
"""simple docstring"""






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       ):



     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	="""| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
      UpperCamelCase_     :					Union[str, Any]   	=dict(zip(lowercase__      ,  range(len(lowercase__   )   )   )   )

      UpperCamelCase_     :					List[Any]   	={
          """unk_token""": """<unk>""",
          """bos_token""": """<s>""",
          """eos_token""": """</s>""",
      }
      UpperCamelCase_     :					Any   	={
          """feature_size""": 1,
          """padding_value""": 0.0,
          """sampling_rate""": 16_000,
          """return_attention_mask""": False,
          """do_normalize""": True,
      }

      UpperCamelCase_     :					Optional[int]   	=tempfile.mkdtemp()
      UpperCamelCase_     :					Dict   	=os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['vocab_file']   )
      UpperCamelCase_     :					List[str]   	=os.path.join(self.tmpdirname      ,  lowercase__   )
      with open(self.vocab_file      ,  'w'      ,  encoding='utf-8'   ) as fp:
       fp.write(json.dumps(lowercase__   ) + '\n'   )

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

      # load decoder from hub
      UpperCamelCase_     :					Optional[Any]   	="""hf-internal-testing/ngram-beam-search-decoder"""



     def  lowerCamelCase_						(							self :Dict      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=self.add_kwargs_tokens_map.copy()
      kwargs.update(lowercase__   )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname      ,  **lowercase__   )



     def  lowerCamelCase_						(							self :int      ,  **_lowerCamelCase :Optional[Any]   ):







      '''simple docstring'''



      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname      ,  **lowercase__   )



     def  lowerCamelCase_						(							self :Any      ,  **_lowerCamelCase :List[str]   ):







      '''simple docstring'''



      return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name      ,  **lowercase__   )



     def  lowerCamelCase_						(							self :List[str]   ):







      '''simple docstring'''



      shutil.rmtree(self.tmpdirname   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					List[Any]   	=self.get_tokenizer()
      UpperCamelCase_     :					Optional[Any]   	=self.get_feature_extractor()
      UpperCamelCase_     :					Tuple   	=self.get_decoder()

      UpperCamelCase_     :					Tuple   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      processor.save_pretrained(self.tmpdirname   )
      UpperCamelCase_     :					Dict   	=WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname   )

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

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

      # 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      ,  lowercase__   )



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=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
      UpperCamelCase_     :					Dict   	=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  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=self.get_tokenizer()
      # add token to trigger raise
      tokenizer.add_tokens(['xx']   )
      with self.assertRaisesRegex(lowercase__      ,  'include'   ):
       WavaVecaProcessorWithLM(
           tokenizer=lowercase__      ,  feature_extractor=self.get_feature_extractor()      ,  decoder=self.get_decoder()   )



     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_feature_extractor()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_tokenizer()
      UpperCamelCase_     :					str   	=self.get_decoder()

      UpperCamelCase_     :					Optional[Any]   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					Any   	=floats_list((3, 1_000)   )

      UpperCamelCase_     :					Optional[Any]   	=feature_extractor(lowercase__      ,  return_tensors='np'   )
      UpperCamelCase_     :					List[Any]   	=processor(lowercase__      ,  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  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.get_feature_extractor()
      UpperCamelCase_     :					Optional[int]   	=self.get_tokenizer()
      UpperCamelCase_     :					List[str]   	=self.get_decoder()

      UpperCamelCase_     :					Optional[Any]   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					List[str]   	="""This is a test string"""

      UpperCamelCase_     :					Optional[Any]   	=processor(text=lowercase__   )

      UpperCamelCase_     :					Dict   	=tokenizer(lowercase__   )

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



     def  lowerCamelCase_						(							self :Tuple      ,  _lowerCamelCase :int=(2, 10, 16)      ,  _lowerCamelCase :str=77   ):







      '''simple docstring'''



      np.random.seed(lowercase__   )
      return np.random.rand(*lowercase__   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[int]   	=self.get_feature_extractor()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_tokenizer()
      UpperCamelCase_     :					Tuple   	=self.get_decoder()

