_on_error=UpperCAmelCase__   )
                      except doctest.UnexpectedException as e:
                             raise e.exc_info[1]  # raise the exception that doctest caught
        self.assertEqual(results.failed   ,		0   )
        self.assertGreater(results.attempted   ,		1   )

 @slow
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




        """simple docstring"""


        UpperCamelCase_											=				"""[...]"""
        UpperCamelCase_											=				importlib.import_module(
            datasets.load.metric_module_factory(os.path.join("""metrics"""   ,		UpperCAmelCase__   )   ).module_path   )
        # run doctest
        with self.use_local_metrics():
               UpperCamelCase_											=				doctest.testmod(UpperCAmelCase__   ,		verbose=UpperCAmelCase__   ,		raise_on_error=UpperCAmelCase__   )
        self.assertEqual(results.failed   ,		0   )
        self.assertGreater(results.attempted   ,		1   )

 @contextmanager
 def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		__UpperCamelCase   ):




        """simple docstring"""


        if metric_name in self.INTENSIVE_CALLS_PATCHER:
               with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCAmelCase__   ):
                      yield
        else:
               yield

 @contextmanager
 def lowerCamelCase_ (					self   ):




        """simple docstring"""


        def load_local_metric(__UpperCamelCase   ,		*__UpperCamelCase   ,		**__UpperCamelCase   ):
               return load_metric(os.path.join("""metrics"""   ,		UpperCAmelCase__   )   ,		*UpperCAmelCase__   ,		**UpperCAmelCase__   )

        with patch("""datasets.load_metric"""   ) as mock_load_metric:
               UpperCamelCase_											=				load_local_metric
               yield

 @classmethod
 def lowerCamelCase_ (					cls   ,		__UpperCamelCase   ):




        """simple docstring"""


        def wrapper(__UpperCamelCase   ):
               UpperCamelCase_											=				contextmanager(UpperCAmelCase__   )
               UpperCamelCase_											=				patcher
               return patcher

        return wrapper




@LocalMetricTest.register_intensive_calls_patcher("""bleurt"""   )
def  lowerCamelCase__ (  a__     :     List[Any]   )						->		str:
       import tensorflow.compat.va as tf
       from bleurt.score import Predictor

       tf.flags.DEFINE_string("""sv"""		,					""""""		,					""""""   )  # handle pytest cli flags

       class 						lowercase_				(				UpperCAmelCase__ ):

        def lowerCamelCase_ (					self   ,		__UpperCamelCase   ):




               """simple docstring"""


               assert len(input_dict["""input_ids"""]   ) == 2
               return np.array([1.03, 1.04]   )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
       with patch("""bleurt.score._create_predictor"""   ) as mock_create_predictor:
              UpperCamelCase_											=				MockedPredictor()
              yield




@LocalMetricTest.register_intensive_calls_patcher("""bertscore"""   )
def  lowerCamelCase__ (  a__     :     Dict   )						->		Tuple:
       import torch

       def bert_cos_score_idf(a__     :     List[Any]		,					a__     :     Optional[Any]		,					*a__     :     str		,					**a__     :     Union[str, Any]   ):
              return torch.tensor([[1.0, 1.0, 1.0]] * len(lowercase_   )   )

       # mock get_model which is supposed to do download a bert model
       # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
       with patch("""bert_score.scorer.get_model"""   ), patch(
           """bert_score.scorer.bert_cos_score_idf"""   ) as mock_bert_cos_score_idf:
              UpperCamelCase_											=				bert_cos_score_idf
              yield




@LocalMetricTest.register_intensive_calls_patcher("""comet"""   )
def  lowerCamelCase__ (  a__     :     Optional[int]   )						->		Union[str, Any]:

       def load_from_checkpoint(a__     :     Dict   ):

              class 						lowercase_				:

               def lowerCamelCase_ (					self   ,		__UpperCamelCase   ,		*__UpperCamelCase   ,		**__UpperCamelCase   ):




                      """simple docstring"""


                      assert len(UpperCAmelCase__   ) == 2
                      UpperCamelCase_											=				[0.19, 0.92]
                      return scores, sum(UpperCAmelCase__   ) / len(UpperCAmelCase__   )



              return Model()

       # mock load_from_checkpoint which is supposed to do download a bert model
       # mock load_from_checkpoint which is supposed to do download a bert model
       with patch("""comet.download_model"""   ) as mock_download_model:
              UpperCamelCase_											=				None
              with patch("""comet.load_from_checkpoint"""   ) as mock_load_from_checkpoint:
                     UpperCamelCase_											=				load_from_checkpoint
                     yield







def  lowerCamelCase__ (  )						->		Optional[int]:
       UpperCamelCase_											=				load_metric(os.path.join("""metrics"""		,					"""seqeval"""   )   )
       UpperCamelCase_											=				"""ERROR"""
       UpperCamelCase_											=				f'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
       with pytest.raises(lowercase_		,					match=re.escape(lowercase_   )   ):
              metric.compute(predictions=[]		,					references=[]		,					scheme=lowercase_   )









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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_						)   ->       Optional[Any]:



       """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(lowercase_						)

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

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

                     result.map(lowercase_ ,				with_indices=lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       str:



       """simple docstring"""

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

       A__       =    re.sub(lowercase_ ,				'''''' ,				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(lowercase_						)						)

       return text







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       List[str]:



       """simple docstring"""

       A__       =    load_dataset(args.dataset ,				args.config ,				split=args.split ,				use_auth_token=lowercase_						)

       # 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=lowercase_						)						)

       # 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(lowercase_						):
              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(lowercase_ ,				remove_columns=dataset.column_names						)

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


if __name__ == "__main__":
  _lowerCamelCase	:		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.""",
  )
  _lowerCamelCase	:		str							       =   parser.parse_args()

  main(args)


