import argparse
import json

from tqdm import tqdm






def  snake_case  (       )						->   Optional[int]:
       _A        =  argparse.ArgumentParser()

       # Required parameters
       parser.add_argument(
           """--src_path"""   ,	type=snake_case__   ,	default="""biencoder-nq-dev.json"""   ,	help="""Path to raw DPR training data"""   ,	)
       parser.add_argument(
           """--evaluation_set"""   ,	type=snake_case__   ,	help="""where to store parsed evaluation_set file"""   ,	)
       parser.add_argument(
           """--gold_data_path"""   ,	type=snake_case__   ,	help="""where to store parsed gold_data_path file"""   ,	)
       _A        =  parser.parse_args()

       with open(args.src_path   ,	"""r""") as src_file, open(args.evaluation_set   ,	"""w""") as eval_file, open(
           args.gold_data_path   ,	"""w""") as gold_file:
              _A        =  json.load(snake_case__)
              for dpr_record in tqdm(snake_case__):
                     _A        =  dpr_record["""question"""]
                     _A        =  [context["""title"""] for context in dpr_record["""positive_ctxs"""]]
                     eval_file.write(question + """\n""")
                     gold_file.write("""\t""".join(snake_case__) + """\n""")


if __name__ == "__main__":
    main()





import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
    from huggingface_hub import snapshot_download
    from pyctcdecode import BeamSearchDecoderCTC

    from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
    from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
    from transformers import WavaVecaForCTC



@require_pyctcdecode
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
        _A        =  dict(zip(lowerCAmelCase_		,				range(len(lowerCAmelCase_							)							)							)							)

        _A        =  {
            """unk_token""": """<unk>""",
            """bos_token""": """<s>""",
            """eos_token""": """</s>""",
        }
        _A        =  {
            """feature_size""": 1,
            """padding_value""": 0.0,
            """sampling_rate""": 1_60_00,
            """return_attention_mask""": False,
            """do_normalize""": True,
        }

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

        with open(self.feature_extraction_file		,				"""w"""		,				encoding="""utf-8"""							) as fp:
               fp.write(json.dumps(lowerCAmelCase_							) + """\n"""							)

        # load decoder from hub
        _A        =  """hf-internal-testing/ngram-beam-search-decoder"""



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Tuple:
        _A        =  self.add_kwargs_tokens_map.copy()
        kwargs.update(lowerCAmelCase_							)
        return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				**lowerCAmelCase_							)  ->					Any:
        return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name		,				**lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        shutil.rmtree(self.tmpdirname							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.get_tokenizer()
        _A        =  self.get_feature_extractor()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        processor.save_pretrained(self.tmpdirname							)
        _A        =  WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname							)

        # tokenizer
        self.assertEqual(processor.tokenizer.get_vocab()		,				tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer		,				lowerCAmelCase_							)

        # feature extractor
        self.assertEqual(processor.feature_extractor.to_json_string()		,				feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor		,				lowerCAmelCase_							)

        # decoder
        self.assertEqual(processor.decoder._alphabet.labels		,				decoder._alphabet.labels							)
        self.assertEqual(
            processor.decoder.model_container[decoder._model_key]._unigram_set		,				decoder.model_container[decoder._model_key]._unigram_set		,				)
        self.assertIsInstance(processor.decoder		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  WavaVecaProcessorWithLM(
            tokenizer=self.get_tokenizer()		,				feature_extractor=self.get_feature_extractor()		,				decoder=self.get_decoder()							)
        processor.save_pretrained(self.tmpdirname							)

        # make sure that error is thrown when decoder alphabet doesn't match
        _A        =  WavaVecaProcessorWithLM.from_pretrained(
            self.tmpdirname		,				alpha=5.0		,				beta=3.0		,				score_boundary=-7.0		,				unk_score_offset=3							)