      UpperCamelCase_     :					str   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					Optional[Any]   	=self._get_dummy_logits(shape=(10, 16)      ,  seed=13   )

      UpperCamelCase_     :					Union[str, Any]   	=processor.decode(lowercase__   )

      UpperCamelCase_     :					Optional[int]   	=decoder.decode_beams(lowercase__   )[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  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Union[str, Any]   	=self.get_feature_extractor()
      UpperCamelCase_     :					List[str]   	=self.get_tokenizer()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_decoder()

      UpperCamelCase_     :					Any   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					Union[str, Any]   	=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:
       UpperCamelCase_     :					Union[str, Any]   	=processor.batch_decode(lowercase__   )
      else:
       with get_context(lowercase__   ).Pool() as pool:
        UpperCamelCase_     :					Optional[int]   	=processor.batch_decode(lowercase__      ,  lowercase__   )

      UpperCamelCase_     :					Any   	=list(lowercase__   )

      with get_context('fork'   ).Pool() as p:
       UpperCamelCase_     :					List[Any]   	=decoder.decode_beams_batch(lowercase__      ,  lowercase__   )

      UpperCamelCase_     :					Dict   	=[], [], []
      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(lowercase__      ,  decoded_processor.text   )
      self.assertListEqual(['<s> <s> </s>', '<s> <s> <s>']      ,  decoded_processor.text   )
      self.assertListEqual(lowercase__      ,  decoded_processor.logit_score   )
      self.assertListEqual(lowercase__      ,  decoded_processor.lm_score   )



     def  lowerCamelCase_						(							self :str   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=self.get_feature_extractor()
      UpperCamelCase_     :					int   	=self.get_tokenizer()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_decoder()

      UpperCamelCase_     :					Union[str, Any]   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					Any   	=self._get_dummy_logits()

      UpperCamelCase_     :					Any   	=15
      UpperCamelCase_     :					List[str]   	=-20.0
      UpperCamelCase_     :					Tuple   	=-4.0

      UpperCamelCase_     :					Dict   	=processor.batch_decode(
          lowercase__      ,  beam_width=lowercase__      ,  beam_prune_logp=lowercase__      ,  token_min_logp=lowercase__      ,  )
      UpperCamelCase_     :					List[Any]   	=decoded_processor_out.text

      UpperCamelCase_     :					int   	=list(lowercase__   )

      with get_context('fork'   ).Pool() as pool:
       UpperCamelCase_     :					Optional[int]   	=decoder.decode_beams_batch(
           lowercase__      ,  lowercase__      ,  beam_width=lowercase__      ,  beam_prune_logp=lowercase__      ,  token_min_logp=lowercase__      ,  )

      UpperCamelCase_     :					Any   	=[d[0][0] for d in decoded_decoder_out]
      UpperCamelCase_     :					Dict   	=[d[0][2] for d in decoded_decoder_out]
      UpperCamelCase_     :					Optional[int]   	=[d[0][3] for d in decoded_decoder_out]

      self.assertListEqual(lowercase__      ,  lowercase__   )
      self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']      ,  lowercase__   )

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

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



     def  lowerCamelCase_						(							self :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=self.get_feature_extractor()
      UpperCamelCase_     :					Any   	=self.get_tokenizer()
      UpperCamelCase_     :					List[Any]   	=self.get_decoder()

      UpperCamelCase_     :					List[str]   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

      UpperCamelCase_     :					List[Any]   	=self._get_dummy_logits()

      UpperCamelCase_     :					List[Any]   	=2.0
      UpperCamelCase_     :					Optional[int]   	=5.0
      UpperCamelCase_     :					Union[str, Any]   	=-20.0
      UpperCamelCase_     :					Optional[int]   	=True

      UpperCamelCase_     :					Any   	=processor.batch_decode(
          lowercase__      ,  alpha=lowercase__      ,  beta=lowercase__      ,  unk_score_offset=lowercase__      ,  lm_score_boundary=lowercase__      ,  )
      UpperCamelCase_     :					str   	=decoded_processor_out.text

      UpperCamelCase_     :					Any   	=list(lowercase__   )
      decoder.reset_params(
          alpha=lowercase__      ,  beta=lowercase__      ,  unk_score_offset=lowercase__      ,  lm_score_boundary=lowercase__      ,  )

      with get_context('fork'   ).Pool() as pool:
       UpperCamelCase_     :					Optional[int]   	=decoder.decode_beams_batch(
           lowercase__      ,  lowercase__      ,  )

      UpperCamelCase_     :					Union[str, Any]   	=[d[0][0] for d in decoded_decoder_out]

      self.assertListEqual(lowercase__      ,  lowercase__   )
      self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']      ,  lowercase__   )
      UpperCamelCase_     :					Any   	=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      ,  lowercase__   )



     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					Tuple   	=WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'   )

      UpperCamelCase_     :					Union[str, Any]   	=processor.decoder.model_container[processor.decoder._model_key]
      UpperCamelCase_     :					str   	=Path(language_model._kenlm_model.path.decode('utf-8'   )   ).parent.parent.absolute()

      UpperCamelCase_     :					Dict   	=os.listdir(lowercase__   )
      UpperCamelCase_     :					Optional[int]   	=["""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(lowercase__      ,  lowercase__   )



     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=snapshot_download('hf-internal-testing/processor_with_lm'   )

      UpperCamelCase_     :					Optional[Any]   	=WavaVecaProcessorWithLM.from_pretrained(lowercase__   )

      UpperCamelCase_     :					List[str]   	=processor.decoder.model_container[processor.decoder._model_key]
      UpperCamelCase_     :					Optional[Any]   	=Path(language_model._kenlm_model.path.decode('utf-8'   )   ).parent.parent.absolute()

      UpperCamelCase_     :					Optional[int]   	=os.listdir(lowercase__   )
      UpperCamelCase_     :					int   	=os.listdir(lowercase__   )

      local_decoder_files.sort()
      expected_decoder_files.sort()

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



     def  lowerCamelCase_						(							self :Any   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'   )
      UpperCamelCase_     :					Union[str, Any]   	=AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'   )

      UpperCamelCase_     :					List[str]   	=floats_list((3, 1_000)   )

      UpperCamelCase_     :					Tuple   	=processor_wavaveca(lowercase__      ,  return_tensors='np'   )
      UpperCamelCase_     :					Optional[Any]   	=processor_auto(lowercase__      ,  return_tensors='np'   )

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

      UpperCamelCase_     :					int   	=self._get_dummy_logits()

      UpperCamelCase_     :					Optional[Any]   	=processor_wavaveca.batch_decode(lowercase__   )
      UpperCamelCase_     :					Tuple   	=processor_auto.batch_decode(lowercase__   )

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



     def  lowerCamelCase_						(							self :int   ):







      '''simple docstring'''



      UpperCamelCase_     :					int   	=self.get_feature_extractor()
      UpperCamelCase_     :					Optional[int]   	=self.get_tokenizer()
      UpperCamelCase_     :					Union[str, Any]   	=self.get_decoder()

      UpperCamelCase_     :					Optional[Any]   	=WavaVecaProcessorWithLM(tokenizer=lowercase__      ,  feature_extractor=lowercase__      ,  decoder=lowercase__   )

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



     @staticmethod
     def  lowerCamelCase_						(							_lowerCamelCase :Optional[Any]      ,  _lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=[d[key] for d in offsets]
      return retrieved_list



     def  lowerCamelCase_						(							self :Optional[int]   ):







      '''simple docstring'''



      UpperCamelCase_     :					Optional[Any]   	=WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'   )
      UpperCamelCase_     :					List[Any]   	=self._get_dummy_logits()[0]

      UpperCamelCase_     :					Optional[Any]   	=processor.decode(lowercase__      ,  output_word_offsets=lowercase__   )
      # 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(lowercase__      ,  lowercase__   )   )

      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  lowerCamelCase_						(							self :List[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'   )
      UpperCamelCase_     :					Optional[Any]   	=self._get_dummy_logits()