        # decoder
        self.assertEqual(processor.language_model.alpha		,				5.0							)
        self.assertEqual(processor.language_model.beta		,				3.0							)
        self.assertEqual(processor.language_model.score_boundary		,				-7.0							)
        self.assertEqual(processor.language_model.unk_score_offset		,				3							)



 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _A        =  self.get_tokenizer()
        # add token to trigger raise
        tokenizer.add_tokens(["""xx"""]							)
        with self.assertRaisesRegex(lowerCAmelCase_		,				"""include"""							):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCAmelCase_		,				feature_extractor=self.get_feature_extractor()		,				decoder=self.get_decoder()							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  floats_list((3, 10_00)							)

        _A        =  feature_extractor(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor(lowerCAmelCase_		,				return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()		,				input_processor[key].sum()		,				delta=1E-2							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  """This is a test string"""

        _A        =  processor(text=lowerCAmelCase_							)

        _A        =  tokenizer(lowerCAmelCase_							)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]		,				encoded_processor[key]							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_=(2, 10, 16)		,				lowerCAmelCase_=77							)  ->					Tuple:
        np.random.seed(lowerCAmelCase_							)
        return np.random.rand(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Tuple:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits(shape=(10, 16)		,				seed=13							)

        _A        =  processor.decode(lowerCAmelCase_							)

        _A        =  decoder.decode_beams(lowerCAmelCase_							)[0]

        self.assertEqual(decoded_decoder[0]		,				decoded_processor.text							)
        self.assertEqual("""</s> <s> </s>"""		,				decoded_processor.text							)
        self.assertEqual(decoded_decoder[-2]		,				decoded_processor.logit_score							)
        self.assertEqual(decoded_decoder[-1]		,				decoded_processor.lm_score							)



 @parameterized.expand([[None], ["""fork"""], ["""spawn"""]]							)
 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Optional[int]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits()

        # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
        #       otherwise, the LM won't be available to the pool's sub-processes.
        # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
        if pool_context is None:
               _A        =  processor.batch_decode(lowerCAmelCase_							)
        else:
               with get_context(lowerCAmelCase_							).Pool() as pool:
                      _A        =  processor.batch_decode(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A        =  list(lowerCAmelCase_							)

        with get_context("""fork"""							).Pool() as p:
               _A        =  decoder.decode_beams_batch(lowerCAmelCase_		,				lowerCAmelCase_							)

        _A   ,  _A   ,  _A        =  [], [], []
        for beams in decoded_beams:
               texts_decoder.append(beams[0][0]							)
               logit_scores_decoder.append(beams[0][-2]							)
               lm_scores_decoder.append(beams[0][-1]							)

        self.assertListEqual(lowerCAmelCase_		,				decoded_processor.text							)
        self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""]		,				decoded_processor.text							)
        self.assertListEqual(lowerCAmelCase_		,				decoded_processor.logit_score							)
        self.assertListEqual(lowerCAmelCase_		,				decoded_processor.lm_score							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits()

        _A        =  15
        _A        =  -20.0
        _A        =  -4.0

        _A        =  processor.batch_decode(
            lowerCAmelCase_		,				beam_width=lowerCAmelCase_		,				beam_prune_logp=lowerCAmelCase_		,				token_min_logp=lowerCAmelCase_		,				)
        _A        =  decoded_processor_out.text

        _A        =  list(lowerCAmelCase_							)

        with get_context("""fork"""							).Pool() as pool:
               _A        =  decoder.decode_beams_batch(
                   lowerCAmelCase_		,				lowerCAmelCase_		,				beam_width=lowerCAmelCase_		,				beam_prune_logp=lowerCAmelCase_		,				token_min_logp=lowerCAmelCase_		,				)

        _A        =  [d[0][0] for d in decoded_decoder_out]
        _A        =  [d[0][2] for d in decoded_decoder_out]
        _A        =  [d[0][3] for d in decoded_decoder_out]