      UpperCamelCase_     :					Optional[Any]   	=processor.batch_decode(lowercase__      ,  output_word_offsets=lowercase__   )

      # 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(lowercase__      ,  lowercase__   )   )

      self.assertListEqual(
          [' '.join(self.get_from_offsets(lowercase__      ,  '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  lowerCamelCase_						(							self :Union[str, Any]   ):







      '''simple docstring'''



      import torch

      UpperCamelCase_     :					List[str]   	=load_dataset('common_voice'      ,  'en'      ,  split='train'      ,  streaming=lowercase__   )
      UpperCamelCase_     :					List[str]   	=ds.cast_column('audio'      ,  datasets.Audio(sampling_rate=16_000   )   )
      UpperCamelCase_     :					List[Any]   	=iter(lowercase__   )
      UpperCamelCase_     :					Any   	=next(lowercase__   )

      UpperCamelCase_     :					str   	=AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'   )
      UpperCamelCase_     :					List[Any]   	=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
      UpperCamelCase_     :					int   	=processor(sample['audio']['array']      ,  return_tensors='pt'   ).input_values

      with torch.no_grad():
       UpperCamelCase_     :					List[Any]   	=model(lowercase__   ).logits.cpu().numpy()

      UpperCamelCase_     :					Optional[Any]   	=processor.decode(logits[0]      ,  output_word_offsets=lowercase__   )

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

      UpperCamelCase_     :					Union[str, Any]   	="""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(lowercase__      ,  'word'   )   )      ,  lowercase__   )
      self.assertEqual(' '.join(self.get_from_offsets(lowercase__      ,  'word'   )   )      ,  output.text   )

      # output times
      UpperCamelCase_     :					List[Any]   	=torch.tensor(self.get_from_offsets(lowercase__      ,  'start_time'   )   )
      UpperCamelCase_     :					Any   	=torch.tensor(self.get_from_offsets(lowercase__      ,  'end_time'   )   )

      # fmt: off
      UpperCamelCase_     :					Any   	=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]   )
      UpperCamelCase_     :					Optional[int]   	=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(lowercase__      ,  lowercase__      ,  atol=0.01   )   )
      self.assertTrue(torch.allclose(lowercase__      ,  lowercase__      ,  atol=0.01   )   )






'''simple docstring'''


import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union

import numpy as np
import pyarrow as pa

import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datasets.filesystems import (
    is_remote_filesystem,
    rename,
)
from datasets.iterable_dataset import _BaseExamplesIterable
from datasets.utils.py_utils import convert_file_size_to_int


lowerCAmelCase_      :  Any				     =  datasets.utils.logging.get_logger(__name__)

if TYPE_CHECKING:
 import pyspark







@dataclass
class      SCREAMING_SNAKE_CASE    (						datasets.BuilderConfig				):
      __magic_name__  :       Optional[datasets.Features]   							=					None



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					"pyspark.sql.DataFrame" ,    UpperCamelCase__    :					List[int] ,    ):




     """simple docstring"""


     import pyspark

     def generate_fn():
          a_     :							Any     						=  df.select("""*""" ,    pyspark.sql.functions.spark_partition_id().alias("""part_id"""				)				)
          for partition_id in partition_order:
               a_     :							int     						=  df_with_partition_id.select("""*"""				).where(F"part_id = {partition_id}"				).drop("""part_id"""				)
               a_     :							str     						=  partition_df.collect()
               a_     :							List[str]     						=  0
               for row in rows:
                    yield F"{partition_id}_{row_id}", row.asDict()
                    row_id += 1

     return generate_fn







class      SCREAMING_SNAKE_CASE    (						_BaseExamplesIterable				):






      def __init__( self   :  Dict						,						lowercase__   :  "pyspark.sql.DataFrame"						,						lowercase__   :  Optional[int]=None						,						):


           '''simple docstring'''

           a_     :							Tuple     						=  df
           a_     :							Dict     						=  partition_order or range(self.df.rdd.getNumPartitions()       )
           a_     :							Dict     						=  _generate_iterable_examples(self.df						,						self.partition_order       )






      def __iter__( self   :  Optional[Any]       ):


           '''simple docstring'''

           yield from self.generate_examples_fn()






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  np.random.Generator       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  list(range(self.df.rdd.getNumPartitions()       )       )
           generator.shuffle(lowercase__       )
           return SparkExamplesIterable(self.df						,						partition_order=lowercase__       )






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.split_shard_indices_by_worker(lowercase__						,						lowercase__       )
           return SparkExamplesIterable(self.df						,						partition_order=lowercase__       )






      @property
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           return len(self.partition_order       )







class      SCREAMING_SNAKE_CASE    (						datasets.DatasetBuilder				):
      __magic_name__  :       Optional[int]   							=					SparkConfig






      def __init__( self   :  Optional[Any]						,						lowercase__   :  "pyspark.sql.DataFrame"						,						lowercase__   :  str = None						,						lowercase__   :  str = None						,						**lowercase__   :  Dict						,						):


           '''simple docstring'''

           import pyspark

           a_     :							Dict     						=  pyspark.sql.SparkSession.builder.getOrCreate()
           a_     :							List[str]     						=  df
           a_     :							List[str]     						=  working_dir

           super().__init__(
               cache_dir=lowercase__						,						config_name=str(self.df.semanticHash()       )						,						**lowercase__						,						)






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           def create_cache_and_write_probe(lowercase__   :  Any       ):
                # makedirs with exist_ok will recursively create the directory. It will not throw an error if directories
                # already exist.
                os.makedirs(self._cache_dir						,						exist_ok=lowercase__       )
                a_     :							List[Any]     						=  os.path.join(self._cache_dir						,						"""fs_test""" + uuid.uuida().hex       )
                # Opening the file in append mode will create a new file unless it already exists, in which case it will not
                # change the file contents.
                open(lowercase__						,						"""a"""       )
                return [probe_file]

           if self._spark.conf.get("""spark.master"""						,						""""""       ).startswith("""local"""       ):
                return

           # If the cluster is multi-node, make sure that the user provided a cache_dir and that it is on an NFS
           # accessible to the driver.
           # TODO: Stream batches to the driver using ArrowCollectSerializer instead of throwing an error.
           if self._cache_dir:
                a_     :							str     						=  (
                    self._spark.sparkContext.parallelize(range(1       )						,						1       ).mapPartitions(lowercase__       ).collect()
                )
                if os.path.isfile(probe[0]       ):
                     return

           raise ValueError(
               """When using Dataset.from_spark on a multi-node cluster, the driver and all workers should be able to access cache_dir"""       )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           return datasets.DatasetInfo(features=self.config.features       )






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  datasets.download.download_manager.DownloadManager       ):


           '''simple docstring'''

           return [datasets.SplitGenerator(name=datasets.Split.TRAIN       )]






      def 							lowercase_						( self   :  Any						,						lowercase__   :  int       ):


           '''simple docstring'''

           import pyspark

           def get_arrow_batch_size(lowercase__   :  List[Any]       ):
                for batch in it:
                     yield pa.RecordBatch.from_pydict({"""batch_bytes""": [batch.nbytes]}       )

           a_     :							Union[str, Any]     						=  self.df.count()
           a_     :							List[str]     						=  df_num_rows if df_num_rows <= 100 else 100
           # Approximate the size of each row (in Arrow format) by averaging over a max-100-row sample.
           a_     :							str     						=  (
               self.df.limit(lowercase__       )
               .repartition(1       )
               .mapInArrow(lowercase__						,						"""batch_bytes: long"""       )
               .agg(pyspark.sql.functions.sum("""batch_bytes"""       ).alias("""sample_bytes"""       )       )
               .collect()[0]
               .sample_bytes
               / sample_num_rows
           )
           a_     :							List[str]     						=  approx_bytes_per_row * df_num_rows
           if approx_total_size > max_shard_size:
                # Make sure there is at least one row per partition.
                a_     :							List[str]     						=  min(lowercase__						,						int(approx_total_size / max_shard_size       )       )
                a_     :							Union[str, Any]     						=  self.df.repartition(lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  str						,						lowercase__   :  int						,						):