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)
        self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""]		,				lowerCAmelCase_							)

        self.assertTrue(np.array_equal(lowerCAmelCase_		,				decoded_processor_out.logit_score							)							)
        self.assertTrue(np.allclose([-20.054, -18.447]		,				lowerCAmelCase_		,				atol=1E-3							)							)

        self.assertTrue(np.array_equal(lowerCAmelCase_		,				decoded_processor_out.lm_score							)							)
        self.assertTrue(np.allclose([-15.554, -13.9474]		,				lowerCAmelCase_		,				atol=1E-3							)							)



 def      UpperCAmelCase							(					self							)  ->					List[Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        _A        =  self._get_dummy_logits()

        _A        =  2.0
        _A        =  5.0
        _A        =  -20.0
        _A        =  True

        _A        =  processor.batch_decode(
            lowerCAmelCase_		,				alpha=lowerCAmelCase_		,				beta=lowerCAmelCase_		,				unk_score_offset=lowerCAmelCase_		,				lm_score_boundary=lowerCAmelCase_		,				)
        _A        =  decoded_processor_out.text

        _A        =  list(lowerCAmelCase_							)
        decoder.reset_params(
            alpha=lowerCAmelCase_		,				beta=lowerCAmelCase_		,				unk_score_offset=lowerCAmelCase_		,				lm_score_boundary=lowerCAmelCase_		,				)

        with get_context("""fork"""							).Pool() as pool:
               _A        =  decoder.decode_beams_batch(
                   lowerCAmelCase_		,				lowerCAmelCase_		,				)

        _A        =  [d[0][0] for d in decoded_decoder_out]

        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)
        self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]		,				lowerCAmelCase_							)
        _A        =  processor.decoder.model_container[processor.decoder._model_key]
        self.assertEqual(lm_model.alpha		,				2.0							)
        self.assertEqual(lm_model.beta		,				5.0							)
        self.assertEqual(lm_model.unk_score_offset		,				-20.0							)
        self.assertEqual(lm_model.score_boundary		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

        _A        =  processor.decoder.model_container[processor.decoder._model_key]
        _A        =  Path(language_model._kenlm_model.path.decode("""utf-8"""							)							).parent.parent.absolute()

        _A        =  os.listdir(lowerCAmelCase_							)
        _A        =  ["""alphabet.json""", """language_model"""]

        downloaded_decoder_files.sort()
        expected_decoder_files.sort()

        # test that only decoder relevant files from
        # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
        # are downloaded and none of the rest (e.g. README.md, ...)
        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  snapshot_download("""hf-internal-testing/processor_with_lm"""							)

        _A        =  WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase_							)

        _A        =  processor.decoder.model_container[processor.decoder._model_key]
        _A        =  Path(language_model._kenlm_model.path.decode("""utf-8"""							)							).parent.parent.absolute()

        _A        =  os.listdir(lowerCAmelCase_							)
        _A        =  os.listdir(lowerCAmelCase_							)

        local_decoder_files.sort()
        expected_decoder_files.sort()

        # test that both decoder form hub and local files in cache are the same
        self.assertListEqual(lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					List[str]:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

        _A        =  floats_list((3, 10_00)							)

        _A        =  processor_wavaveca(lowerCAmelCase_		,				return_tensors="""np"""							)
        _A        =  processor_auto(lowerCAmelCase_		,				return_tensors="""np"""							)

        for key in input_wavaveca.keys():
               self.assertAlmostEqual(input_wavaveca[key].sum()		,				input_auto[key].sum()		,				delta=1E-2							)

        _A        =  self._get_dummy_logits()

        _A        =  processor_wavaveca.batch_decode(lowerCAmelCase_							)
        _A        =  processor_auto.batch_decode(lowerCAmelCase_							)

        self.assertListEqual(decoded_wavaveca.text		,				decoded_auto.text							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.get_feature_extractor()
        _A        =  self.get_tokenizer()
        _A        =  self.get_decoder()

        _A        =  WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_		,				feature_extractor=lowerCAmelCase_		,				decoder=lowerCAmelCase_							)

        self.assertListEqual(
            processor.model_input_names		,				feature_extractor.model_input_names		,				msg="""`processor` and `feature_extractor` model input names do not match"""		,				)