           '''simple docstring'''

           import pyspark

           a_     :							str     						=  ParquetWriter if file_format == """parquet""" else ArrowWriter
           a_     :							int     						=  os.path.join(self._working_dir						,						os.path.basename(lowercase__       )       ) if self._working_dir else fpath
           a_     :							Optional[int]     						=  file_format == """parquet"""

           # Define these so that we don't reference self in write_arrow, which will result in a pickling error due to
           # pickling the SparkContext.
           a_     :							int     						=  self.config.features
           a_     :							List[str]     						=  self._writer_batch_size
           a_     :							str     						=  self._fs.storage_options

           def write_arrow(lowercase__   :  Optional[Any]       ):
                # Within the same SparkContext, no two task attempts will share the same attempt ID.
                a_     :							int     						=  pyspark.TaskContext().taskAttemptId()
                a_     :							List[str]     						=  next(lowercase__						,						lowercase__       )
                if first_batch is None:
                     # Some partitions might not receive any data.
                     return pa.RecordBatch.from_arrays(
                         [[task_id], [0], [0]]						,						names=["""task_id""", """num_examples""", """num_bytes"""]						,						)
                a_     :							List[str]     						=  0
                a_     :							Union[str, Any]     						=  writer_class(
                    features=lowercase__						,						path=working_fpath.replace("""SSSSS"""						,						F"{shard_id:05d}"       ).replace("""TTTTT"""						,						F"{task_id:05d}"       )						,						writer_batch_size=lowercase__						,						storage_options=lowercase__						,						embed_local_files=lowercase__						,						)
                a_     :							List[str]     						=  pa.Table.from_batches([first_batch]       )
                writer.write_table(lowercase__       )
                for batch in it:
                     if max_shard_size is not None and writer._num_bytes >= max_shard_size:
                          a_						,     a_     :							Optional[Any]     						=  writer.finalize()
                          writer.close()
                          yield pa.RecordBatch.from_arrays(
                              [[task_id], [num_examples], [num_bytes]]						,						names=["""task_id""", """num_examples""", """num_bytes"""]						,						)
                          shard_id += 1
                          a_     :							Any     						=  writer_class(
                              features=writer._features						,						path=working_fpath.replace("""SSSSS"""						,						F"{shard_id:05d}"       ).replace("""TTTTT"""						,						F"{task_id:05d}"       )						,						writer_batch_size=lowercase__						,						storage_options=lowercase__						,						embed_local_files=lowercase__						,						)
                     a_     :							str     						=  pa.Table.from_batches([batch]       )
                     writer.write_table(lowercase__       )

                if writer._num_bytes > 0:
                     a_						,     a_     :							Any     						=  writer.finalize()
                     writer.close()
                     yield pa.RecordBatch.from_arrays(
                         [[task_id], [num_examples], [num_bytes]]						,						names=["""task_id""", """num_examples""", """num_bytes"""]						,						)

                if working_fpath != fpath:
                     for file in os.listdir(os.path.dirname(lowercase__       )       ):
                          a_     :							str     						=  os.path.join(os.path.dirname(lowercase__       )						,						os.path.basename(lowercase__       )       )
                          shutil.move(lowercase__						,						lowercase__       )

           a_     :							Any     						=  (
               self.df.mapInArrow(lowercase__						,						"""task_id: long, num_examples: long, num_bytes: long"""       )
               .groupBy("""task_id"""       )
               .agg(
                   pyspark.sql.functions.sum("""num_examples"""       ).alias("""total_num_examples"""       )						,						pyspark.sql.functions.sum("""num_bytes"""       ).alias("""total_num_bytes"""       )						,						pyspark.sql.functions.count("""num_bytes"""       ).alias("""num_shards"""       )						,						pyspark.sql.functions.collect_list("""num_examples"""       ).alias("""shard_lengths"""       )						,						)
               .collect()
           )
           for row in stats:
                yield row.task_id, (row.total_num_examples, row.total_num_bytes, row.num_shards, row.shard_lengths)