 @staticmethod
 def      UpperCAmelCase							(					lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  [d[key] for d in offsets]
        return retrieved_list



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  self._get_dummy_logits()[0]

        _A        =  processor.decode(lowerCAmelCase_		,				output_word_offsets=lowerCAmelCase_							)
        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()							)		,				4							)
        self.assertTrue("""text""" in outputs							)
        self.assertTrue("""word_offsets""" in outputs							)
        self.assertTrue(isinstance(lowerCAmelCase_		,				lowerCAmelCase_							)							)

        self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""]		,				"""word"""							)							)		,				outputs.text							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,				"""word"""							)		,				["""<s>""", """<s>""", """</s>"""]							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,				"""start_offset"""							)		,				[0, 2, 4]							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""]		,				"""end_offset"""							)		,				[1, 3, 5]							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A        =  WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
        _A        =  self._get_dummy_logits()

        _A        =  processor.batch_decode(lowerCAmelCase_		,				output_word_offsets=lowerCAmelCase_							)

        # check Wav2Vec2CTCTokenizerOutput keys for word
        self.assertEqual(len(outputs.keys()							)		,				4							)
        self.assertTrue("""text""" in outputs							)
        self.assertTrue("""word_offsets""" in outputs							)
        self.assertTrue(isinstance(lowerCAmelCase_		,				lowerCAmelCase_							)							)

        self.assertListEqual(
            [""" """.join(self.get_from_offsets(lowerCAmelCase_		,				"""word"""							)							) for o in outputs["""word_offsets"""]]		,				outputs.text							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,				"""word"""							)		,				["""<s>""", """<s>""", """</s>"""]							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,				"""start_offset"""							)		,				[0, 2, 4]							)
        self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0]		,				"""end_offset"""							)		,				[1, 3, 5]							)



 @slow
 @require_torch
 @require_torchaudio
 def      UpperCAmelCase							(					self							)  ->					Any:
        import torch

        _A        =  load_dataset("""common_voice"""		,				"""en"""		,				split="""train"""		,				streaming=lowerCAmelCase_							)
        _A        =  ds.cast_column("""audio"""		,				datasets.Audio(sampling_rate=1_60_00							)							)
        _A        =  iter(lowerCAmelCase_							)
        _A        =  next(lowerCAmelCase_							)

        _A        =  AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""							)
        _A        =  WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""							)

        # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
        _A        =  processor(sample["""audio"""]["""array"""]		,				return_tensors="""pt"""							).input_values

        with torch.no_grad():
               _A        =  model(lowerCAmelCase_							).logits.cpu().numpy()

        _A        =  processor.decode(logits[0]		,				output_word_offsets=lowerCAmelCase_							)

        _A        =  model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
        _A        =  [
            {
                """start_time""": d["""start_offset"""] * time_offset,
                """end_time""": d["""end_offset"""] * time_offset,
                """word""": d["""word"""],
            }
            for d in output["""word_offsets"""]
        ]

        _A        =  """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""

        # output words
        self.assertEqual(""" """.join(self.get_from_offsets(lowerCAmelCase_		,				"""word"""							)							)		,				lowerCAmelCase_							)
        self.assertEqual(""" """.join(self.get_from_offsets(lowerCAmelCase_		,				"""word"""							)							)		,				output.text							)

        # output times
        _A        =  torch.tensor(self.get_from_offsets(lowerCAmelCase_		,				"""start_time"""							)							)
        _A        =  torch.tensor(self.get_from_offsets(lowerCAmelCase_		,				"""end_time"""							)							)

        # fmt: off
        _A        =  torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]							)
        _A        =  torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]							)
        # fmt: on

        self.assertTrue(torch.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=0.01							)							)
        self.assertTrue(torch.allclose(lowerCAmelCase_		,				lowerCAmelCase_		,				atol=0.01							)							)