      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  "datasets.SplitGenerator"						,						lowercase__   :  str = "arrow"						,						lowercase__   :  Optional[Union[str, int]] = None						,						lowercase__   :  Optional[int] = None						,						**lowercase__   :  Optional[int]						,						):


           '''simple docstring'''

           self._validate_cache_dir()

           a_     :							Dict     						=  convert_file_size_to_int(max_shard_size or MAX_SHARD_SIZE       )
           self._repartition_df_if_needed(lowercase__       )
           a_     :							Any     						=  not is_remote_filesystem(self._fs       )
           a_     :							str     						=  os.path.join if is_local else posixpath.join

           a_     :							Optional[Any]     						=  """-TTTTT-SSSSS-of-NNNNN"""
           a_     :							int     						=  F"{self.name}-{split_generator.name}{SUFFIX}.{file_format}"
           a_     :							Optional[Any]     						=  path_join(self._output_dir						,						lowercase__       )

           a_     :							Dict     						=  0
           a_     :							Optional[Any]     						=  0
           a_     :							Dict     						=  0
           a_     :							List[str]     						=  []
           a_     :							Dict     						=  []

           for task_id, content in self._prepare_split_single(lowercase__						,						lowercase__						,						lowercase__       ):
                (
                    (
                    a_
                )						,     (
                    a_
                )						,     (
                    a_
                )						,     (
                    a_
                )						,     
                )     :							Tuple     						=  content
                if num_bytes > 0:
                     total_num_examples += num_examples
                     total_num_bytes += num_bytes
                     total_shards += num_shards
                     task_id_and_num_shards.append((task_id, num_shards)       )
                     all_shard_lengths.extend(lowercase__       )

           a_     :							List[Any]     						=  total_num_examples
           a_     :							int     						=  total_num_bytes

           # should rename everything at the end
           logger.debug(F"Renaming {total_shards} shards."       )
           if total_shards > 1:
                a_     :							Dict     						=  all_shard_lengths

                # Define fs outside of _rename_shard so that we don't reference self in the function, which will result in a
                # pickling error due to pickling the SparkContext.
                a_     :							List[str]     						=  self._fs

                # use the -SSSSS-of-NNNNN pattern
                def _rename_shard(
                    lowercase__   :  int						,						lowercase__   :  int						,						lowercase__   :  int						,						):
                     rename(
                         lowercase__						,						fpath.replace("""SSSSS"""						,						F"{shard_id:05d}"       ).replace("""TTTTT"""						,						F"{task_id:05d}"       )						,						fpath.replace("""TTTTT-SSSSS"""						,						F"{global_shard_id:05d}"       ).replace("""NNNNN"""						,						F"{total_shards:05d}"       )						,						)

                a_     :							int     						=  []
                a_     :							List[str]     						=  0
                for i in range(len(lowercase__       )       ):
                     a_						,     a_     :							List[str]     						=  task_id_and_num_shards[i]
                     for shard_id in range(lowercase__       ):
                          args.append([task_id, shard_id, global_shard_id]       )
                          global_shard_id += 1
                self._spark.sparkContext.parallelize(lowercase__						,						len(lowercase__       )       ).map(lambda lowercase__       : _rename_shard(*lowercase__       )       ).collect()
           else:
                # don't use any pattern
                a_     :							int     						=  0
                a_     :							Any     						=  task_id_and_num_shards[0][0]
                self._rename(
                    fpath.replace("""SSSSS"""						,						F"{shard_id:05d}"       ).replace("""TTTTT"""						,						F"{task_id:05d}"       )						,						fpath.replace(lowercase__						,						""""""       )						,						)






      def 							lowercase_						( self   :  Tuple						,						lowercase__   :  "datasets.SplitGenerator"						,						):


           '''simple docstring'''

           return SparkExamplesIterable(self.df       )
